- Email: [email protected]
A Plant Modeling Process for Practical Control System Design
Copyright CO IFAC Advances in Automotive Control, Mohican State Park, Loudonville, Ohio, USA, 1998
A PLANT MODELlNG PROCESS FOR PRACTICAL CONTROL SYSTEM DESIGN Peter J. Maloney
Delphi Energy and Engine Management Systems Control Systems Engineering Group, MIC 483-342-101 Milford Proving Ground, 3300 GM Rd., Milford, Michigan 48380-3726 USA EMAIL: [email protected]
Abstract: A plant modeling process is presented with an example application. The process was designed to achieve development cost savings by removing roadblocks to the application of classical mechanics and control theory in production automotive control systems. The development of small, physicallybased, effective plant models is facilitated by a communication format that coordinates conflicting conceptual and detail design perspectives inherent in the engineering workforce. Copyright © 1998 IFAC Keywords: Plant modeling, Design process, Holism, Atomism, Human factors, Design-for-Manufacture, Bond-Graph
The work presented in this paper represents an attempt to "identify and treat the disease" related to the previously listed "symptoms" via design process changes.
1. INTRODUCTION
As part of an effort to reduce development costs associated with Engine Management Systems (EMS), the broad characteristics of selected production EMS control algorithms were reviewed. The review focused only on attributes of production EMS algorithms in order to uncover actual problems that increase cost.
In (D'souza, 1988) and (Paynter, 1961), the importance of small, effective plant models for control system design was stressed. All of the high-cost symptoms listed above were found to be related to the large, complex, heuristic nature of production EMS algorithms.
The following qualitative characteristics of the production EMS algorithms were identified as "symptoms" related to high control system development cost: •
Small changes in engine hardware result in large control algorithm changes
•
Control algorithm complexity is increasing much faster than overall performance
•
Function and stability are achieved through time-intensilte empirical tuning, with little evidence of formal control design
2. ENGINEERING PROBLEM DEFINITION
The following four problem areas in EMS algorithm design were identified as possible causes for the "symptoms" listed in Section 1:
131
•
Heuristics dominate over physics
•
Poor technical communication
•
Designs are too detailed
•
Too many adjustable features
2.1 Heuristics dominate over physics
In (Wickens, 1984), psychological differences between individuals were discussed in terms of "survey" verses "route" knowledge. Wickens presented research indicating that some individuals are biased towards an atomistlc "route-knowledge" approach to problem solutions, which Is highly detailed and analytical. This type of person would tend to communicate in detailed, verbal form and would excel at solving clearly defined, detailed problems (Wickens, 1984).
Heuristic approaches have been used very effectively in production EMS algorithms, but have become unnecessarily large, complex, and expensive due to their unclear relationship to the physics of the automotive plant. Significant cost savings are expected from the replacement of heuristic algorithms with algorithms based on classical mechanics for the follOWing reasons: •
Classical mechanics has been proven effective in practical application for almost three centuries, and in large part enabled the industrial revolution
•
When describing physical system behavior, classical mechanics will almost always yield a smaller, more comprehensive description of system dynamics than a purely heuristic approach, because it was created to deal with physical systems.
Other individuals are more biased towards a holistic "survey-knowledge" approach, which relies on a general but vague map and intuition to navigate to a problem solution. This type of person would tend to communicate with general graphical maps and would excel at setting up poorly defined problems for solution (Wickens, 1984). (Giesecke et. ai, 1981) described design engineering drawings as a set of tools used to hamess the positive aspects of both the holistic and atomistic viewpoints.
The following assumption was made to explain the near absence of classical mechanics in production EMS algorithms: •
All inspection of the engineering communications used in production EMS algorithm design has lead the author to the following conclusions:
Practitioners of classical mechanics in the auto industry have been unable to make simplifying assumptions as broadly as the practitioners of heuristic approaches, possibly due to a detail-oriented viewpoint.
