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A Framework for the Control Room of the Future: Human-in-the-loop MPC
2nd IFAC Conference on Cyber-Physical & Human-Systems 2nd IFAC on Cyber-Physical & Human-Systems Miami, FL,Conference USA, Dec. 14-15, 2018 2nd IFAC on Cyber-Physical & Human-Systems Miami, FL,Conference USA, Dec. 14-15, 2018 Available online at www.sciencedirect.com 2nd IFAC Conference on Cyber-Physical & Human-Systems 2nd IFAC on Cyber-Physical & Human-Systems Miami, FL,Conference USA, Dec. 14-15, 2018 Miami, FL, USA, Dec. 14-15, 2018 Miami, FL, USA, Dec. 14-15, 2018
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IFAC PapersOnLine 51-34 (2019) 252–257
A Framework for the Control Room of the Future: A Framework for the Control Room of the Future: A Framework for the Control Room the Future: Human-in-the-loop MPCof A for the Control Room Room of the the Future: Future: Human-in-the-loop MPCof A Framework Framework for the Control Human-in-the-loop MPC Human-in-the-loop Sambit Ghosh*, B. WayneMPC Bequette* Human-in-the-loop MPC Sambit Ghosh*, B. Wayne Bequette*
Sambit Ghosh*, B. Wayne Wayne Bequette* Bequette* Sambit Ghosh*, Sambit Ghosh*, B. B.Rensselaer Wayne Bequette* *Dept. Of Chemical and Biological Engineering, Polytechnic Institute, Troy, NY 12180 *Dept. Of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180 USA (Tel:(518) 276-6377; e-mail: [email protected]). *Dept. Of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180 USA (Tel:(518) 276-6377; e-mail: [email protected]). *Dept. Of Chemical and Biological Engineering, Rensselaer Polytechnic *Dept. Of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Institute, Troy, Troy, NY NY 12180 12180 USA (Tel:(518) 276-6377; e-mail: [email protected]). USA (Tel:(518) 276-6377; e-mail: [email protected]). USA (Tel:(518) 276-6377; e-mail: [email protected]). Abstract: A general framework and preliminary results for the Control Room of the Future are presented. Abstract: A general framework and preliminary resultslanguage for the Control Roomfacial of the recognition, Future are presented. The inclusion of latest technology (e.g., natural processing, big-data Abstract: A general framework and preliminary results for the Control Room of the Future are presented. The inclusion of latest technology (e.g., natural language processing, facial recognition, big-data Abstract: A general framework and preliminary results for the Control Room of the Future are presented. analytics) in control rooms, Model Predictive Control (MPC), and integration of human-in-the-loop is Abstract: A general framework and preliminary results for the Control Room of the Future are presented. The inclusion of latest technology (e.g., natural language processing, facial recognition, big-data analytics) in control rooms, Model Predictive Control (MPC), and integration of human-in-the-loop is The inclusion of latest technology (e.g., natural language processing, facial recognition, big-data proposed. The paper focuses on process start-ups and uses a two-tank interacting nonlinear system to The inclusion of latest technology (e.g., natural language processing, facial recognition, big-data analytics) in control rooms, Model Predictive Control and integration of human-in-the-loop is proposed. The paper focuses on process and (MPC), uses of a two-tank interacting nonlinear systemare to analytics) in control rooms, Model Control (MPC), and integration human-in-the-loop is demonstrate the dynamic changes inPredictive the start-ups network topology the Threeof of simulations analytics) in control rooms, Model Predictive Control (MPC), and plant. integration ofsets human-in-the-loop is proposed. The paper focuses on process start-ups and uses a two-tank interacting nonlinear system to demonstrate the dynamic changes in the start-ups network topology of thetoplant. Three A sets of simulations are proposed. The paper focuses on process and uses a two-tank interacting nonlinear system to conducted to highlight the MPC performance under human inputs the system. multiple-model MPC proposed. The paper focuses on process start-ups and uses of a two-tank interacting nonlinear systemare to demonstrate the dynamic changes in the network topology the plant. Three sets of simulations conducted highlight thethe MPC performance under human to the of system. multiple-model MPC demonstrate the dynamic changes in network topology of the Three sets of are approach isto used to solve nonlinear control problem andinputs the using A it in human-in-the-loop demonstrate the dynamic changes in the the network topology of benefits thetoplant. plant. Three sets of simulations simulationsMPC are conducted to highlight the MPC performance under human inputs the system. A multiple-model approach is used to solve the nonlinear control problem and the benefits of using it in human-in-the-loop conducted to highlight the MPC performance under human inputs to the system. A multiple-model MPC systems are discussed. Future work includes using the network formulation as a supervisory fault conducted toused highlight thethe MPC performance under human inputs to the of system. A multiple-model MPC approach is to solve nonlinear control problem and the benefits using it in human-in-the-loop systems discussed. Future work includes using the formulation asina human-in-the-loop supervisory fault approach is used to the nonlinear control problem and the benefits of it detectionare layer, providing suggestions to the personnel vianetwork the Smart Control Room and implementing approach is used to solve solveFuture the nonlinear control problem and benefits of using using it ina human-in-the-loop systems are discussed. work includes using the network formulation as supervisory fault detection layer, providing suggestions to the personnel via the Smart Control Room and implementing systems are discussed. Future work includes using the network formulation as a supervisory fault advancedare models for predicting human decisions for safe process start-ups and transient operations. systems discussed. Future work includes using the network formulation as a and supervisory fault detection layer, providing suggestions to the personnel via the Smart Control Room implementing advanced layer, modelsproviding for predicting human decisions for safe via process start-ups and transient operations. detection suggestions to the personnel the Smart Control Room and implementing detection layer, providing suggestions to the personnel via the Smart Control Room and implementing advanced models for predicting human for safe process start-ups and transient operations. © 2019, IFAC (International Federation ofdecisions Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Keywords: Model Predictive Control, start-up, human-in-the-loop, safety, operators. advanced models for predicting human decisions for and advanced predicting humanstart-up, decisions for safe safe process process start-ups start-ups and transient transient operations. operations. Keywords:models Modelfor Predictive Control, human-in-the-loop, safety, operators. Keywords: Model Predictive Control, start-up, human-in-the-loop, safety, operators. Keywords: safety, operators. Keywords: Model Model Predictive Predictive Control, Control, start-up, start-up, human-in-the-loop, human-in-the-loop, safety, operators.the role of the operator. When that automation eliminates 1. INTRODUCTION that automation eliminates role offrom the aoperator. When operators make aeliminates decision, the it comes very complex 1. INTRODUCTION that automation the role of the operator. When operators make a decision, it comes from a very complex that automation eliminates the role of the operator. When 1. INTRODUCTION interaction of his/her process knowledge, experience, the that automation eliminates the role of the operator. When 1. INTRODUCTION operators make a decision, it comes from a very complex Industry 4.0 1. envisions the future of manufacturing systems as interaction of his/her process knowledge, experience, the INTRODUCTION operators make a decision, it comes from a very complex goals he/she or the process demands (Rasmussen, 1983) and operators make a decision, it comes from a very complex Industry 4.0 envisions thewhere futurecyber-physical of manufacturing systems as interaction of his/her process knowledge, experience, the an integrated framework systems (CPS) goals he/sheof or his/her the process demands (Rasmussen, 1983) interaction process knowledge, the Industry 4.0 envisions the future of manufacturing systems as the human cognition itself. Moreover, theexperience, health of and the interaction of his/her process knowledge, experience, an integrated framework where cyber-physical systems (CPS) Industry 4.0 envisions the future of manufacturing systems as goals he/she or the process demands (Rasmussen, 1983) and play an important role (Lasi et al., 2014). Humans are an human cognition itself. Moreover, the health of the Industry 4.0 envisions thewhere futurecyber-physical of manufacturing systems as the goals he/she or the process demands (Rasmussen, 1983) and an integrated framework systems (CPS) operator, time should be (Rasmussen, noted. It is easy to and see goals he/she oroftheday/year process demands 1983) play an part important rolea complex (Lasi et al., Humans an integrated framework where systems (CPS) the human cognition itself. Moreover, the of the integral of such and 2014). dynamic system,are asan a operator, time of day/year should bewith noted. It health is to easy to see an integrated framework whereetcyber-physical cyber-physical systems (CPS) the human cognition itself. Moreover, the