- Email: [email protected]
137 Model matching control of unknown nonlinear systems using recurrent neurakl networks
533
A ~ 127
Tracidng and Feedback Information In Hierarchkal Control of Large.Scale Systems A. Sldaoui, Z. Binder, R. Perret, pp $49-$$4
132
A Hierarchical Control Approach for Broodband-ISDN Communication Networks A. Pltsillides, J.F. Lambert, R.E. Warfleld, pp 573-576
This paper presents a synthesis of procedures used in hierarchical o~irni74tion methods for large-scale systems formed by interconnected traits,by means of the tracking approach. A genend decomposition coordination structure, combining various coordination strategies and integra~ng feedback information from the real system, is proposed. The reanhing algorithms, which are highly efficient, user-frieudly, and spplicable to a large class of problems, •re illustrated by numerical examples.
A hierarchical control structure is formulated for B-ISDN. As an example, a distributed optimal control system that dynamically allocates bandwidth to virtual paths in B-ISDN is developed. The fonn of the solution is suitable for use in • nonlinear multilevel control structure, with fast local control using local feedback, and slower high-level coordination. This is • suitable component for incorporation in a broader overallhierarchical structure.
128
133
Direct CharWtedzatlm of DeceutrMia~l Fixed Modes Based on the Notion of Parametrk Sensitivity of Eigeuvalues J.R. Brito de S o n n , pp $.f~-$$9
The notion of the penuuetric semitivity of eigeuvalues is employed in this paper to develop necessary and sufficient conditions for the direct characterization of decentralized fixed modes. It is shown that these modes are completely characterized when: 1) their firat-order sensitivities with respect to all gains of the decentmliz~ controller are equal to zero, and 2) the tint-order derivatives of their associated eigeuveaor with respect to particular gains of the controller are onhogonal to particular rows in the output matrix of the systern (C matrix). 129
Combinatorial Technique for the Design and Synthesis of Large Scale Manufacturing Systems F. Friodler, L.T. Fan, PIP $61-$64
Mmufacturing systems synthesis (MSS) is an essential step in designing any manufacturing system for producing desired products from available raw materials or pieces of stock. Its importance arises from the fact that essentiallyevery induatrial product is manufactured through a network of manufacturing or operating units. Often, a large number of different networks are availaHe; moreover, the pmfitabih'ty of the same product from different networks may vary widely. Methods for general network synthesis problems are unable to solve MSS because of the unique comhinatorial feature of manufacturing systems. The combinatorial part of MSS has been ezamined here for developin8 algorithmic procedures to generate opumal manufacturing systems.
A distributed mathematical model is developed for a hierarchical team coordination problem, examined in an experiment with human teams. The model treats team members as individual decision entities, and captures team coordination as an exchange of decision information. Comparing experimental data and model solutions, three salient trends are that human te.a~s tend to: overcommunicate during coordination (especially under low time pressure), over-cooperate on team tasks of multiple-decisionmaker responsibility, and reduce planning horizons and coordination activities as time pressure increases. These descriptive factors are quantified and incorporated in the model to develop a normativedescriptive model. The model solutions match the experimental data very closely.
134
Sampled.Data Decentralizod Controller Design M.H. Shor, W.R. Perkins, pp .f~.S68
The H.-norm-bounding controller design problem is solved for decentralized sampled-data controllers. The sampled-data controller design problem is posed as the limit of • sequence of two-rate digital controller design problems. Each two-rate controller design bounds the diserete-time H . norm of • system with disturbance and regulated-ontput variables sampled between control sampling imumces. For the limiting controller, the continuous-time H ~ norm is betmded. Analytical expressions •re derived for the controller and observer gains and design equations, and numerical schemes are presented for the solution of the equations. 131
Development of Realtime Cognitive State Estimator
M. Takahashl, M. Kitamura, H. Yeshikawa, pp 569-572 A real-time cognitive state estimator, based on physiological data, is now under development to provide information for the study of Man-Machine Interfaces. The system for off-line estimation that has already been developed is tint described, to outline the authors' basic idea Of cognitive state estimation, based on physiological data. The modification, to develop • real-time system, is then describad in detail, with the emphasis on the realtime data processing and data-transfer method~ogy. The current state of the system development is presented, with the preliminary results of the real-time analysis.