•
Most of the communication takes place in "route-knowledge" fonn (detailed computer code)
•
Most of the design or "survey-knowledge" is stored in the minds of the masterdesigner ("Lead calibrator" in the automotive business), and is not always written down or placed in drawings
•
Production algorithm designers have been given the conflicting roles of artist and detail-designer, resulting in a heuristic (holistic, artistic, vague) algorithm structure described and communicated in a very detailed (atomistic, analytical, precise) computer-code fonn. The heuristic approach taken by production designers has been remarkably successful, but their difficult assignment has resulted in an emphasis on the extremes required for successful design (holism and atomism), and has resulted in algorithms that are both structurally vague and at the same time very detailed.
2.2 Poor technical communication
The importance of human communication is implied in (Genesis 11 ~ 1-9), and discussed in terms of technical graphics in (Giesecke et. ai, 1981). In the Tower of Babel account (Genesis 11 :19), God recognized that man had developed a
common language. and said, .....now nothing that they propose to do will be withheld from them." Confusing the language of the builders was sufficient to halt construction of the tower of Babel, and technology was not the issue. (Giesecke et. al. 1981) classified two main types of drawings: artistic and technical. Artistic drawings (e.g. concept sketches) express "aesthetic, philosophic, or other abstract ideas" to the builders. Technical drawings are made to represent a design in detail so that it can be constructed and replicated efficiently.
132
2.3 Designs are too detailed
3. PROBLEM SOLUTION· PLANT MODELlNG PROCESS
In (Kuhn, 1962), and (Paynter, 1961) the possibility of an inherent detail-oriented, atomistic bias in conventional science is raised.
In recognition of the fact that EMS algorithms are designed by humans, and that most EMS design problems start with vague descriptions and end up with detailed solutions, it was considered crucial to develop a process to balance the holistic and atomistic biases inherent in any design team to insure an appropriate design direction and level of design depth.
(Paynter, 1961) contended that the advent of quantum and relativistic mechanics in the early 1900's shifted attention away from the macroscopic behavior of physical systems, because it was believed that in-depth understanding of the basic building blocks of matter would yield increased insight into the behavior of the entire system.
Figure 1 shows a design process intended to correct the problems identified in Section 2. The process consists of the following main aspects:
(Kuhn, 1962) contended that the main body of scientific activity is composed of "puzzlesolving" activities, as opposed to the production of new theories. "Puzzle-solving" involves adding detail to existing theories by applying them in increasing depth to various problems. •
• •
The plant modeling process was developed from the mechanical design concepts in (Pugh, 1996), since mechanical design is a very mature field and has a long history of using graphical communication formats to unite concept and detail. The underlying philosophy is used to keep several design themes in the minds of the designers so that their work is structured well and kept efficient.
The highly disproportionate ratio of detailed software design documentation to concept design documentation in production EMS algorithm design may indicate an inherent atomistic bias. The result of such a bias may explain the rapid increase in EMS RAM, ROM, and throughput requirements, often independent of changes in base engine hardware.
As in mechanical design, the plant-modeling design phases facilitate a smooth transition from a qualitative visual model to a quantitative mathematical model through the use of graphical communication formats.
2.4 Too many adjustable features In the well-known manufacturing engineering book by (Boothroyd, et. ai, 1994), Design-ForManufacture (DFM) and Design-For-Assembly (DFA) principles are presented. One of the main points of the book is the idea that adjustable features should be minimized, since they put the function of a design at risk and increase cost. Vague, poor designs require many adjustable features, but good designs function well without them. •
An underlying philosophy Design phases
3.1 Underlying Philosophy The underlying plant modeling philosophy consists of a series of design themes taken from the Mechanical Design and Manufacturing Engineering fields:
The author observed that production EMS algorithm design not only allows adjustable features (calibration parameters), but generally sees adjustability as desirable and necessary. EMS design seems to have been influenced by the software industry, which tends to sell software based on the number of adjustable features.
133
•
Use classical mechanics and make broad assumptions (holism)
•
Avoid excessive detail (atomism)
•
Minimize adjustable features so that calibration activities and their associated costs and risks can be reduced significantly
Observe Plant, Visualize
Market Assessment
Dl"W"'\hla.l'n
Observation: Engine power oU1plt and emissions are controled via fuel, engine gas mass ftow rates, and spark. Hypothesis: In typical EMS, few gas mass flow raes are known. Providing explicit estimaB s of all releva1t gas states wll help the trol
,
Tecmical Problem Definition
~ft'" ~ftl .... Ift ...