health of the play an important role (Lasi al., 2014). Humans are an that behaviour will change respect a plant’s the human cognition itself. Moreover, the health of the integral part of such a complex and dynamic system, as a play an important role (Lasi et al., 2014). Humans are an operator, time of day/year should be noted. It is easy to see result safety is of paramount importance for the plant, the that human behaviour will change with respect to a plant’s play an part important rolea complex (Lasi et al., 2014). Humans areasana operator, time of day/year should be noted. It is easy to see integral of such and dynamic system, location on the globe due to social cultural differences, operator, time of day/year should be noted. It is easy to see result safety is of paramount importance for the plant, the that human behaviour will change with respect to a plant’s integral part of such a complex and dynamic system, as a operators and of thesuch surrounding environment. A prime example on behaviour the globe duechange to social cultural differences, integral part a complex and dynamic system, asthea location that human will with respect aa plant’s result safety paramount importance for the plant, governmental norms, weather patterns etc. On theto other hand, that human behaviour will change with respect to plant’s operators and is theof surrounding environment. ABP prime example location on the globe due to social cultural differences, result safety is of paramount importance for the plant, the of a systematic safety-related failure is the Texas City governmental norms, weather patterns etc. On the other hand, result safety is of paramount importance for the plant, the location on the globe due to social cultural differences, operators and the surrounding environment. A prime example the problem of loss of situation awareness and skills in location on the globe due to socialetc. cultural differences, of a systematic safety-related failure theABP Texas operators and surrounding prime example governmental norms, patterns On the hand, refinery explosion in 2005,environment. which is occurred due toCity a the problem of loss weather ofcontrol situation awareness andother skills in operators and the thesafety-related surrounding environment. ABP prime example governmental norms, weather patterns etc. On the other hand, of a systematic failure is the Texas City human-out-of-the-loop was discussed by Endsley et governmental norms, weather patterns etc. On the other hand, refinery explosion in 2005, failure which is occurred due and/or toCitya the problem of loss of situation awareness and skills in of aa systematic the combination of safety-related mis-communication, mis-calibrated control was awareness discussed by Endsley et of systematic safety-related failure is the BP BP Texas Texas Citya human-out-of-the-loop the problem of loss of situation and skills in refinery explosion in 2005, which occurred due to al. (1995). They also propose levels of automation explaining the problem of loss of situation awareness and skills in combination of poor mis-communication, mis-calibrated and/or refinery explosion in 2005, which occurred due to human-out-of-the-loop control was discussed by Endsley et failing sensors, control design, (1995). They alsoofpropose levels of explaining refinery explosion in 2005,system whichinterface occurred due failure to aa al. human-out-of-the-loop control was discussed by et combination of mis-communication, mis-calibrated and/or the different kinds roles played byautomation the automation and human-out-of-the-loop control was of discussed by Endsley Endsley et failing sensors, poor control system interface design, failure combination of mis-communication, mis-calibrated and/or al. (1995). They also propose levels automation explaining to follow documented start-up protocols, and outdated relief different kinds of roles played by the automation and combination of poor mis-communication, mis-calibrated and/or the al. (1995). They also propose levels of automation explaining failing sensors, control system interface design, failure operators. al. (1995). They also propose levels of automation explaining to follow documented start-up protocols, and outdated relief the different kinds of roles played by the automation and failing sensors, poor control system interface design, system design (CSB, operators. failing sensors, poor 2007). control system interface design, failure failure the different to follow documented start-up protocols, and outdated relief the different kinds kinds of of roles roles played played by by the the automation automation and and system design (CSB, 2007). operators. to follow documented start-up protocols, and outdated relief to follow documented start-up protocols, and outdated relief operators. system design (CSB, 2007). 1.2 Human-in-the-loop (HiTL) control operators. system design (CSB, 2007). Although industrial system design (CSB, safety 2007). systems can address most faults 1.2 Human-in-the-loop (HiTL) control Although industrial safety systems can address mosthumanfaults 1.2 Human-in-the-loop (HiTL) control and safety breaches, it has not implemented the latest 1.2 Human-in-the-loop (HiTL) Although industrial safety systems can address most faults A of HiTL applications in the Internet-of-things 1.2 review Human-in-the-loop (HiTL) control control and safety breaches, it has not implemented the latest humanAlthough industrial safety systems can address most faults in-the-loop (HiTL) techniques to takecan pre-emptive measures. Although industrial safety systems address most faults A review of HiTLbyapplications in(2015) the Internet-of-things and safety breaches, it has not implemented the latest humanframework is given Nunes et al. who discuss the in-the-loop (HiTL) techniques to take pre-emptive measures. and safety breaches, it has not implemented the latest humanA review of HiTL applications in the Internet-of-things On the other hand, robotics, autonomous vehicle design and and safety breaches, it has not implemented the latest humanframework is given by Nunes et al. (2015) who discuss the A review of HiTL applications in the Internet-of-things in-the-loop (HiTL) techniques to take pre-emptive measures. role of data acquisition from HiTL systems to infer the state review of HiTLbyapplications in(2015) the Internet-of-things On the other(HiTL) hand, robotics, autonomous vehicle design and A in-the-loop techniques to take pre-emptive measures. framework is given Nunes et al. who discuss the aeronautics have implemented methods that allow the human in-the-loop (HiTL) techniques to take pre-emptive measures. role of data acquisition from HiTL systems to infer the state framework is given by Nunes et al. (2015) who discuss the On the other hand, robotics, autonomous vehicle design and of theofhuman and thebyprediction of their future decisions for framework is given Nunes et al. (2015) who discuss the aeronautics have implemented methods that allow the human On the other hand, robotics, autonomous vehicle design and role data acquisition from HiTL systems to infer the state operator and the automatic control systems to work in On the other hand, robotics, autonomous vehicle design and of the human and the prediction of their future decisions for role of data acquisition from HiTL systems to infer the state aeronautics have implemented methods that allow the human better actuation. Gaham et al. (2015) propose a framework role of data acquisition from HiTL systems to infer the state operator and the automatic control systems to work in aeronautics have implemented methods that human of the human and the prediction of their future decisions for tandem. aeronautics have implemented methods that allow allowtothe thework human better actuation. Gaham etand al. give (2015) propose aresults framework of the human and the of future operator and the automatic control systems in for HiTL control systems preliminary onfor of the human andGaham the prediction prediction of their their future decisions decisions fora tandem. operator and the automatic control systems to work in better actuation. et al. (2015) propose a framework operator and the automatic control systems to work in for HiTL control systems and give preliminary on a better actuation. Gaham et al. (2015) propose aaresults framework tandem. scheduling process that integrates HiTL CPS. Recent better actuation. Gaham et al. (2015) propose framework tandem. for HiTL control systems give preliminary results on a 1.1 Role of operators in plants tandem. scheduling process that and integrates HiTLtaken CPS. Recent for HiTL and give results on developments in systems HiTL control have place for HiTL control control systems and give preliminary preliminary results on inaa 1.1 Role of operators in plants scheduling process that integrates HiTL CPS. Recent developments in HiTL control have taken place in scheduling process that integrates HiTL CPS. Recent 1.1 Role of operators in plants autonomous vehicle design, robotics and aeronautics. scheduling process that integrates HiTL CPS. Recent 1.1 of in developments in HiTL control have taken place in TheRole on-going applications vehicle design, robotics and aeronautics. 