Stabilization and Blocking in State Feedback Control of Discrete Event Systems S. T•kal, T. Ushio, S. Kodama, pp 583-586
In this paper, controlled discrete-event systems modeled by the Rmnadge-Wonham model, with • control spedfication given in terms of admissible states and target states, •re considered. First, an algorithm to compute the minimally restrictive control-invariaut predicate, such that a non-blocking feedback exists, is presented. Next, blocking in the context of the state feedback control problem with blocking is studied. For control-invariant predicates, two performance measures are defined, and techniques for improving these two measures are presented. 135
130
Modeling Team Behavlour: Coordination and Decision Wei-Ping Wang, P.B. Luh, D.L. Kleinman, pp 577-582
Novel Neural Network Models for Optimal Control Problems Cang-Pu Wu, Xlshi Huang, pp 587-590
Novel neural network models (OCNN) •re presented for solving optimal control problems. The features of the networks are as follows: (1) As equality constraints, the system dynamic equations are embedded in OCNN, which overcomes the difficulty caused by the system dynamic equations. (2) Only the control variables •re taken to be the state variables of OCNN; hence, the dimensions of OCNN •re significantly reduced. As • result of the above features, OCNN can greatly speed up the problem solving. Therefore, OCNN has promising appfications to real-time problems.
136
Modeling Dynamic Systems Using Finite Elements C.S. Berger, pp 591.594
A Fmite dement program using simplices has been developed for modeling non-linear dynamic systems. The method is similar to some single-stage neural nets; it uses the fast convergence properties of single-stage nets but is also able to adapt its memory usage to match the complexity of the plant being modelled. 137
Model Matching Control of Unknown Nonlinear Systems Using Recurrent Neural Networks Liang Jin, P.N. Niklforuk, M.M. Gupta, pp 595.602
A scheme of multi-layered recurrent neural networks (MRNNs), discussed in this paper, provides the potential for the learning and
534
Abstracts
control of a general class of mknown discrete-time nonlinear systems which are treated as "black boxes" with multiple inputs and multiple outputs. A model of the MR_NNis described by • set of nonlinear difference equations, and a suitable analysis for the input-output dynamics of the model is performed to obufin the inverse dynamics. An equivalent enntrol concept is introduced to develop a model-based learning control architecture with simultaneous on-line identification and control for an unknown nonlinear plant. The potential of the proposed methods is demonstrated by simulation results.
138
Application of an Expert System to Hot Strip Mill Load Balance Control K. Kurihara, S. Murakaml, H. Umeda, G. Kameyama, pp 603-606
The draft schedule in a hot strip finisher mill is automatically determined by a computer, using a theoretical model. When roiling thin-gauge or hard material, operators must often adjust the load balance because of snaking or fluctuations. A draft-schedule expert system has beam introduced in the finisher mill set-up system at the NKK Fukyama Works No.2 hot strip mill. The operator's knowledge was summarized in the planning to vary the load distribution over the stands according to the rolling schedule, the restriction of main motor current distribution and the determination of roll gap difference. In a feasibility test, the prototype system proved to be 90% applicable to the actual process, with a real-time guidance system.
139
Non-Linear System Identification Using Neural Networks Oriented Speech Liu Huaqlang, DaI Guanzhong, Xu Nalplng, pp 607-610
Speech can be considered as the output of a time-varying nonlinear dynamic system. This paper researches how the signal during one cycle of a fundamental tone of speech is picked up from natural speech and produced by a non-time-varying system, employing the methods and results available from non-linear systems research. The signal during one cycle is regarded as the smallest trait of speech. Many signal-processing methods can easily be used here, and changes of speech are reflected in good time by those of the system panmaeters. Non-linear system identification using nenral methods is employed for drawing system parameters. Speech recognition can currently be achieved in this way.