And all oontrol-relevant gas mass flow rates and gas pressures for engine A, so flat the information is of sufficient quality to meet emissions standard B, and diagnostic requirement C.
'\
/."~~~ r.;:;~,~ . : -_:....... ~_-::~ . \~.
~ fIJ: ~ ·
...
','; ,.ea l t
..
, 1
;
.
.~..~ -:;. ~' _.. -
~~
..
B
·~i
·tl;'~.,-
,2 I
Analytical Sc hematic ... Map
Simplified Spatial Map
Spedalty knowledge requirements: • Fundanental fluid mechanics • Basic EEcrcuit theory
I
3
Analytical Mathematical Map
~_
x = g(x,u) .....
y=CX
.........
w
~
,
,Ll
51 Model-fltting, Sensitivity, Validation
Controls Block Diagram
Control/Estimator Design, Rapid Prototyping, and Software Engineering Processes
x,x t
7
Retum to step 4 with model reductions or en han cs ments, exit if finished.
'6
8
Figure 1. Plant Modeling Process Overview
3.2 Design Phases
3.2.3 Technical Problem Definition Phase
The design phases shown in Figure 1 were adapted from a typical mechanical design process (Pugh) and extended for the purpose of structured plant model development.
Step 3 of Figure 1 represents the beginning of technical work. Design requirements are recorded based on the visualized problem solution from Step 2. Initially the requirements can and should be qualitative for flexibility in the early phases of design. As the work progresses. more detailed specifications can be set.
The process was first applied in the development of the Pneumatic State Model (PSM), a simple, compact, comprehensive engine gas flow network model. The model was used to develop the Pneumatic State Estimator (PSE) described in (Maloney and Olin, 1998).
Step 3 corresponds roughly to a requirements document phase in mechanical design, but has the follOWing additional modeling-specific features:
3.2.1 Market Assessment Phase Step 1 of Figure 1 shows a market assessment phase in the plant model development process, which is responsible for defining a general qualitative product goal, based on a need assessment and an initially vague proposal for fulfilling the need. Step 1 is analogous to the Quality Function Deployment methodology in mechanical design.
•
"Through" and "across" variables should be chosen (e.g. torque and speed, voltage and current, concentration and molar flow rate•...)
•
Specialty fields should be identified that most closely relate to the plant to be modeled (e.g. Fluid Mechanics, Chemical Engineering, ...)
The PSM concept was developed to provide EMS customers with comprehensive gas flow rate and pressure estimates for use in control and diagnostic algorithms
Pressure and mass flow were chosen as "across" and "through" variables for the PSM since their product relates to power flow if scaled with gas density.
The motivation to produce the algorithm came from the observation that engine power and emissions are controlled to a large extent by using valves to manipulate flow rates. The physical basis of the algorithm was designed to cut cost through direct. automated calibration procedures. and to provide a basis for control system design.
Fluid Mechanics was chosen as a primary specialty field for model development. since the medium of energy transfer was a fluid. The plant model was visualized in Step 2 as a network, making electrical circuit analysis analogies (see Rizzoni. 1993) and BondGraphs (see Kamopp et al. 1990) an attractive tool.
3.2.2 Observation and Visualization Phase
3.2.4 Spatial Mapping Phase
Step 2 in Figure 1 shows an Observation and Visualization process. which represents the transition between a qualitative marketing problem definition, and an engineering problem definition. The PSM was visualized mentally as a planar flow network. from looking at, listening to, and operating an actual three dimensional engine.
Step 4 in Figure 1 involves the development of a "Spatial Map" suitable to convey the main aspects of the model concept, intent. and structure to analysts and other concept designers. The diagram is analogous to a concept sketch in mechanical design. The problem scope and depth are qualitatively conveyed by the map, which corresponds to "survey knowledge" discussed in Section 2.2.
It was assumed that almost all (if not all) physical models are bom first from qualitative mental visualization. and later given detail and quantitative description by the artificial tool of mathematics. For this reason. observation and visualization were placed before analytical work to remove the heavy burden of detail from the concept development phase.
