Journal ArticleDOI
Nonlinear control structures based on embedded neural system models
Gordon Lightbody,George W. Irwin +1 more
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TLDR
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.read more
Citations
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Journal ArticleDOI
Neural network based model predictive control for a steel pickling process
TL;DR: In this paper, a multi-layer feed-forward neural network model based predictive control scheme is developed for a multivariable nonlinear steel pickling process in acid baths, where three variables under controlled are the hydrochloric acid concentrations.
Journal ArticleDOI
Implementation of neural network predictive control to a multivariable chemical reactor
Dingli Yu,J.B. Gomm +1 more
TL;DR: Implementation of a neural network model-based predictive control scheme to a laboratory-scaled multivariable chemical reactor is described in this paper.
Journal ArticleDOI
An approximate internal model-based neural control for unknown nonlinear discrete processes
Han-Xiong Li,Hua Deng +1 more
TL;DR: An approximate internal model-based neural control (AIMNC) strategy is proposed for unknown nonaffine nonlinear discrete processes under disturbed environment and can be used for open-loop unstable nonlinear processes or a class of systems with unstable zero dynamics.
Journal ArticleDOI
Nonlinear system identification: From multiple-model networks to Gaussian processes
TL;DR: This paper examines the evolution from the radial basis function network to the local model network and finally to the Gaussian process model.
Journal ArticleDOI
Nonlinear internal model control: application of inverse model based fuzzy control
TL;DR: This paper investigates the possible applications of dynamical fuzzy systems to control nonlinear plants with asymptotically stable zero dynamics using a fuzzy nonlinear internal model control strategy that consists in including a dynamical Takagi-Sugeno fuzzy model of the plant within the control structure.
References
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Adaptive Control
TL;DR: Benefiting from the feedback of users who are familiar with the first edition, the material has been reorganized and rewritten, giving a more balanced and teachable presentation of fundamentals and applications.
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A learning algorithm for continually running fully recurrent neural networks
Ronald J. Williams,David Zipser +1 more
TL;DR: The exact form of a gradient-following learning algorithm for completely recurrent networks running in continually sampled time is derived and used as the basis for practical algorithms for temporal supervised learning tasks.
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Graham C. Goodwin,Kwai Sang Sin +1 more
TL;DR: This unified survey focuses on linear discrete-time systems and explores the natural extensions to nonlinear systems and summarizes the theoretical and practical aspects of a large class of adaptive algorithms.