140
Adaptive Control of Accelerated Cooling Plant Based on Distributed Observer V.I. Utkin, I.M. Kaliko, G. Bartolini, C. Ghlazza, pp 611-614
modelling uncertainties, the repetitive controller's gain is adjusted to reduce the infinite norm of the error in the frequency domain. Secondly, an alternative repetitive control system with higherorder repetitive function is analyzed and designed. Weightings of the higher-order repetitive function are determined such that the infinite norm of the relative error transfer function is minimized. Computer simulation results for a typical disk drive head positioning servo system validate the proposed methods.
142
A combustion engine dynamometer described by ordinary differential equations is identified by least-squares estimation and state variable filters. The estimated parameters of the dynamometer are compared with the parameters achieved from theoretical modelling. Based on the estimated parameters, a disturbance compensation, a PI-torque controller and a state-space controller are designed. The three control algorithms are compared.
143
141
Analysis and Design of Two Types of Digital Repetitive Control Systems Wou-Sok Chang, II Hong Sub, June-Dong Kim, pp 615-620
Two types of linear digital repetitive control systems are designed and analyzed to re.duce the error ~ , including harmonic and non-hanmonic components. In a novel gain-adjusting algorithm for conventional and modified repetitive control systems with
Discretlzatiou and Continualization of MIMO Systems S. Bingulac, H.F. Vanlandingham, pp 625-628
New, numerically robust algorithms are presented for converting linear continuous-time constant-parameter state models into equivalent discrete-time state models (discretization) as well as the reverse problem of determining continuous-time models to represent given discrete-time models (continualization). Two methods of discretizing linear uniformly sampled systems have been considered for their utility in computer-aided design. These methods are the standard zero-order hold method, which assumes that inputs are held constant at their previous sample value for the duration of the sample interval, and a method which assumes that the inputs are linearly interpolated between samples.
144
Parameter Identification and Adaptive Control of Continuous Systems with Zero-Order Hold S. Sagara, Zi-Jiang Yang, K. Wade, T. Tsuji, pp 629-632
This paper discusses the digital implementation techniques of paramemr identification and adaptive control for continuous systems with zero-order hold (ZOH), focusing on the bilinear transformation based on the block pulse functions (BPFs). It is found that the emphasized discreti~-~g method yields excellent performances of parameter identification and adaptive control, even in the case of large sampling intervals.
145 The process of accelerated cooling of stoel plates is under consideration. A control algorithm is developed, taking into account temperature distribution within plate thickness and the essential temperature dependence of the coefficients in the motion equation. The proposed control approach provides precise average temperature tracking in the oomplete information case. The method is based on ordinary differential equations describing the dynamics of the scalar variable under control. For real-life situations with entry and exit surface temperature measurements and uncertain plant parameters, the developed method implies the application of a distributed state observer associated with an adaptation loop.
Parameter Estimation and Digital Control with Continuous-Time Models and Application to a Combustion.Engine Dynamometer R. lsermann, K.U. Voigt, K. Pfeiffer, pp 621-624
Joint State and Parameter Estimation in ContinuousTime MIMO System S. Mukhopadhyay, pp 633-636
This paper considers state estimation in a linear multi-input multioutput (MIMO) system with significant parameter uncertainty. The method proposed is based on parameter estimation in continuous-time models (CTMs). It involves simultaneous recursive estimation of a transformed version of the model parameters, as well as the dynamic state vector. The method is computationaHy simple and suited to real-time implementation. It may be adopted for adaptive control, state estimation of timevarying and nonlinear systems, fault detection and diagnosis, etc.
