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126 Neural networks (methodologies for process modelling and control)
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Abstracts
Abstracts in this section are from papers presented at: IFAC SYMPOSIUM ON A R T I F I C I A L INTELLIGENCE IN REAL-TIME CONTROL Delft, The Netherlands, 16-18 June 1992 Full papers appear in the Proceedings volume to which the page numbers relate, published by IFAC and available from Pergamon Press. (ISBN: 0 08 041898 8)
120 Knowledge Based Control: Selecting the Right Tool for the Job R. Leitch, pp 1-10 This paper proposes a classification of system models in terms of their knowledge classes and characteristics, and relates these to existing approaches to the use of AI methods in control. Such a classification is a necessary precursor to developing a methodological approach to identifying the most appropriate technique (tool) for a given generic class of applications (job).
121 The Functional Link Net Approach to the Learning of Real-Time Optimal Control Yoh-Han Pao, pp 11-18 The paper presents a strategy for learning optimal control. The approach uses functional-link neural network implementations which have several beneficial properties giving advantages over the more-common generalized delta rule implementations. The learning task is decomposed into three parts: identification and monitoring, one-step-ahead control generation and control path optimization. Each of these parts is accomplished with its own functional-link net and these are coordinated to provide the real-time learning of the optimal control path.
122 Neural Networks Applied to Optimal Flight Control T. McKelvey, pp 19-24 This paper presents a method for developing control laws for nonlinear systems based on an optimal control formulation. Due to the nonlinearities of the system, no analytical solution exists. The method proposed here uses the "black box" structure of a neural network to model a feedback control law. The network is trained with the back-propagation learning method by using examples of optimal control produced with a differential dynamic programming technique. Two different optimal control problems from flight control are studied. The produced control laws are simulated and the results analyzed. Neural networks show promise for application to optimal control problems with nonlinear systems.
123 Adaptive Neural Network Control of FESInduced Cyclical Lower Leg Movements S.H. Stroeve, H.M. Franken, P.H. Veltink, W.T.C. van Luenen, pp 25-30 As a first step to controlling paraplegic gait by functional electrical stimulation (FES), the control of the swinging lower leg is studied. This paper deals with a neural control system developed for this case, and tested for a
model using computer simulations. Performance with random initial weights was poor after training with back propagation through time (B'l'l') and fair after supervised learning (SL). B'I'I" training with weights initialized by SL achieved good control, and thus improved the performance of the controller initially trained by SL. An adaptive neural control system based on B T r has been proposed and partially tested. The controller adapted relatively fast to an important model parameter change.
124 Regularization as a Substitute for Pre-Processing of Data in Neural Network Training J. Sj6berg, pp 31-36 The great importance of pre-processing of data before the training of a feedforward network is emphasised by many researchers. This pre-processing is not always straightforward, and, further, the need for pre-processing makes the model "less black". The authors show that regularization, besides its other positive effects, reduces the need for pre-processing.
125 Neural Network Modelling and Control of a Plant Exhibiting the Jump Phenomena G. Lightbody, G. Irwin, pp 37-42 This paper focuses primarily on the modelling and control of nonlinear systems that exhibit gain discontinuities in their frequency plots. Structures and learning algorithms for neural-network-based nonlinear modelling are introduced and applied to the modelling and k-step ahead prediction of an example system. A nonlinear Internal Model Controller (IMC) is developed, based on the ability of the feedforward neural network to form nonlinear forward and inverse models. The results of simulation studies are given in each case.
126 Neural Networks (Methodologies for Process Modelling and Control) G.A. Montague, A.J. Morris, M.J. Willis, pp 43-48 There are strong relationships between Artificial Neural Network and Radial Basis Function approaches to system modelling and representation. Indeed, the RBF representation can be implemented in the form of a twolayered network. This paper reviews the contributions that these two approaches can make to process modelling and control. The development of dynamic system representations is then examined in order to provide a basis for predictive control. Two alternative network modelling philosophies are considered: a time series approach and a network structure with embedded dynamics. Potential applications of the methods discussed are highlighted through studies of typical industrial process-control problems.