1.1 Role of operators operators in plants plantsin cyber-physical and human autonomous developments in HiTL control have taken place Chipalkatty et al. (2013) give a method to predict the role of developments in HiTL control have and takenaeronautics. place in ina The on-going applications in cyber-physical and human autonomous vehicle design, robotics systems borrowapplications concepts dating back decadesand when the Chipalkatty et vehicle al. (2013) giveby a method to predict the role of a autonomous design, robotics and aeronautics. The on-going in cyber-physical human human operating a robot using system identification autonomous vehicle design, robotics and aeronautics. systems borrow concepts dating back systems decadesand when the human Chipalkatty et al. (2013) give aa method to predict the role of a The on-going applications in human theoretical frameworks of human centric were being operating a robot by using system identification The on-going applications in cyber-physical cyber-physical and human Chipalkatty et give to predict the of systems borrow concepts dating back decades when the techniques while implementing Model Predictive Control Chipalkatty et al. al. (2013) (2013) giveby a method method to predict the role role of aa theoretical frameworks of human centric systems were being human operating a robot using system identification systems borrow concepts dating back decades when the developed. The concepts landmark paper by systems Bainbridge (1983) while implementing Model Control systems borrow dating back decades were when the techniques human operating aa They robot by identification theoretical frameworks of human centric being (MPC) in the robot. thatsystem aPredictive simple zero-order human operating robotconclude by using using system identification developed. Theincrease landmark paper bywith Bainbridge (1983) techniques while implementing Model Predictive Control theoretical frameworks of human centric systems were being discussed the in problems operators when (MPC) in the robot. They conclude that a simple zero-order theoretical frameworks of human centric systems were being techniques while implementing Model Predictive Control developed. The landmark paper by Bainbridge (1983) hold model for human decision prediction is preferred by techniques while implementing Model Predictive Control discussed the increase in problems with operators when (MPC) in the robot. They conclude that a simple zero-order developed. The landmark paper by Bainbridge (1983) automations are put in place. As more complex tasks are hold model for human decision prediction is preferred by developed. The landmark paper by Bainbridge (1983) (MPC) in the robot. They conclude that a simple zero-order discussed the increase in problems with operators when operators, compared to manual control, fixed prediction (MPC) in the robot. They conclude that a simple zero-order automations are put in place. As more complex tasks are hold model for human is preferred by discussed increase in operators when automated, humans are still required towith monitor or take over operators, compared to decision manual prediction control,identification fixed prediction discussed the the increase in problems problems with operators when hold model for human decision prediction is by automations are put in place. As more complex tasks are horizon model least squares system with hold model forand human decision prediction is preferred preferred by automated, humans are still required to monitor or take over operators, compared to manual control, fixed prediction automations are put in place. As more complex tasks are during abnormal events. It is thus counter-intuitive to think model andmodel. least squares system identification with automationshumans are putare in still place. As more complex tasksover are horizon operators, compared to manual control, fixed prediction automated, required to monitor or take variable horizon Erez et al. (2013) implement an operators, compared to manual control, fixed prediction during abnormal events. It is thus counter-intuitive to think horizon model and least squares system identification with automated, humans are required to over horizonand model. Erez et system al. (2013) implementwith an automated, humans are still still required to monitor monitor or or take take over variable horizon model least squares identification during abnormal events. It is thus counter-intuitive to think horizon model least squares system identification horizonandmodel. Erez et al. (2013) implementwith an during events. during abnormal abnormal events. It It is is thus thus counter-intuitive counter-intuitive to to think think 292 variable variable model. Erez et Copyright ©2018 variablebyhorizon horizon model. Erezreserved. et al. al. (2013) (2013) implement implement an an 2405-8963 © 2019, IFAC IFAC (International Federation of Automatic Control) Hosting Elsevier Ltd. All rights Copyright ©2018 IFAC 292 Peer review©2018 under IFAC responsibility of International Federation of Automatic Copyright 292Control. Copyright ©2018 IFAC 292 10.1016/j.ifacol.2019.01.043 Copyright ©2018 IFAC 292
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MPC-based robot where the controller is assigned low-level control and the human operator takes the role of a supervisory control. The operator selects tasks to be completed which updates the cost function in the MPC realtime. Although the MPC was robust to model errors, the performance degraded in the presence of large estimation errors. The authors suggest better sensors and simulations to correct for the reduced performance. Anderson et al. (2010) develop an MPC-based vehicle control where the human and MPC control moves are weighted based on threat level in real-time. The MPC plays a role of corrective action if the human violates a control move. Barz et al. (2017) provide a framework for integrating operators in cyber-physical production facilities by using latest digital pen-based data logging systems. The operators enter any anomalies they detect using the digital pen technology, which is then linked with semantic knowledge sources like process and business models. Maestre et al. (2014) implement a HiTL MPC for a large-scale irrigation canal model, where the human operators are spread across a wide land area. In the context of healthcare, Cameron and Bequette (2012) discuss the importance of event (meal, exercise, sleep) prediction for a closed-loop artificial pancreas for individuals with type 1 diabetes. This is similar to the problem of predicting future decisions by humans in the HiTL framework.
2.
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METHODOLOGY
The general framework of the proposed approach is given in the block diagram in Fig 1 which is a subset of the Control Room of the Future proposed in Ghosh and Bequette (2018). The plant is controlled by operators and MPC, with additional decision-support from the SCR. The supervisory layer outputs are controlled by inputs from the SCR that can detect changes in human facial expressions, use natural language processing, use process history data, and do realtime calculations and forecasting to assist the operators in decision-making. It is the SCR that is able to collate all the data and project it as a network of the entire plant with personnel.
1.3 Issues facing the process industry Forbes et al. (2015) review the challenges automation faces in the process industry from operators. They discuss the benefits of MPC and the role new design and control systems should play to build operator trust in MPC. Leveson and Stephanopoulos (2014) discuss safety as a system-wide problem and not just localised equipment fault or human error. As a result, advanced control systems that consider a plethora of features from operators, sensors, ergonomics, socio-economic data etc. are crucial to implement the next generation of industrial control systems.
Fig 1: Smart Control Room framework for the two-tank problem in this study. A multiple-model MPC is used to solve the nonlinear problem where the MPC manipulates control valve 1, two operators are controlling control valves 2&3.
1.4 Overview of the work
The plant to be controlled is a two-tank interacting system with equations:
2.1 Nonlinear plant equations
In the present work the authors build on a proposed framework (Ghosh and Bequette, 2018) where a plantwide control system is supplemented with the latest technology that predicts human behaviour and takes pre-emptive steps to decide the human-MPC role in controlling the plant. In the past work it was demonstrated that the Smart Control Room (SCR), equipped with information of the entire plant including personnel, was able to detect process and human interaction changes as changes in graph/network-based metrics. This framework addresses the need for system-wide analysis as mentioned in Leveson and Stephanopoulos (2014).
𝑑𝑑ℎ1 = (𝐾𝐾𝑖𝑖𝑖𝑖 𝐹𝐹𝑖𝑖𝑖𝑖 − 𝐾𝐾1 𝛽𝛽1 √ℎ1− ℎ2 )/𝐴𝐴1 (1) 𝑑𝑑𝑑𝑑 𝑑𝑑ℎ2 = (𝐾𝐾1 𝛽𝛽1 √ℎ1 − ℎ2 − 𝐾𝐾3 𝛽𝛽2 √ℎ2 )/𝐴𝐴2 (2) 𝑑𝑑𝑑𝑑 Where h1, h2 are the tank heights (m), β1 β2 are flow coefficients, Kin K1 and K2 are valve fractions (0-1) for CV1,2&3 (Fig 1), Fin is maximum inlet flowrate (m3/min), and A1 A2 are tank cross-sectional areas. The control objective is to bring the liquid height in tank 2 from zero to a setpoint, thus forming a simple start-up problem.
The current work demonstrates how a closed-loop MPC performs when operators make changes to inputs not manipulated by the MPC. Moreover, the operator inputs are not known by the MPC. The analysis focusses on network parameters to show how the structure of the entire HiTL system evolves with time. 293
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Table 1: Two tank parameter values Parameter
Value
Kin,K1,K2
0-1
β1, β2
1 m3/min/min0.5
A1, A2
10 m2
2.4 Simulations and Assumptions Three sets of simulations are conducted as mentioned in Table2. For each set, the network analysis is done dynamically to show how topological changes occur using the network spectra. Fig 2 is an example of the network for Sim1 at steady state.