146
Bias Distribution in Continuous-Time Transfer Function Estimates by Prediction Error Method V.N. Bapat, S. Mukhopadyay, A. Patra, D.C. Saha, pp 637-641
Estimation of simple transfer function models of complex dynamic systems is an important issue for robustness of all subsequent application stages of the models, such as control, fault detection, etc. This paper considers an R L S approach to s-transferfunction
A ~ 127
Tracidng and Feedback Information In Hierarchkal Control of Large.Scale Systems A. Sldaoui, Z. Binder, R. Perret, pp $49-$$4
132
A Hierarchical Control Approach for Broodband-ISDN Communication Networks A. Pltsillides, J.F. Lambert, R.E. Warfleld, pp 573-576
This paper presents a synthesis of procedures used in hierarchical o~irni74tion methods for large-scale systems formed by interconnected traits,by means of the tracking approach. A genend decomposition coordination structure, combining various coordination strategies and integra~ng feedback information from the real system, is proposed. The reanhing algorithms, which are highly efficient, user-frieudly, and spplicable to a large class of problems, •re illustrated by numerical examples.
A hierarchical control structure is formulated for B-ISDN. As an example, a distributed optimal control system that dynamically allocates bandwidth to virtual paths in B-ISDN is developed. The fonn of the solution is suitable for use in • nonlinear multilevel control structure, with fast local control using local feedback, and slower high-level coordination. This is • suitable component for incorporation in a broader overallhierarchical structure.
128
133
Direct CharWtedzatlm of DeceutrMia~l Fixed Modes Based on the Notion of Parametrk Sensitivity of Eigeuvalues J.R. Brito de S o n n , pp $.f~-$$9
The notion of the penuuetric semitivity of eigeuvalues is employed in this paper to develop necessary and sufficient conditions for the direct characterization of decentralized fixed modes. It is shown that these modes are completely characterized when: 1) their firat-order sensitivities with respect to all gains of the decentmliz~ controller are equal to zero, and 2) the tint-order derivatives of their associated eigeuveaor with respect to particular gains of the controller are onhogonal to particular rows in the output matrix of the systern (C matrix). 129
Combinatorial Technique for the Design and Synthesis of Large Scale Manufacturing Systems F. Friodler, L.T. Fan, PIP $61-$64
Mmufacturing systems synthesis (MSS) is an essential step in designing any manufacturing system for producing desired products from available raw materials or pieces of stock. Its importance arises from the fact that essentiallyevery induatrial product is manufactured through a network of manufacturing or operating units. Often, a large number of different networks are availaHe; moreover, the pmfitabih'ty of the same product from different networks may vary widely. Methods for general network synthesis problems are unable to solve MSS because of the unique comhinatorial feature of manufacturing systems. The combinatorial part of MSS has been ezamined here for developin8 algorithmic procedures to generate opumal manufacturing systems.
A distributed mathematical model is developed for a hierarchical team coordination problem, examined in an experiment with human teams. The model treats team members as individual decision entities, and captures team coordination as an exchange of decision information. Comparing experimental data and model solutions, three salient trends are that human te.a~s tend to: overcommunicate during coordination (especially under low time pressure), over-cooperate on team tasks of multiple-decisionmaker responsibility, and reduce planning horizons and coordination activities as time pressure increases. These descriptive factors are quantified and incorporated in the model to develop a normativedescriptive model. The model solutions match the experimental data very closely.
134
Sampled.Data Decentralizod Controller Design M.H. Shor, W.R. Perkins, pp .f~.S68
The H.-norm-bounding controller design problem is solved for decentralized sampled-data controllers. The sampled-data controller design problem is posed as the limit of • sequence of two-rate digital controller design problems. Each two-rate controller design bounds the diserete-time H . norm of • system with disturbance and regulated-ontput variables sampled between control sampling imumces. For the limiting controller, the continuous-time H ~ norm is betmded. Analytical expressions •re derived for the controller and observer gains and design equations, and numerical schemes are presented for the solution of the equations. 131
Development of Realtime Cognitive State Estimator
M. Takahashl, M. Kitamura, H. Yeshikawa, pp 569-572 A real-time cognitive state estimator, based on physiological data, is now under development to provide information for the study of Man-Machine Interfaces. The system for off-line estimation that has already been developed is tint described, to outline the authors' basic idea Of cognitive state estimation, based on physiological data. The modification, to develop • real-time system, is then describad in detail, with the emphasis on the realtime data processing and data-transfer method~ogy. The current state of the system development is presented, with the preliminary results of the real-time analysis.