A "Spatial Map" was developed for the PSM. by depicting the engine as a planar series of valves, pipes, and restrictions assembled around a main pumping element (the engine). The diagram focuses on the similarities of engine subsystems (e.g. throttle, idle air. EGR. and purge are all controlled by "valve elements"). 135
3.2.5 Schematic Mapping Phase
observer (Maloney and OUn, 1998), or is useful for desktop control system design.
Step 5 in Figure 1 involves the development of a schematic map, appropriate for bridging the gap between the artistic concept sketch in Figure 4 and the later detail-design steps. The Electrical Engineering field uses electrical schematics to begin analytical development by default. Mechanical Engineering and other fields have many different types of diagrams, making communication much more difficult.
4. SUMMARY AND CONCLUSIONS A plant modeling process was developed to remove roadblocks to the application of classical mechanics and control theory in production EMS to decrease algorithm complexity, decrease algorithm sensitivity to hardware changes, and save development time associated with algorithm tuning. An example application was shown for the design of the Pneumatic State Model described in (Maloney and Olin, 1998). The plant modeling process is an important first step in the emulation of the mature and highly integrated design processes found in design engineering.
The Pneumatic State Model used a BondGraph model to serve as a schematic. The model was then transformed into an electrical analogy so that it could be communicated to the widest possible group of analysts and designers.
3.2.6 Mathematical Mapping Phase Step 6 in Figure 1 involves the transformation of the schematic map in Step 5 to a mathematical equation-set. The Bond-graph methodology allowed equation assembly by inspection for the Pneumatic State Model. At Step 6 the design has reached a highly analytical form, which should be used for work purposes but not for concept communication.
REFERENCES Boothroyd, Dewhurst, and Knight (1994). Product Design for Manufacture and Assembly. M. Dekker, New York. D'Souza, A. F. (1988). Design of Control Systems. pp. 9-14. Prentice Hall, Inc. New Jersey. Giesecke, Mitchell, Spencer, Hill, Loving, and Dygdon (1981). Engineering Graphics. :P ed. Pp. 1-8. Macmillan, New York. The Holy Bible, New King James Version (1990). Genesis 11 :1-9. Thomas Nelson, Inc. Karnopp, Margolis, and Rosenburg (1990).. System Dynamics, A Unified Approach, 2nd ed., John Wiley, New York. Kuhn, T. S. (1962). The Structure of Scientific Revolutions. 3 rd ed. pp. 35-42. The University of Chicago Press, Chicago. Maloney, P. and Olin, P. (1998). Pneumatic and Thermal State Estimators for Production Engine Control and Diagnostics. SAE Paper 980517. Paynter, H. M. (1961). Class notes for M.I. T. Course 2.751: Analysis and Design of Engineering Systems, p. 10, 17. The M.I.T. Press, Cambridge MA. Pugh, Stuart (1996). Creating Innovative Products Using Total Design, p. 51. Addison Wesley, New York. Rizzoni, G. (1993). Principles and Applications of Electrical Engineering. pp. 64-182, R. D. Irwin, Inc., Illinois. Wickens, C. D. (1984). Engineering Psychology and Human Performance, pp. 120-123, pp. 163-166, pp. 185-187, Charles E. Merrill, Columbus Ohio.
3.2.7 Block-Diagram Phase Step 7 in Figure 1 Involves the preparation of a model for controls-oriented analysis and observer development. The equations of Step 6 are transformed into block diagram form. It should be noted that despite its graphical nature, the block diagram is not effective for conveying the physics of a plant at the concept level, as evidenced by its difference in appearance to Steps 2 and 4.