Abstracts
Abstracts in this section are from papers presented at: IFAC SYMPOSIUM ON A R T I F I C I A L INTELLIGENCE IN REAL-TIME CONTROL Delft, The Netherlands, 16-18 June 1992 Full papers appear in the Proceedings volume to which the page numbers relate, published by IFAC and available from Pergamon Press. (ISBN: 0 08 041898 8)
120 Knowledge Based Control: Selecting the Right Tool for the Job R. Leitch, pp 1-10 This paper proposes a classification of system models in terms of their knowledge classes and characteristics, and relates these to existing approaches to the use of AI methods in control. Such a classification is a necessary precursor to developing a methodological approach to identifying the most appropriate technique (tool) for a given generic class of applications (job).
121 The Functional Link Net Approach to the Learning of Real-Time Optimal Control Yoh-Han Pao, pp 11-18 The paper presents a strategy for learning optimal control. The approach uses functional-link neural network implementations which have several beneficial properties giving advantages over the more-common generalized delta rule implementations. The learning task is decomposed into three parts: identification and monitoring, one-step-ahead control generation and control path optimization. Each of these parts is accomplished with its own functional-link net and these are coordinated to provide the real-time learning of the optimal control path.
122 Neural Networks Applied to Optimal Flight Control T. McKelvey, pp 19-24 This paper presents a method for developing control laws for nonlinear systems based on an optimal control formulation. Due to the nonlinearities of the system, no analytical solution exists. The method proposed here uses the "black box" structure of a neural network to model a feedback control law. The network is trained with the back-propagation learning method by using examples of optimal control produced with a differential dynamic programming technique. Two different optimal control problems from flight control are studied. The produced control laws are simulated and the results analyzed. Neural networks show promise for application to optimal control problems with nonlinear systems.
123 Adaptive Neural Network Control of FESInduced Cyclical Lower Leg Movements S.H. Stroeve, H.M. Franken, P.H. Veltink, W.T.C. van Luenen, pp 25-30 As a first step to controlling paraplegic gait by functional electrical stimulation (FES), the control of the swinging lower leg is studied. This paper deals with a neural control system developed for this case, and tested for a
model using computer simulations. Performance with random initial weights was poor after training with back propagation through time (B'l'l') and fair after supervised learning (SL). B'I'I" training with weights initialized by SL achieved good control, and thus improved the performance of the controller initially trained by SL. An adaptive neural control system based on B T r has been proposed and partially tested. The controller adapted relatively fast to an important model parameter change.
124 Regularization as a Substitute for Pre-Processing of Data in Neural Network Training J. Sj6berg, pp 31-36 The great importance of pre-processing of data before the training of a feedforward network is emphasised by many researchers. This pre-processing is not always straightforward, and, further, the need for pre-processing makes the model "less black". The authors show that regularization, besides its other positive effects, reduces the need for pre-processing.
125 Neural Network Modelling and Control of a Plant Exhibiting the Jump Phenomena G. Lightbody, G. Irwin, pp 37-42 This paper focuses primarily on the modelling and control of nonlinear systems that exhibit gain discontinuities in their frequency plots. Structures and learning algorithms for neural-network-based nonlinear modelling are introduced and applied to the modelling and k-step ahead prediction of an example system. A nonlinear Internal Model Controller (IMC) is developed, based on the ability of the feedforward neural network to form nonlinear forward and inverse models. The results of simulation studies are given in each case.
126 Neural Networks (Methodologies for Process Modelling and Control) G.A. Montague, A.J. Morris, M.J. Willis, pp 43-48 There are strong relationships between Artificial Neural Network and Radial Basis Function approaches to system modelling and representation. Indeed, the RBF representation can be implemented in the form of a twolayered network. This paper reviews the contributions that these two approaches can make to process modelling and control. The development of dynamic system representations is then examined in order to provide a basis for predictive control. Two alternative network modelling philosophies are considered: a time series approach and a network structure with embedded dynamics. Potential applications of the methods discussed are highlighted through studies of typical industrial process-control problems.