Stabilization and Blocking in State Feedback Control of Discrete Event Systems S. T•kal, T. Ushio, S. Kodama, pp 583-586
In this paper, controlled discrete-event systems modeled by the Rmnadge-Wonham model, with • control spedfication given in terms of admissible states and target states, •re considered. First, an algorithm to compute the minimally restrictive control-invariaut predicate, such that a non-blocking feedback exists, is presented. Next, blocking in the context of the state feedback control problem with blocking is studied. For control-invariant predicates, two performance measures are defined, and techniques for improving these two measures are presented. 135
130
Modeling Team Behavlour: Coordination and Decision Wei-Ping Wang, P.B. Luh, D.L. Kleinman, pp 577-582
Novel Neural Network Models for Optimal Control Problems Cang-Pu Wu, Xlshi Huang, pp 587-590
Novel neural network models (OCNN) •re presented for solving optimal control problems. The features of the networks are as follows: (1) As equality constraints, the system dynamic equations are embedded in OCNN, which overcomes the difficulty caused by the system dynamic equations. (2) Only the control variables •re taken to be the state variables of OCNN; hence, the dimensions of OCNN •re significantly reduced. As • result of the above features, OCNN can greatly speed up the problem solving. Therefore, OCNN has promising appfications to real-time problems.
136
Modeling Dynamic Systems Using Finite Elements C.S. Berger, pp 591.594
A Fmite dement program using simplices has been developed for modeling non-linear dynamic systems. The method is similar to some single-stage neural nets; it uses the fast convergence properties of single-stage nets but is also able to adapt its memory usage to match the complexity of the plant being modelled. 137
Model Matching Control of Unknown Nonlinear Systems Using Recurrent Neural Networks Liang Jin, P.N. Niklforuk, M.M. Gupta, pp 595.602
A scheme of multi-layered recurrent neural networks (MRNNs), discussed in this paper, provides the potential for the learning and
534
Abstracts
control of a general class of mknown discrete-time nonlinear systems which are treated as "black boxes" with multiple inputs and multiple outputs. A model of the MR_NNis described by • set of nonlinear difference equations, and a suitable analysis for the input-output dynamics of the model is performed to obufin the inverse dynamics. An equivalent enntrol concept is introduced to develop a model-based learning control architecture with simultaneous on-line identification and control for an unknown nonlinear plant. The potential of the proposed methods is demonstrated by simulation results.
138
Application of an Expert System to Hot Strip Mill Load Balance Control K. Kurihara, S. Murakaml, H. Umeda, G. Kameyama, pp 603-606
The draft schedule in a hot strip finisher mill is automatically determined by a computer, using a theoretical model. When roiling thin-gauge or hard material, operators must often adjust the load balance because of snaking or fluctuations. A draft-schedule expert system has beam introduced in the finisher mill set-up system at the NKK Fukyama Works No.2 hot strip mill. The operator's knowledge was summarized in the planning to vary the load distribution over the stands according to the rolling schedule, the restriction of main motor current distribution and the determination of roll gap difference. In a feasibility test, the prototype system proved to be 90% applicable to the actual process, with a real-time guidance system.
139
Non-Linear System Identification Using Neural Networks Oriented Speech Liu Huaqlang, DaI Guanzhong, Xu Nalplng, pp 607-610
Speech can be considered as the output of a time-varying nonlinear dynamic system. This paper researches how the signal during one cycle of a fundamental tone of speech is picked up from natural speech and produced by a non-time-varying system, employing the methods and results available from non-linear systems research. The signal during one cycle is regarded as the smallest trait of speech. Many signal-processing methods can easily be used here, and changes of speech are reflected in good time by those of the system panmaeters. Non-linear system identification using nenral methods is employed for drawing system parameters. Speech recognition can currently be achieved in this way.