2.2 Multiple model-based MPC The MPC framework used in this work is based on multiple linear models with state estimation (Kuure-Kinsey and Bequette, 2010). Such an approach allows the implementation of linear MPC (computationally favourable) and dynamic updates of the linear model best approximating the local operating regime of the plant. As a result, operators can learn the operating conditions during start-up/shutdowns or transient processes. The multiple-model method consists of a bank of linear statespace models approximating the possible operating regimes of the nonlinear plant. Since the MPC manipulated input considered is Kin (equation 1), the operating regimes considered are four values of Kin (10-4, 0.3, 0.5 & 1).
Fig 2: Network structure of the final steady-state plant for Sim1. Sup-Supervisor, Op-Operator, CV-Control valve, SenSensors, MPC-Model Predictive Controller. Assumptions:
A detailed explanation of the multiple-model MPC formulation can be found in Kuure-Kinsey and Bequette (2010). For this study the prediction horizon and control horizon were set at 60 and 3 sampling time units (0.1 min) respectively. Constraints were implemented on the input and the optimisation problem was solved using MATLAB quadprog. The control objective is to bring the liquid height in tank 2 to 2m from 0m.
1. No noise in data, all sensors are perfect. 2. Supervisor and operators are in full communication, so the link weights between Sup and Op1, Op2 are always 1. 3. All network links are normalised using the final steadystate value of the link for the respective simulations. As a result, all networks will have a link-weight of 1 at steadystate in Fig 2.
2.3 Graph Theoretic Analysis Due to the connectedness of HiTL systems, the authors developed a preliminary framework (Ghosh and Bequette, 2018) which projects any plant and the human personnel as a single network which can be tracked in time. It was demonstrated how such a network can detect changes due to human decisions, sensor biases etc. when no controller is inloop i.e. complete manual control. In this work, the framework will be extended to demonstrate how MPC and humans sharing control of a process affect the network topology of the whole system. Such a technique directly addresses the systemic issues mentioned in Section 1.3 of this paper.
4. All data is available without losses.
Table 2: Simulations conducted in the present study
The detailed methodology of developing a network for HiTL plants can be found in Ghosh and Bequette (2018). In brief, the nodes are the physical entities in the plant (equipment, personnel etc) and the links between nodes are the variables connecting them (e.g. flowrate between tank and valve).
294
Kin(MPC)
K2 (Op1)
K3(Op2)
Sim1
[0,1]
1
1
Sim2
[0,1]
0.8(100-120min)
1
Sim3
[0,1]
1
0.8(50-100min)
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Fig 3: Input-Output responses of the two-tank plant for simulations 1, 2 and 3. Inputs are from MPC, Operator 1 (Op1) and Operator 2 (Op2). 3. RESULTS AND DISCUSSIONS
3.2 Network analyses Given the complex dynamics in a HiTL-MPC system, a network framework is very useful to understand the topological changes of the process. As discussed in Ghosh and Bequette (2018), the eigen spectra of the normalized Laplacian matrix holds special information some of which are:
3.1 MPC performance The toy problem used here has the MPC controlling the liquid level in tank 2 using Kin, and operators are manipulating K2 and K3 manually. This represents a simple HiTl-MPC system where the MPC does not have the K2 and K3 information. Fig 3 gives the dynamic response of the system for Sim1,2&3. It is interesting to note that when operators make changes to K2 and K3, the MPC can take corrective action and bring the system to setpoint. The human inputs can be observed as discrete changes to the entire process. The advantage of multiple-model MPC is that it finds the most probable local linear model. Since the linear models are designed around some operating conditions, it can assist the operators to learn/know about the current conditions especially during start-ups/shutdowns/transients. An example of this is represented in Fig 4 where the most active model can be identified using the model weights for Sim1.
1. Eigenvalues (eval) are always scaled from 0-2 i.e, 0≤λ1≤..≤λn≤2, n (11 in this example) being the network size. 2. All eigenvectors (evec) are orthonormal and hold crucial network partitioning information, especially the evec corresponding to the second smallest eval i.e, λ2. 3. For dynamic networks, the spectral dynamics describe the overall structural changes The previous work (Ghosh and Bequette, 2018) discussed the role of evecs in network partitioning and using that to detect changes in two sets of plant dynamics. In this work, it is shown how the corresponding evals also hold structural information. Fig 5 describes the temporal change of the second and eighth eval for Sim 1,2&3. The evec for λ2 describes the first global partitioning of the network. The evec for λ8 (or any higher index of λ) describes how the network partitions can be re-organised to get back the original network. Hence, structurally these are very different network information. Since the dynamics of Sim1&2 are the same till 100 mins, eval2&8 dynamics are also the same. However, due to operator changes at t=100min in Sim2, the network changes and as a result the spectra also change. Similar comparisons can be drawn between the other simulation sets. It is easy to see why the networks are different, because of different input and variable magnitudes, the link-weights as shown in Fig 2 will be different. This is
Fig 4: Model weight evolution of the model-bank in two-tank problem. 295
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Fig 5: Dynamic eigenvalue profile for eigenvalue 2 and eigenvalue 8 for simulations 1,2 and 3. the advantage of using networks as it clearly brings out any changes in the connecting components.
personnel and train them for emergency events or complex processes like start-ups/shutdowns.
Another useful metric is the shortest path or simply a path between two nodes. Shortest path is the optimal path between two nodes based on the path cost. However, in many problems, the sub-optimal path is more relevant than the shortest path. As an example, from Fig 2, the shortest path between MPC and Tank2 is just 2 path-lengths via Sen2. However, the sub-optimal path MPC-CV1-Tank1-CV2Tank2 is the actual path via which the MPC changes affect the Tank2 liquid height. Such an analysis is useful for fault propagation analysis. It quickly reduces the complex network to a sub-network which can be analysed more easily.
Fig 6: A simple representation of human cognition in the presence of Smart Control Room (SCR).
The simple demonstrations shown here bring out the complex interplay of closed-loop dynamics of various unit-operations and operators making changes to the system. Indeed, one can also add complexities to this HiTL framework by assuming cognitive models for the human personnel and simulating the dynamic effects of such decisions and how does an MPC (or any other kinds of controller) react to such changes with/without prior cognitive model information. Fig 6 presents a simple input-output representation of human cognitive functions. The operator can interact with the SCR in real-time, get information about the plant states or any relevant information like a scheduled start-up/shutdown in future, analyse plant data-history and use statistical trends to compare current versus past plant performances etc. It is interesting to note that the human response will be very different in the context of experience, culture, location, health etc.
4. CONCLUSIONS AND FUTURE WORK Preliminary results are presented in the context of a smart control room, underlying MPC laws incorporating HiTL and simulations using a two-tank interacting system to show the effect of a combined control strategy using MPC and human operators. Future work will involve three broad directions: 1. Integration of natural language processing, facial recognition, and big-data analytics using facilities at RPI (Su, 2017) as the Smart Control Room. This would also require experiments in the SCR with rigorous process simulations mimicking the plant. 2. Network based fault detection layer to provide assistance to operators and take corrective actions. The complete spectra of the network must be utilized to find out both global and local changes of the process.
On the other hand, a supervisory control layer can use the network changes and give suggestions/alarms to the operators and change setpoints for the MPC. As a result, the supervisory layer can dynamically change the network itself. This kind of a set-up is especially useful to train personnel by simply replacing the real plant with simulations. The SCR can then propose scenarios based on the experience of the
3. Simple cognitive and logic models to mimic operator experience and decision making.
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ACKNOWLEDGEMENTS
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Ghosh, S., & Bequette, B. W., (2018). Using Cognitive Computing for the Control Room of the Future. In Comp. Aid. Chem. Eng., Vol. 44, pp. 649-654. Elsevier.