3.2.8 Model-fitting and Analysis Phase Step 8 in Figure 1 represents the first introduction of detailed quantitative analysis (other than "ball-parking" exercises). The analysis phase represents the majority of modeling work, consisting of activities such as test design, data gathering, model-fitting, sensitivity analysis, model reduction proposals, detailed specifications, and formal model validation. Since design is iterative in nature, earlier design phases can, and should be revisited and refined due to the availability of quantitative information. After Step 8 has reached satisfactory completion, a plant model is ready for software application in the form of an 136
A PLANT MODELlNG PROCESS FOR PRACTICAL CONTROL SYSTEM DESIGN Peter J. Maloney
Delphi Energy and Engine Management Systems Control Systems Engineering Group, MIC 483-342-101 Milford Proving Ground, 3300 GM Rd., Milford, Michigan 48380-3726 USA EMAIL: [email protected]
Abstract: A plant modeling process is presented with an example application. The process was designed to achieve development cost savings by removing roadblocks to the application of classical mechanics and control theory in production automotive control systems. The development of small, physicallybased, effective plant models is facilitated by a communication format that coordinates conflicting conceptual and detail design perspectives inherent in the engineering workforce. Copyright © 1998 IFAC Keywords: Plant modeling, Design process, Holism, Atomism, Human factors, Design-for-Manufacture, Bond-Graph
The work presented in this paper represents an attempt to "identify and treat the disease" related to the previously listed "symptoms" via design process changes.
1. INTRODUCTION
As part of an effort to reduce development costs associated with Engine Management Systems (EMS), the broad characteristics of selected production EMS control algorithms were reviewed. The review focused only on attributes of production EMS algorithms in order to uncover actual problems that increase cost.
In (D'souza, 1988) and (Paynter, 1961), the importance of small, effective plant models for control system design was stressed. All of the high-cost symptoms listed above were found to be related to the large, complex, heuristic nature of production EMS algorithms.
The following qualitative characteristics of the production EMS algorithms were identified as "symptoms" related to high control system development cost: •
Small changes in engine hardware result in large control algorithm changes
•
Control algorithm complexity is increasing much faster than overall performance
•
Function and stability are achieved through time-intensilte empirical tuning, with little evidence of formal control design
2. ENGINEERING PROBLEM DEFINITION
The following four problem areas in EMS algorithm design were identified as possible causes for the "symptoms" listed in Section 1:
131
•
Heuristics dominate over physics
•
Poor technical communication
•
Designs are too detailed
•
Too many adjustable features
2.1 Heuristics dominate over physics
In (Wickens, 1984), psychological differences between individuals were discussed in terms of "survey" verses "route" knowledge. Wickens presented research indicating that some individuals are biased towards an atomistlc "route-knowledge" approach to problem solutions, which Is highly detailed and analytical. This type of person would tend to communicate in detailed, verbal form and would excel at solving clearly defined, detailed problems (Wickens, 1984).
Heuristic approaches have been used very effectively in production EMS algorithms, but have become unnecessarily large, complex, and expensive due to their unclear relationship to the physics of the automotive plant. Significant cost savings are expected from the replacement of heuristic algorithms with algorithms based on classical mechanics for the follOWing reasons: •
Classical mechanics has been proven effective in practical application for almost three centuries, and in large part enabled the industrial revolution
•
When describing physical system behavior, classical mechanics will almost always yield a smaller, more comprehensive description of system dynamics than a purely heuristic approach, because it was created to deal with physical systems.
Other individuals are more biased towards a holistic "survey-knowledge" approach, which relies on a general but vague map and intuition to navigate to a problem solution. This type of person would tend to communicate with general graphical maps and would excel at setting up poorly defined problems for solution (Wickens, 1984). (Giesecke et. ai, 1981) described design engineering drawings as a set of tools used to hamess the positive aspects of both the holistic and atomistic viewpoints.
The following assumption was made to explain the near absence of classical mechanics in production EMS algorithms: •
All inspection of the engineering communications used in production EMS algorithm design has lead the author to the following conclusions:
Practitioners of classical mechanics in the auto industry have been unable to make simplifying assumptions as broadly as the practitioners of heuristic approaches, possibly due to a detail-oriented viewpoint.