140
Adaptive Control of Accelerated Cooling Plant Based on Distributed Observer V.I. Utkin, I.M. Kaliko, G. Bartolini, C. Ghlazza, pp 611-614
modelling uncertainties, the repetitive controller's gain is adjusted to reduce the infinite norm of the error in the frequency domain. Secondly, an alternative repetitive control system with higherorder repetitive function is analyzed and designed. Weightings of the higher-order repetitive function are determined such that the infinite norm of the relative error transfer function is minimized. Computer simulation results for a typical disk drive head positioning servo system validate the proposed methods.
142
A combustion engine dynamometer described by ordinary differential equations is identified by least-squares estimation and state variable filters. The estimated parameters of the dynamometer are compared with the parameters achieved from theoretical modelling. Based on the estimated parameters, a disturbance compensation, a PI-torque controller and a state-space controller are designed. The three control algorithms are compared.
143
141
Analysis and Design of Two Types of Digital Repetitive Control Systems Wou-Sok Chang, II Hong Sub, June-Dong Kim, pp 615-620
Two types of linear digital repetitive control systems are designed and analyzed to re.duce the error ~ , including harmonic and non-hanmonic components. In a novel gain-adjusting algorithm for conventional and modified repetitive control systems with
Discretlzatiou and Continualization of MIMO Systems S. Bingulac, H.F. Vanlandingham, pp 625-628
New, numerically robust algorithms are presented for converting linear continuous-time constant-parameter state models into equivalent discrete-time state models (discretization) as well as the reverse problem of determining continuous-time models to represent given discrete-time models (continualization). Two methods of discretizing linear uniformly sampled systems have been considered for their utility in computer-aided design. These methods are the standard zero-order hold method, which assumes that inputs are held constant at their previous sample value for the duration of the sample interval, and a method which assumes that the inputs are linearly interpolated between samples.
144
Parameter Identification and Adaptive Control of Continuous Systems with Zero-Order Hold S. Sagara, Zi-Jiang Yang, K. Wade, T. Tsuji, pp 629-632
This paper discusses the digital implementation techniques of paramemr identification and adaptive control for continuous systems with zero-order hold (ZOH), focusing on the bilinear transformation based on the block pulse functions (BPFs). It is found that the emphasized discreti~-~g method yields excellent performances of parameter identification and adaptive control, even in the case of large sampling intervals.
145 The process of accelerated cooling of stoel plates is under consideration. A control algorithm is developed, taking into account temperature distribution within plate thickness and the essential temperature dependence of the coefficients in the motion equation. The proposed control approach provides precise average temperature tracking in the oomplete information case. The method is based on ordinary differential equations describing the dynamics of the scalar variable under control. For real-life situations with entry and exit surface temperature measurements and uncertain plant parameters, the developed method implies the application of a distributed state observer associated with an adaptation loop.
Parameter Estimation and Digital Control with Continuous-Time Models and Application to a Combustion.Engine Dynamometer R. lsermann, K.U. Voigt, K. Pfeiffer, pp 621-624
Joint State and Parameter Estimation in ContinuousTime MIMO System S. Mukhopadhyay, pp 633-636
This paper considers state estimation in a linear multi-input multioutput (MIMO) system with significant parameter uncertainty. The method proposed is based on parameter estimation in continuous-time models (CTMs). It involves simultaneous recursive estimation of a transformed version of the model parameters, as well as the dynamic state vector. The method is computationaHy simple and suited to real-time implementation. It may be adopted for adaptive control, state estimation of timevarying and nonlinear systems, fault detection and diagnosis, etc.
146
Bias Distribution in Continuous-Time Transfer Function Estimates by Prediction Error Method V.N. Bapat, S. Mukhopadyay, A. Patra, D.C. Saha, pp 637-641
Estimation of simple transfer function models of complex dynamic systems is an important issue for robustness of all subsequent application stages of the models, such as control, fault detection, etc. This paper considers an R L S approach to s-transferfunction