The authors acknowledge funding from the Department of Energy Clean Energy Smart Manufacturing Innovation Institute (CESMII).
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A Framework for the Control Room of the Future: A Framework for the Control Room of the Future: A Framework for the Control Room the Future: Human-in-the-loop MPCof A for the Control Room Room of the the Future: Future: Human-in-the-loop MPCof A Framework Framework for the Control Human-in-the-loop MPC Human-in-the-loop Sambit Ghosh*, B. WayneMPC Bequette* Human-in-the-loop MPC Sambit Ghosh*, B. Wayne Bequette*
Sambit Ghosh*, B. Wayne Wayne Bequette* Bequette* Sambit Ghosh*, Sambit Ghosh*, B. B.Rensselaer Wayne Bequette* *Dept. Of Chemical and Biological Engineering, Polytechnic Institute, Troy, NY 12180 *Dept. Of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180 USA (Tel:(518) 276-6377; e-mail: [email protected]). *Dept. Of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180 USA (Tel:(518) 276-6377; e-mail: [email protected]). *Dept. Of Chemical and Biological Engineering, Rensselaer Polytechnic *Dept. Of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Institute, Troy, Troy, NY NY 12180 12180 USA (Tel:(518) 276-6377; e-mail: [email protected]). USA (Tel:(518) 276-6377; e-mail: [email protected]). USA (Tel:(518) 276-6377; e-mail: [email protected]). Abstract: A general framework and preliminary results for the Control Room of the Future are presented. Abstract: A general framework and preliminary resultslanguage for the Control Roomfacial of the recognition, Future are presented. The inclusion of latest technology (e.g., natural processing, big-data Abstract: A general framework and preliminary results for the Control Room of the Future are presented. The inclusion of latest technology (e.g., natural language processing, facial recognition, big-data Abstract: A general framework and preliminary results for the Control Room of the Future are presented. analytics) in control rooms, Model Predictive Control (MPC), and integration of human-in-the-loop is Abstract: A general framework and preliminary results for the Control Room of the Future are presented. The inclusion of latest technology (e.g., natural language processing, facial recognition, big-data analytics) in control rooms, Model Predictive Control (MPC), and integration of human-in-the-loop is The inclusion of latest technology (e.g., natural language processing, facial recognition, big-data proposed. The paper focuses on process start-ups and uses a two-tank interacting nonlinear system to The inclusion of latest technology (e.g., natural language processing, facial recognition, big-data analytics) in control rooms, Model Predictive Control and integration of human-in-the-loop is proposed. The paper focuses on process and (MPC), uses of a two-tank interacting nonlinear systemare to analytics) in control rooms, Model Control (MPC), and integration human-in-the-loop is demonstrate the dynamic changes inPredictive the start-ups network topology the Threeof of simulations analytics) in control rooms, Model Predictive Control (MPC), and plant. integration ofsets human-in-the-loop is proposed. The paper focuses on process start-ups and uses a two-tank interacting nonlinear system to demonstrate the dynamic changes in the start-ups network topology of thetoplant. Three A sets of simulations are proposed. The paper focuses on process and uses a two-tank interacting nonlinear system to conducted to highlight the MPC performance under human inputs the system. multiple-model MPC proposed. The paper focuses on process start-ups and uses of a two-tank interacting nonlinear systemare to demonstrate the dynamic changes in the network topology the plant. Three sets of simulations conducted highlight thethe MPC performance under human to the of system. multiple-model MPC demonstrate the dynamic changes in network topology of the Three sets of are approach isto used to solve nonlinear control problem andinputs the using A it in human-in-the-loop demonstrate the dynamic changes in the the network topology of benefits thetoplant. plant. Three sets of simulations simulationsMPC are conducted to highlight the MPC performance under human inputs the system. A multiple-model approach is used to solve the nonlinear control problem and the benefits of using it in human-in-the-loop conducted to highlight the MPC performance under human inputs to the system. A multiple-model MPC systems are discussed. Future work includes using the network formulation as a supervisory fault conducted toused highlight thethe MPC performance under human inputs to the of system. A multiple-model MPC approach is to solve nonlinear control problem and the benefits using it in human-in-the-loop systems discussed. Future work includes using the formulation asina human-in-the-loop supervisory fault approach is used to the nonlinear control problem and the benefits of it detectionare layer, providing suggestions to the personnel vianetwork the Smart Control Room and implementing approach is used to solve solveFuture the nonlinear control problem and benefits of using using it ina human-in-the-loop systems are discussed. work includes using the network formulation as supervisory fault detection layer, providing suggestions to the personnel via the Smart Control Room and implementing systems are discussed. Future work includes using the network formulation as a supervisory fault advancedare models for predicting human decisions for safe process start-ups and transient operations. systems discussed. Future work includes using the network formulation as a and supervisory fault detection layer, providing suggestions to the personnel via the Smart Control Room implementing advanced layer, modelsproviding for predicting human decisions for safe via process start-ups and transient operations. detection suggestions to the personnel the Smart Control Room and implementing detection layer, providing suggestions to the personnel via the Smart Control Room and implementing advanced models for predicting human for safe process start-ups and transient operations. © 2019, IFAC (International Federation ofdecisions Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Keywords: Model Predictive Control, start-up, human-in-the-loop, safety, operators. advanced models for predicting human decisions for and advanced predicting humanstart-up, decisions for safe safe process process start-ups start-ups and transient transient operations. operations. Keywords:models Modelfor Predictive Control, human-in-the-loop, safety, operators. Keywords: Model Predictive Control, start-up, human-in-the-loop, safety, operators. Keywords: safety, operators. Keywords: Model Model Predictive Predictive Control, Control, start-up, start-up, human-in-the-loop, human-in-the-loop, safety, operators.the role of the operator. When that automation eliminates 1. INTRODUCTION that automation eliminates role offrom the aoperator. When operators make aeliminates decision, the it comes very complex 1. INTRODUCTION that automation the role of the operator. When operators make a decision, it comes from a very complex that automation eliminates the role of the operator. When 1. INTRODUCTION interaction of his/her process knowledge, experience, the that automation eliminates the role of the operator. When 1. INTRODUCTION operators make a decision, it comes from a very complex Industry 4.0 1. envisions the future of manufacturing systems as interaction of his/her process knowledge, experience, the INTRODUCTION operators make a decision, it comes from a very complex goals he/she or the process demands (Rasmussen, 1983) and operators make a decision, it comes from a very complex Industry 4.0 envisions thewhere futurecyber-physical of manufacturing systems as interaction of his/her process knowledge, experience, the an integrated framework systems (CPS) goals he/sheof or his/her the process demands (Rasmussen, 1983) interaction process knowledge, the Industry 4.0 envisions the future of manufacturing systems as the human cognition itself. Moreover, theexperience, health of and the interaction of his/her process knowledge, experience, an integrated framework where cyber-physical systems (CPS) Industry 4.0 envisions the future of manufacturing systems as goals he/she or the process demands (Rasmussen, 1983) and play an important role (Lasi et al., 2014). Humans are an human cognition itself. Moreover, the health of the Industry 4.0 envisions thewhere futurecyber-physical of manufacturing systems as the goals he/she or the process demands (Rasmussen, 1983) and an integrated framework systems (CPS) operator, time should be (Rasmussen, noted. It is easy to and see goals he/she oroftheday/year process demands 1983) play an part important rolea complex (Lasi et al., Humans an integrated framework where systems (CPS) the human cognition itself. Moreover, the of the integral of such and 2014). dynamic system,are asan a operator, time of day/year should bewith noted. It health is to easy to see an integrated framework whereetcyber-physical cyber-physical systems (CPS) the human cognition itself. Moreover, the health of the play an important role (Lasi al., 2014). Humans are an that behaviour will change respect a plant’s the human cognition itself. Moreover, the health of the integral part of such a complex and dynamic system, as a play an important role (Lasi et al., 2014). Humans are an operator, time of day/year should be noted. It is easy