Neural networks for nonlinear internal model control
01 Sep 1991-Vol. 138, Iss: 5, pp 431-438
TL;DR: In this paper, a novel technique, directly using artificial neural networks, is proposed for the adaptive control of nonlinear systems, where the ability of neural networks to model arbitrary nonlinear functions and their inverses is exploited.
Abstract: A novel technique, directly using artificial neural networks, is proposed for the adaptive control of nonlinear systems. The ability of neural networks to model arbitrary nonlinear functions and their inverses is exploited. The use of nonlinear function inverses raises questions of the existence of the inverse operators. These are investigated and results are given characterising the invertibility of a class of nonlinear dynamical systems. The control structure used is internal model control. It is used to directly incorporate networks modelling the plant and its inverse within the control strategy. The potential of the proposed method is demonstrated by an example.
Citations
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TL;DR: In this paper, a nonlinear internal model control (NIMC) strategy based on neural network models is proposed for SISO processes, which includes time delay compensation in the form of a Smith predictor and ensures offset-free performance.
286 citations
Patent•
08 Jan 2002
TL;DR: In this paper, a control network (74) is provided that accurately models the plant (72), and the output of the control network provides a predicted output which is combined with a desired output to generate an error.
Abstract: A plant (72) is operable to receive control inputs c(t) and provide an output y(t). The plant (72) has associated therewith state variables s(t) that are not variable. A control network (74) is provided that accurately models the plant (72). The output of the control network (74) provides a predicted output which is combined with a desired output to generate an error. This error is back propagated through an inverse control network (76), which is the inverse of the control network (74) to generate a control error signal that is input to a distributed control system (73) to vary the control inputs to the plant (72) in order to change the output y(t) to meet the desired output. The control network (74) is comprised of a first network NET 1 that is operable to store a representation of the dependency of the control variables on the state variables. The predicted result is subtracted from the actual state variable input and stored as a residual in a residual layer (102). The output of the residual layer (102) is input to a hidden layer (108) which also receives the control inputs to generate a predicted output in an output layer (106). During back propagation of error, the residual values in the residual layer (102) are latched and only the control inputs allowed to vary.
186 citations
TL;DR: The artificial neural network (ANN) technique is extended to the simulation of the time-dependent behavior of a heat exchanger (HX) and used to control the temperature of air passing over it, which allows the system to reach steady-state operating conditions in regions where the PI and PID controllers are not able to perform as well.
149 citations
TL;DR: It is shown that the design of such nonadaptive indirect control systems necessitates only the training of the inverse of the model deprived from its delay, and that the presence of the delay thus does not increase the order ofThe inverse.
Abstract: We propose a design procedure of neural internal model control systems for stable processes with delay. We show that the design of such nonadaptive indirect control systems necessitates only the training of the inverse of the model deprived from its delay, and that the presence of the delay thus does not increase the order of the inverse. The controller is then obtained by cascading this inverse with a rallying model which imposes the regulation dynamic behavior and ensures the robustness of the stability. A change in the desired regulation dynamic behavior, or an improvement of the stability, can be obtained by simply tuning the rallying model, without retraining the whole model reference controller. The robustness properties of internal model control systems being obtained when the inverse is perfect, we detail the precautions which must be taken for the training of the inverse so that it is accurate in the whole space visited during operation with the process. In the same spirit, we make an emphasis on neural models affine in the control input, whose perfect inverse is derived without training. The control of simulated processes illustrates the proposed design procedure and the properties of the neural internal model control system for processes without and with delay.
147 citations
TL;DR: A novel nonlinear internal model control (IMC) strategy is suggested, that utilizes a nonlinear neural model of the plant to generate parameter estimates over the nonlinear operating region for an adaptive linear internal model, without the problems associated with recursive parameter identification algorithms.
Abstract: This paper investigates in detail the possible application of neural networks to the modeling and adaptive control of nonlinear systems. Nonlinear neural-network-based plant modeling is first discussed, based on the approximation capabilities of the multilayer perceptron. A structure is then proposed to utilize feedforward networks within a direct model reference adaptive control strategy. The difficulties involved in training this network, embedded within the closed-loop are discussed and a novel neural-network-based sensitivity modeling approach proposed to allow for the backpropagation of errors through the plant to the neural controller. Finally, a novel nonlinear internal model control (IMC) strategy is suggested, that utilizes a nonlinear neural model of the plant to generate parameter estimates over the nonlinear operating region for an adaptive linear internal model, without the problems associated with recursive parameter identification algorithms. Unlike other neural IMC approaches the linear control law can then be readily designed. A continuous stirred tank reactor was chosen as a realistic nonlinear case study for the techniques discussed in the paper.
143 citations
References
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TL;DR: It is demonstrated that finite linear combinations of compositions of a fixed, univariate function and a set of affine functionals can uniformly approximate any continuous function ofn real variables with support in the unit hypercube.
Abstract: In this paper we demonstrate that finite linear combinations of compositions of a fixed, univariate function and a set of affine functionals can uniformly approximate any continuous function ofn real variables with support in the unit hypercube; only mild conditions are imposed on the univariate function. Our results settle an open question about representability in the class of single hidden layer neural networks. In particular, we show that arbitrary decision regions can be arbitrarily well approximated by continuous feedforward neural networks with only a single internal, hidden layer and any continuous sigmoidal nonlinearity. The paper discusses approximation properties of other possible types of nonlinearities that might be implemented by artificial neural networks.
12,286 citations
Book•
01 Jan 1975
TL;DR: In this paper, the Bellman-Gronwall Lemma has been applied to the small gain theorem in the context of linear systems and convolutional neural networks, and it has been shown that it can be applied to linear systems.
Abstract: Preface to the Classics edition Preface Acknowledgments Note to the reader List of symbols 1. Memoryless nonlinearities 2. Norms 3. General theorems 4. Linear systems 5. Applications of the small gain theorem 6. Passivity Appendix A. Integrals and series Appendix B. Fourier transforms Appendix C. Convolution Appendix D. Algebras Appendix E. Bellman-Gronwall Lemma References Index.
2,894 citations
Book•
01 Jan 1988
TL;DR: A state-of-the-art study of computerized control of chemical processes used in industry is presented in this article for chemical engineering and industrial chemistry students involved in learning the micro-macro design of chemical process systems.
Abstract: A state-of-the-art study of computerized control of chemical processes used in industry, this book is for chemical engineering and industrial chemistry students involved in learning the micro-macro design of chemical process systems.
2,689 citations
1,373 citations
TL;DR: A modified error-back propagation algorithm, based on propagation of the output error through the plant, is introduced, for learning several learning architectures for training the neural controller to provide the appropriate inputs to the plant.
Abstract: A multilayered neural network processor is used to control a given plant Several learning architectures are proposed for training the neural controller to provide the appropriate inputs to the plant so that a desired response is obtained A modified error-back propagation algorithm, based on propagation of the output error through the plant, is introduced The properties of the proposed architectures are studied through a simulation example >
1,071 citations