•
Most of the communication takes place in "route-knowledge" fonn (detailed computer code)
•
Most of the design or "survey-knowledge" is stored in the minds of the masterdesigner ("Lead calibrator" in the automotive business), and is not always written down or placed in drawings
•
Production algorithm designers have been given the conflicting roles of artist and detail-designer, resulting in a heuristic (holistic, artistic, vague) algorithm structure described and communicated in a very detailed (atomistic, analytical, precise) computer-code fonn. The heuristic approach taken by production designers has been remarkably successful, but their difficult assignment has resulted in an emphasis on the extremes required for successful design (holism and atomism), and has resulted in algorithms that are both structurally vague and at the same time very detailed.
2.2 Poor technical communication
The importance of human communication is implied in (Genesis 11 ~ 1-9), and discussed in terms of technical graphics in (Giesecke et. ai, 1981). In the Tower of Babel account (Genesis 11 :19), God recognized that man had developed a
common language. and said, .....now nothing that they propose to do will be withheld from them." Confusing the language of the builders was sufficient to halt construction of the tower of Babel, and technology was not the issue. (Giesecke et. al. 1981) classified two main types of drawings: artistic and technical. Artistic drawings (e.g. concept sketches) express "aesthetic, philosophic, or other abstract ideas" to the builders. Technical drawings are made to represent a design in detail so that it can be constructed and replicated efficiently.
132
2.3 Designs are too detailed
3. PROBLEM SOLUTION· PLANT MODELlNG PROCESS
In (Kuhn, 1962), and (Paynter, 1961) the possibility of an inherent detail-oriented, atomistic bias in conventional science is raised.
In recognition of the fact that EMS algorithms are designed by humans, and that most EMS design problems start with vague descriptions and end up with detailed solutions, it was considered crucial to develop a process to balance the holistic and atomistic biases inherent in any design team to insure an appropriate design direction and level of design depth.
(Paynter, 1961) contended that the advent of quantum and relativistic mechanics in the early 1900's shifted attention away from the macroscopic behavior of physical systems, because it was believed that in-depth understanding of the basic building blocks of matter would yield increased insight into the behavior of the entire system.
Figure 1 shows a design process intended to correct the problems identified in Section 2. The process consists of the following main aspects:
(Kuhn, 1962) contended that the main body of scientific activity is composed of "puzzlesolving" activities, as opposed to the production of new theories. "Puzzle-solving" involves adding detail to existing theories by applying them in increasing depth to various problems. •
• •
The plant modeling process was developed from the mechanical design concepts in (Pugh, 1996), since mechanical design is a very mature field and has a long history of using graphical communication formats to unite concept and detail. The underlying philosophy is used to keep several design themes in the minds of the designers so that their work is structured well and kept efficient.
The highly disproportionate ratio of detailed software design documentation to concept design documentation in production EMS algorithm design may indicate an inherent atomistic bias. The result of such a bias may explain the rapid increase in EMS RAM, ROM, and throughput requirements, often independent of changes in base engine hardware.
As in mechanical design, the plant-modeling design phases facilitate a smooth transition from a qualitative visual model to a quantitative mathematical model through the use of graphical communication formats.
2.4 Too many adjustable features In the well-known manufacturing engineering book by (Boothroyd, et. ai, 1994), Design-ForManufacture (DFM) and Design-For-Assembly (DFA) principles are presented. One of the main points of the book is the idea that adjustable features should be minimized, since they put the function of a design at risk and increase cost. Vague, poor designs require many adjustable features, but good designs function well without them. •
An underlying philosophy Design phases
3.1 Underlying Philosophy The underlying plant modeling philosophy consists of a series of design themes taken from the Mechanical Design and Manufacturing Engineering fields:
The author observed that production EMS algorithm design not only allows adjustable features (calibration parameters), but generally sees adjustability as desirable and necessary. EMS design seems to have been influenced by the software industry, which tends to sell software based on the number of adjustable features.
133
•
Use classical mechanics and make broad assumptions (holism)
•
Avoid excessive detail (atomism)
•
Minimize adjustable features so that calibration activities and their associated costs and risks can be reduced significantly
Observe Plant, Visualize
Market Assessment
Dl"W"'\hla.l'n
Observation: Engine power oU1plt and emissions are controled via fuel, engine gas mass ftow rates, and spark. Hypothesis: In typical EMS, few gas mass flow raes are known. Providing explicit estimaB s of all releva1t gas states wll help the trol
,
Tecmical Problem Definition
~ft'" ~ftl .... Ift ...