to see result safety is of paramount importance for the plant, the that human behaviour will change with respect to a plant’s play an part important rolea complex (Lasi et al., 2014). Humans areasana operator, time of day/year should be noted. It is easy to see integral of such and dynamic system, location on the globe due to social cultural differences, operator, time of day/year should be noted. It is easy to see result safety is of paramount importance for the plant, the that human behaviour will change with respect to a plant’s integral part of such a complex and dynamic system, as a operators and of thesuch surrounding environment. A prime example on behaviour the globe duechange to social cultural differences, integral part a complex and dynamic system, asthea location that human will with respect aa plant’s result safety paramount importance for the plant, governmental norms, weather patterns etc. On theto other hand, that human behaviour will change with respect to plant’s operators and is theof surrounding environment. ABP prime example location on the globe due to social cultural differences, result safety is of paramount importance for the plant, the of a systematic safety-related failure is the Texas City governmental norms, weather patterns etc. On the other hand, result safety is of paramount importance for the plant, the location on the globe due to social cultural differences, operators and the surrounding environment. A prime example the problem of loss of situation awareness and skills in location on the globe due to socialetc. cultural differences, of a systematic safety-related failure theABP Texas operators and surrounding prime example governmental norms, patterns On the hand, refinery explosion in 2005,environment. which is occurred due toCity a the problem of loss weather ofcontrol situation awareness andother skills in operators and the thesafety-related surrounding environment. ABP prime example governmental norms, weather patterns etc. On the other hand, of a systematic failure is the Texas City human-out-of-the-loop was discussed by Endsley et governmental norms, weather patterns etc. On the other hand, refinery explosion in 2005, failure which is occurred due and/or toCitya the problem of loss of situation awareness and skills in of aa systematic the combination of safety-related mis-communication, mis-calibrated control was awareness discussed by Endsley et of systematic safety-related failure is the BP BP Texas Texas Citya human-out-of-the-loop the problem of loss of situation and skills in refinery explosion in 2005, which occurred due to al. (1995). They also propose levels of automation explaining the problem of loss of situation awareness and skills in combination of poor mis-communication, mis-calibrated and/or refinery explosion in 2005, which occurred due to human-out-of-the-loop control was discussed by Endsley et failing sensors, control design, (1995). They alsoofpropose levels of explaining refinery explosion in 2005,system whichinterface occurred due failure to aa al. human-out-of-the-loop control was discussed by et combination of mis-communication, mis-calibrated and/or the different kinds roles played byautomation the automation and human-out-of-the-loop control was of discussed by Endsley Endsley et failing sensors, poor control system interface design, failure combination of mis-communication, mis-calibrated and/or al. (1995). They also propose levels automation explaining to follow documented start-up protocols, and outdated relief different kinds of roles played by the automation and combination of poor mis-communication, mis-calibrated and/or the al. (1995). They also propose levels of automation explaining failing sensors, control system interface design, failure operators. al. (1995). They also propose levels of automation explaining to follow documented start-up protocols, and outdated relief the different kinds of roles played by the automation and failing sensors, poor control system interface design, system design (CSB, operators. failing sensors, poor 2007). control system interface design, failure failure the different to follow documented start-up protocols, and outdated relief the different kinds kinds of of roles roles played played by by the the automation automation and and system design (CSB, 2007). operators. to follow documented start-up protocols, and outdated relief to follow documented start-up protocols, and outdated relief operators. system design (CSB, 2007). 1.2 Human-in-the-loop (HiTL) control operators. system design (CSB, 2007). Although industrial system design (CSB, safety 2007). systems can address most faults 1.2 Human-in-the-loop (HiTL) control Although industrial safety systems can address mosthumanfaults 1.2 Human-in-the-loop (HiTL) control and safety breaches, it has not implemented the latest 1.2 Human-in-the-loop (HiTL) Although industrial safety systems can address most faults A of HiTL applications in the Internet-of-things 1.2 review Human-in-the-loop (HiTL) control control and safety breaches, it has not implemented the latest humanAlthough industrial safety systems can address most faults in-the-loop (HiTL) techniques to takecan pre-emptive measures. Although industrial safety systems address most faults A review of HiTLbyapplications in(2015) the Internet-of-things and safety breaches, it has not implemented the latest humanframework is given Nunes et al. who discuss the in-the-loop (HiTL) techniques to take pre-emptive measures. and safety breaches, it has not implemented the latest humanA review of HiTL applications in the Internet-of-things On the other hand, robotics, autonomous vehicle design and and safety breaches, it has not implemented the latest humanframework is given by Nunes et al. (2015) who discuss the A review of HiTL applications in the Internet-of-things in-the-loop (HiTL) techniques to take pre-emptive measures. role of data acquisition from HiTL systems to infer the state review of HiTLbyapplications in(2015) the Internet-of-things On the other(HiTL) hand, robotics, autonomous vehicle design and A in-the-loop techniques to take pre-emptive measures. framework is given Nunes et al. who discuss the aeronautics have implemented methods that allow the human in-the-loop (HiTL) techniques to take pre-emptive measures. role of data acquisition from HiTL systems to infer the state framework is given by Nunes et al. (2015) who discuss the On the other hand, robotics, autonomous vehicle design and of theofhuman and thebyprediction of their future decisions for framework is given Nunes et al. (2015) who discuss the aeronautics have implemented methods that allow the human On the other hand, robotics, autonomous vehicle design and role data acquisition from HiTL systems to infer the state operator and the automatic control systems to work in On the other hand, robotics, autonomous vehicle design and of the human and the prediction of their future decisions for role of data acquisition from HiTL systems to infer the state aeronautics have implemented methods that allow the human better actuation. Gaham et al. (2015) propose a framework role of data acquisition from HiTL systems to infer the state operator and the automatic control systems to work in aeronautics have implemented methods that human of the human and the prediction of their future decisions for tandem. aeronautics have implemented methods that allow allowtothe thework human better actuation. Gaham etand al. give (2015) propose aresults framework of the human and the of future operator and the automatic control systems in for HiTL control systems preliminary onfor of the human andGaham the prediction prediction of their their future decisions decisions fora tandem. operator and the automatic control systems to work in better actuation. et al. (2015) propose a framework operator and the automatic control systems to work in for HiTL control systems and give preliminary on a better actuation. Gaham et al. (2015) propose aaresults framework tandem. scheduling process that integrates HiTL CPS. Recent better actuation. Gaham et al. (2015) propose framework tandem. for HiTL control systems give preliminary results on a 1.1 Role of operators in plants tandem. scheduling process that and integrates HiTLtaken CPS. Recent for HiTL and give results on developments in systems HiTL control have place for HiTL control control systems and give preliminary preliminary results on inaa 1.1 Role of operators in plants scheduling process that integrates HiTL CPS. Recent developments in HiTL control have taken place in scheduling process that integrates HiTL CPS. Recent 1.1 Role of operators in plants autonomous vehicle design, robotics and aeronautics. scheduling process that integrates HiTL CPS. Recent 1.1 of in developments in HiTL control have taken place in TheRole on-going applications vehicle design, robotics and aeronautics. 