And all oontrol-relevant gas mass flow rates and gas pressures for engine A, so flat the information is of sufficient quality to meet emissions standard B, and diagnostic requirement C.
'\
/."~~~ r.;:;~,~ . : -_:....... ~_-::~ . \~.
~ fIJ: ~ ·
...
','; ,.ea l t
..
, 1
;
.
.~..~ -:;. ~' _.. -
~~
..
B
·~i
·tl;'~.,-
,2 I
Analytical Sc hematic ... Map
Simplified Spatial Map
Spedalty knowledge requirements: • Fundanental fluid mechanics • Basic EEcrcuit theory
I
3
Analytical Mathematical Map
~_
x = g(x,u) .....
y=CX
.........
w
~
,
,Ll
51 Model-fltting, Sensitivity, Validation
Controls Block Diagram
Control/Estimator Design, Rapid Prototyping, and Software Engineering Processes
x,x t
7
Retum to step 4 with model reductions or en han cs ments, exit if finished.
'6
8
Figure 1. Plant Modeling Process Overview
3.2 Design Phases
3.2.3 Technical Problem Definition Phase
The design phases shown in Figure 1 were adapted from a typical mechanical design process (Pugh) and extended for the purpose of structured plant model development.
Step 3 of Figure 1 represents the beginning of technical work. Design requirements are recorded based on the visualized problem solution from Step 2. Initially the requirements can and should be qualitative for flexibility in the early phases of design. As the work progresses. more detailed specifications can be set.
The process was first applied in the development of the Pneumatic State Model (PSM), a simple, compact, comprehensive engine gas flow network model. The model was used to develop the Pneumatic State Estimator (PSE) described in (Maloney and Olin, 1998).
Step 3 corresponds roughly to a requirements document phase in mechanical design, but has the follOWing additional modeling-specific features:
3.2.1 Market Assessment Phase Step 1 of Figure 1 shows a market assessment phase in the plant model development process, which is responsible for defining a general qualitative product goal, based on a need assessment and an initially vague proposal for fulfilling the need. Step 1 is analogous to the Quality Function Deployment methodology in mechanical design.
•
"Through" and "across" variables should be chosen (e.g. torque and speed, voltage and current, concentration and molar flow rate•...)
•
Specialty fields should be identified that most closely relate to the plant to be modeled (e.g. Fluid Mechanics, Chemical Engineering, ...)
The PSM concept was developed to provide EMS customers with comprehensive gas flow rate and pressure estimates for use in control and diagnostic algorithms
Pressure and mass flow were chosen as "across" and "through" variables for the PSM since their product relates to power flow if scaled with gas density.
The motivation to produce the algorithm came from the observation that engine power and emissions are controlled to a large extent by using valves to manipulate flow rates. The physical basis of the algorithm was designed to cut cost through direct. automated calibration procedures. and to provide a basis for control system design.
Fluid Mechanics was chosen as a primary specialty field for model development. since the medium of energy transfer was a fluid. The plant model was visualized in Step 2 as a network, making electrical circuit analysis analogies (see Rizzoni. 1993) and BondGraphs (see Kamopp et al. 1990) an attractive tool.
3.2.2 Observation and Visualization Phase
3.2.4 Spatial Mapping Phase
Step 2 in Figure 1 shows an Observation and Visualization process. which represents the transition between a qualitative marketing problem definition, and an engineering problem definition. The PSM was visualized mentally as a planar flow network. from looking at, listening to, and operating an actual three dimensional engine.
Step 4 in Figure 1 involves the development of a "Spatial Map" suitable to convey the main aspects of the model concept, intent. and structure to analysts and other concept designers. The diagram is analogous to a concept sketch in mechanical design. The problem scope and depth are qualitatively conveyed by the map, which corresponds to "survey knowledge" discussed in Section 2.2.
It was assumed that almost all (if not all) physical models are bom first from qualitative mental visualization. and later given detail and quantitative description by the artificial tool of mathematics. For this reason. observation and visualization were placed before analytical work to remove the heavy burden of detail from the concept development phase.
A "Spatial Map" was developed for the PSM. by depicting the engine as a planar series of valves, pipes, and restrictions assembled around a main pumping element (the engine). The diagram focuses on the similarities of engine subsystems (e.g. throttle, idle air. EGR. and purge are all controlled by "valve elements"). 135
3.2.5 Schematic Mapping Phase
observer (Maloney and OUn, 1998), or is useful for desktop control system design.
Step 5 in Figure 1 involves the development of a schematic map, appropriate for bridging the gap between the artistic concept sketch in Figure 4 and the later detail-design steps. The Electrical Engineering field uses electrical schematics to begin analytical development by default. Mechanical Engineering and other fields have many different types of diagrams, making communication much more difficult.
4. SUMMARY AND CONCLUSIONS A plant modeling process was developed to remove roadblocks to the application of classical mechanics and control theory in production EMS to decrease algorithm complexity, decrease algorithm sensitivity to hardware changes, and save development time associated with algorithm tuning. An example application was shown for the design of the Pneumatic State Model described in (Maloney and Olin, 1998). The plant modeling process is an important first step in the emulation of the mature and highly integrated design processes found in design engineering.
The Pneumatic State Model used a BondGraph model to serve as a schematic. The model was then transformed into an electrical analogy so that it could be communicated to the widest possible group of analysts and designers.
3.2.6 Mathematical Mapping Phase Step 6 in Figure 1 involves the transformation of the schematic map in Step 5 to a mathematical equation-set. The Bond-graph methodology allowed equation assembly by inspection for the Pneumatic State Model. At Step 6 the design has reached a highly analytical form, which should be used for work purposes but not for concept communication.
REFERENCES Boothroyd, Dewhurst, and Knight (1994). Product Design for Manufacture and Assembly. M. Dekker, New York. D'Souza, A. F. (1988). Design of Control Systems. pp. 9-14. Prentice Hall, Inc. New Jersey. Giesecke, Mitchell, Spencer, Hill, Loving, and Dygdon (1981). Engineering Graphics. :P ed. Pp. 1-8. Macmillan, New York. The Holy Bible, New King James Version (1990). Genesis 11 :1-9. Thomas Nelson, Inc. Karnopp, Margolis, and Rosenburg (1990).. System Dynamics, A Unified Approach, 2nd ed., John Wiley, New York. Kuhn, T. S. (1962). The Structure of Scientific Revolutions. 3 rd ed. pp. 35-42. The University of Chicago Press, Chicago. Maloney, P. and Olin, P. (1998). Pneumatic and Thermal State Estimators for Production Engine Control and Diagnostics. SAE Paper 980517. Paynter, H. M. (1961). Class notes for M.I. T. Course 2.751: Analysis and Design of Engineering Systems, p. 10, 17. The M.I.T. Press, Cambridge MA. Pugh, Stuart (1996). Creating Innovative Products Using Total Design, p. 51. Addison Wesley, New York. Rizzoni, G. (1993). Principles and Applications of Electrical Engineering. pp. 64-182, R. D. Irwin, Inc., Illinois. Wickens, C. D. (1984). Engineering Psychology and Human Performance, pp. 120-123, pp. 163-166, pp. 185-187, Charles E. Merrill, Columbus Ohio.
3.2.7 Block-Diagram Phase Step 7 in Figure 1 Involves the preparation of a model for controls-oriented analysis and observer development. The equations of Step 6 are transformed into block diagram form. It should be noted that despite its graphical nature, the block diagram is not effective for conveying the physics of a plant at the concept level, as evidenced by its difference in appearance to Steps 2 and 4.
3.2.8 Model-fitting and Analysis Phase Step 8 in Figure 1 represents the first introduction of detailed quantitative analysis (other than "ball-parking" exercises). The analysis phase represents the majority of modeling work, consisting of activities such as test design, data gathering, model-fitting, sensitivity analysis, model reduction proposals, detailed specifications, and formal model validation. Since design is iterative in nature, earlier design phases can, and should be revisited and refined due to the availability of quantitative information. After Step 8 has reached satisfactory completion, a plant model is ready for software application in the form of an 136