1.1 Role of operators operators in plants plantsin cyber-physical and human autonomous developments in HiTL control have taken place Chipalkatty et al. (2013) give a method to predict the role of developments in HiTL control have and takenaeronautics. place in ina The on-going applications in cyber-physical and human autonomous vehicle design, robotics systems borrowapplications concepts dating back decadesand when the Chipalkatty et vehicle al. (2013) giveby a method to predict the role of a autonomous design, robotics and aeronautics. The on-going in cyber-physical human human operating a robot using system identification autonomous vehicle design, robotics and aeronautics. systems borrow concepts dating back systems decadesand when the human Chipalkatty et al. (2013) give aa method to predict the role of a The on-going applications in human theoretical frameworks of human centric were being operating a robot by using system identification The on-going applications in cyber-physical cyber-physical and human Chipalkatty et give to predict the of systems borrow concepts dating back decades when the techniques while implementing Model Predictive Control Chipalkatty et al. al. (2013) (2013) giveby a method method to predict the role role of aa theoretical frameworks of human centric systems were being human operating a robot using system identification systems borrow concepts dating back decades when the developed. The concepts landmark paper by systems Bainbridge (1983) while implementing Model Control systems borrow dating back decades were when the techniques human operating aa They robot by identification theoretical frameworks of human centric being (MPC) in the robot. thatsystem aPredictive simple zero-order human operating robotconclude by using using system identification developed. Theincrease landmark paper bywith Bainbridge (1983) techniques while implementing Model Predictive Control theoretical frameworks of human centric systems were being discussed the in problems operators when (MPC) in the robot. They conclude that a simple zero-order theoretical frameworks of human centric systems were being techniques while implementing Model Predictive Control developed. The landmark paper by Bainbridge (1983) hold model for human decision prediction is preferred by techniques while implementing Model Predictive Control discussed the increase in problems with operators when (MPC) in the robot. They conclude that a simple zero-order developed. The landmark paper by Bainbridge (1983) automations are put in place. As more complex tasks are hold model for human decision prediction is preferred by developed. The landmark paper by Bainbridge (1983) (MPC) in the robot. They conclude that a simple zero-order discussed the increase in problems with operators when operators, compared to manual control, fixed prediction (MPC) in the robot. They conclude that a simple zero-order automations are put in place. As more complex tasks are hold model for human is preferred by discussed increase in operators when automated, humans are still required towith monitor or take over operators, compared to decision manual prediction control,identification fixed prediction discussed the the increase in problems problems with operators when hold model for human decision prediction is by automations are put in place. As more complex tasks are horizon model least squares system with hold model forand human decision prediction is preferred preferred by automated, humans are still required to monitor or take over operators, compared to manual control, fixed prediction automations are put in place. As more complex tasks are during abnormal events. It is thus counter-intuitive to think model andmodel. least squares system identification with automationshumans are putare in still place. As more complex tasksover are horizon operators, compared to manual control, fixed prediction automated, required to monitor or take variable horizon Erez et al. (2013) implement an operators, compared to manual control, fixed prediction during abnormal events. It is thus counter-intuitive to think horizon model and least squares system identification with automated, humans are required to over horizonand model. Erez et system al. (2013) implementwith an automated, humans are still still required to monitor monitor or or take take over variable horizon model least squares identification during abnormal events. It is thus counter-intuitive to think horizon model least squares system identification horizonandmodel. Erez et al. (2013) implementwith an during events. during abnormal abnormal events. It It is is thus thus counter-intuitive counter-intuitive to to think think 292 variable variable model. Erez et Copyright ©2018 variablebyhorizon horizon model. Erezreserved. et al. al. (2013) (2013) implement implement an an 2405-8963 © 2019, IFAC IFAC (International Federation of Automatic Control) Hosting Elsevier Ltd. All rights Copyright ©2018 IFAC 292 Peer review©2018 under IFAC responsibility of International Federation of Automatic Copyright 292Control. Copyright ©2018 IFAC 292 10.1016/j.ifacol.2019.01.043 Copyright ©2018 IFAC 292
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MPC-based robot where the controller is assigned low-level control and the human operator takes the role of a supervisory control. The operator selects tasks to be completed which updates the cost function in the MPC realtime. Although the MPC was robust to model errors, the performance degraded in the presence of large estimation errors. The authors suggest better sensors and simulations to correct for the reduced performance. Anderson et al. (2010) develop an MPC-based vehicle control where the human and MPC control moves are weighted based on threat level in real-time. The MPC plays a role of corrective action if the human violates a control move. Barz et al. (2017) provide a framework for integrating operators in cyber-physical production facilities by using latest digital pen-based data logging systems. The operators enter any anomalies they detect using the digital pen technology, which is then linked with semantic knowledge sources like process and business models. Maestre et al. (2014) implement a HiTL MPC for a large-scale irrigation canal model, where the human operators are spread across a wide land area. In the context of healthcare, Cameron and Bequette (2012) discuss the importance of event (meal, exercise, sleep) prediction for a closed-loop artificial pancreas for individuals with type 1 diabetes. This is similar to the problem of predicting future decisions by humans in the HiTL framework.
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METHODOLOGY
The general framework of the proposed approach is given in the block diagram in Fig 1 which is a subset of the Control Room of the Future proposed in Ghosh and Bequette (2018). The plant is controlled by operators and MPC, with additional decision-support from the SCR. The supervisory layer outputs are controlled by inputs from the SCR that can detect changes in human facial expressions, use natural language processing, use process history data, and do realtime calculations and forecasting to assist the operators in decision-making. It is the SCR that is able to collate all the data and project it as a network of the entire plant with personnel.
1.3 Issues facing the process industry Forbes et al. (2015) review the challenges automation faces in the process industry from operators. They discuss the benefits of MPC and the role new design and control systems should play to build operator trust in MPC. Leveson and Stephanopoulos (2014) discuss safety as a system-wide problem and not just localised equipment fault or human error. As a result, advanced control systems that consider a plethora of features from operators, sensors, ergonomics, socio-economic data etc. are crucial to implement the next generation of industrial control systems.
Fig 1: Smart Control Room framework for the two-tank problem in this study. A multiple-model MPC is used to solve the nonlinear problem where the MPC manipulates control valve 1, two operators are controlling control valves 2&3.
1.4 Overview of the work
The plant to be controlled is a two-tank interacting system with equations:
2.1 Nonlinear plant equations
In the present work the authors build on a proposed framework (Ghosh and Bequette, 2018) where a plantwide control system is supplemented with the latest technology that predicts human behaviour and takes pre-emptive steps to decide the human-MPC role in controlling the plant. In the past work it was demonstrated that the Smart Control Room (SCR), equipped with information of the entire plant including personnel, was able to detect process and human interaction changes as changes in graph/network-based metrics. This framework addresses the need for system-wide analysis as mentioned in Leveson and Stephanopoulos (2014).
𝑑𝑑ℎ1 = (𝐾𝐾𝑖𝑖𝑖𝑖 𝐹𝐹𝑖𝑖𝑖𝑖 − 𝐾𝐾1 𝛽𝛽1 √ℎ1− ℎ2 )/𝐴𝐴1 (1) 𝑑𝑑𝑑𝑑 𝑑𝑑ℎ2 = (𝐾𝐾1 𝛽𝛽1 √ℎ1 − ℎ2 − 𝐾𝐾3 𝛽𝛽2 √ℎ2 )/𝐴𝐴2 (2) 𝑑𝑑𝑑𝑑 Where h1, h2 are the tank heights (m), β1 β2 are flow coefficients, Kin K1 and K2 are valve fractions (0-1) for CV1,2&3 (Fig 1), Fin is maximum inlet flowrate (m3/min), and A1 A2 are tank cross-sectional areas. The control objective is to bring the liquid height in tank 2 from zero to a setpoint, thus forming a simple start-up problem.
The current work demonstrates how a closed-loop MPC performs when operators make changes to inputs not manipulated by the MPC. Moreover, the operator inputs are not known by the MPC. The analysis focusses on network parameters to show how the structure of the entire HiTL system evolves with time. 293
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Table 1: Two tank parameter values Parameter
Value
Kin,K1,K2
0-1
β1, β2
1 m3/min/min0.5
A1, A2
10 m2
2.4 Simulations and Assumptions Three sets of simulations are conducted as mentioned in Table2. For each set, the network analysis is done dynamically to show how topological changes occur using the network spectra. Fig 2 is an example of the network for Sim1 at steady state.
2.2 Multiple model-based MPC The MPC framework used in this work is based on multiple linear models with state estimation (Kuure-Kinsey and Bequette, 2010). Such an approach allows the implementation of linear MPC (computationally favourable) and dynamic updates of the linear model best approximating the local operating regime of the plant. As a result, operators can learn the operating conditions during start-up/shutdowns or transient processes. The multiple-model method consists of a bank of linear statespace models approximating the possible operating regimes of the nonlinear plant. Since the MPC manipulated input considered is Kin (equation 1), the operating regimes considered are four values of Kin (10-4, 0.3, 0.5 & 1).
Fig 2: Network structure of the final steady-state plant for Sim1. Sup-Supervisor, Op-Operator, CV-Control valve, SenSensors, MPC-Model Predictive Controller. Assumptions:
A detailed explanation of the multiple-model MPC formulation can be found in Kuure-Kinsey and Bequette (2010). For this study the prediction horizon and control horizon were set at 60 and 3 sampling time units (0.1 min) respectively. Constraints were implemented on the input and the optimisation problem was solved using MATLAB quadprog. The control objective is to bring the liquid height in tank 2 to 2m from 0m.
1. No noise in data, all sensors are perfect. 2. Supervisor and operators are in full communication, so the link weights between Sup and Op1, Op2 are always 1. 3. All network links are normalised using the final steadystate value of the link for the respective simulations. As a result, all networks will have a link-weight of 1 at steadystate in Fig 2.
2.3 Graph Theoretic Analysis Due to the connectedness of HiTL systems, the authors developed a preliminary framework (Ghosh and Bequette, 2018) which projects any plant and the human personnel as a single network which can be tracked in time. It was demonstrated how such a network can detect changes due to human decisions, sensor biases etc. when no controller is inloop i.e. complete manual control. In this work, the framework will be extended to demonstrate how MPC and humans sharing control of a process affect the network topology of the whole system. Such a technique directly addresses the systemic issues mentioned in Section 1.3 of this paper.
4. All data is available without losses.
Table 2: Simulations conducted in the present study
The detailed methodology of developing a network for HiTL plants can be found in Ghosh and Bequette (2018). In brief, the nodes are the physical entities in the plant (equipment, personnel etc) and the links between nodes are the variables connecting them (e.g. flowrate between tank and valve).
294
Kin(MPC)
K2 (Op1)
K3(Op2)
Sim1
[0,1]
1
1
Sim2
[0,1]
0.8(100-120min)
1
Sim3
[0,1]
1
0.8(50-100min)
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Fig 3: Input-Output responses of the two-tank plant for simulations 1, 2 and 3. Inputs are from MPC, Operator 1 (Op1) and Operator 2 (Op2). 3. RESULTS AND DISCUSSIONS
3.2 Network analyses Given the complex dynamics in a HiTL-MPC system, a network framework is very useful to understand the topological changes of the process. As discussed in Ghosh and Bequette (2018), the eigen spectra of the normalized Laplacian matrix holds special information some of which are:
3.1 MPC performance The toy problem used here has the MPC controlling the liquid level in tank 2 using Kin, and operators are manipulating K2 and K3 manually. This represents a simple HiTl-MPC system where the MPC does not have the K2 and K3 information. Fig 3 gives the dynamic response of the system for Sim1,2&3. It is interesting to note that when operators make changes to K2 and K3, the MPC can take corrective action and bring the system to setpoint. The human inputs can be observed as discrete changes to the entire process. The advantage of multiple-model MPC is that it finds the most probable local linear model. Since the linear models are designed around some operating conditions, it can assist the operators to learn/know about the current conditions especially during start-ups/shutdowns/transients. An example of this is represented in Fig 4 where the most active model can be identified using the model weights for Sim1.
1. Eigenvalues (eval) are always scaled from 0-2 i.e, 0≤λ1≤..≤λn≤2, n (11 in this example) being the network size. 2. All eigenvectors (evec) are orthonormal and hold crucial network partitioning information, especially the evec corresponding to the second smallest eval i.e, λ2. 3. For dynamic networks, the spectral dynamics describe the overall structural changes The previous work (Ghosh and Bequette, 2018) discussed the role of evecs in network partitioning and using that to detect changes in two sets of plant dynamics. In this work, it is shown how the corresponding evals also hold structural information. Fig 5 describes the temporal change of the second and eighth eval for Sim 1,2&3. The evec for λ2 describes the first global partitioning of the network. The evec for λ8 (or any higher index of λ) describes how the network partitions can be re-organised to get back the original network. Hence, structurally these are very different network information. Since the dynamics of Sim1&2 are the same till 100 mins, eval2&8 dynamics are also the same. However, due to operator changes at t=100min in Sim2, the network changes and as a result the spectra also change. Similar comparisons can be drawn between the other simulation sets. It is easy to see why the networks are different, because of different input and variable magnitudes, the link-weights as shown in Fig 2 will be different. This is
Fig 4: Model weight evolution of the model-bank in two-tank problem. 295
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Fig 5: Dynamic eigenvalue profile for eigenvalue 2 and eigenvalue 8 for simulations 1,2 and 3. the advantage of using networks as it clearly brings out any changes in the connecting components.
personnel and train them for emergency events or complex processes like start-ups/shutdowns.
Another useful metric is the shortest path or simply a path between two nodes. Shortest path is the optimal path between two nodes based on the path cost. However, in many problems, the sub-optimal path is more relevant than the shortest path. As an example, from Fig 2, the shortest path between MPC and Tank2 is just 2 path-lengths via Sen2. However, the sub-optimal path MPC-CV1-Tank1-CV2Tank2 is the actual path via which the MPC changes affect the Tank2 liquid height. Such an analysis is useful for fault propagation analysis. It quickly reduces the complex network to a sub-network which can be analysed more easily.
Fig 6: A simple representation of human cognition in the presence of Smart Control Room (SCR).
The simple demonstrations shown here bring out the complex interplay of closed-loop dynamics of various unit-operations and operators making changes to the system. Indeed, one can also add complexities to this HiTL framework by assuming cognitive models for the human personnel and simulating the dynamic effects of such decisions and how does an MPC (or any other kinds of controller) react to such changes with/without prior cognitive model information. Fig 6 presents a simple input-output representation of human cognitive functions. The operator can interact with the SCR in real-time, get information about the plant states or any relevant information like a scheduled start-up/shutdown in future, analyse plant data-history and use statistical trends to compare current versus past plant performances etc. It is interesting to note that the human response will be very different in the context of experience, culture, location, health etc.
4. CONCLUSIONS AND FUTURE WORK Preliminary results are presented in the context of a smart control room, underlying MPC laws incorporating HiTL and simulations using a two-tank interacting system to show the effect of a combined control strategy using MPC and human operators. Future work will involve three broad directions: 1. Integration of natural language processing, facial recognition, and big-data analytics using facilities at RPI (Su, 2017) as the Smart Control Room. This would also require experiments in the SCR with rigorous process simulations mimicking the plant. 2. Network based fault detection layer to provide assistance to operators and take corrective actions. The complete spectra of the network must be utilized to find out both global and local changes of the process.
On the other hand, a supervisory control layer can use the network changes and give suggestions/alarms to the operators and change setpoints for the MPC. As a result, the supervisory layer can dynamically change the network itself. This kind of a set-up is especially useful to train personnel by simply replacing the real plant with simulations. The SCR can then propose scenarios based on the experience of the
3. Simple cognitive and logic models to mimic operator experience and decision making.
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ACKNOWLEDGEMENTS
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Ghosh, S., & Bequette, B. W., (2018). Using Cognitive Computing for the Control Room of the Future. In Comp. Aid. Chem. Eng., Vol. 44, pp. 649-654. Elsevier.
The authors acknowledge funding from the Department of Energy Clean Energy Smart Manufacturing Innovation Institute (CESMII).
Kuure-Kinsey, M. and Bequette, B.W., (2010). Multiple model predictive control strategy for disturbance rejection. Ind. Eng. Chem. Res., 49(17), 7983-7989. Lasi, H., Fettke, P., Kemper, H.G., Feld, T. and Hoffmann, M., (2014). Ind. 4.0. Bus. & Inf. Sys. Eng., 6(4), pp.239-242.
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