Journal ArticleDOI
Neural networks for nonlinear internal model control
Kenneth J. Hunt,D. Sbarbaro +1 more
- Vol. 138, Iss: 5, pp 431-438
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TLDR
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.read more
Citations
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Proceedings ArticleDOI
Internal model control based on RBF neural network inverse system decoupling in a 3-DOf helicopter system
TL;DR: An internal control strategy based on RBF neural network inverse system decoupling for helicopter process is proposed and the simulation shows that this strategy is very validity in tracking control of the 3-DOF helicopter system.
Journal ArticleDOI
Additive feedforward control with neural networks
TL;DR: In this article, the authors demonstrate a method to control a nonlinear, multivariable, noisy process using trained neural networks, where the trained neural network controller acting as the inverse process model is placed in a supplementary pure feedforward path to an existing feedback controller.
Journal ArticleDOI
Properties of the Neural Network Internal Model Controller
TL;DR: In this article, a nonlinear internal model controller (IMC), realized with a multilayer perceptron neural network, is studied, and the neural networks for the process model and the resulting controller are identified using the Recursive Prediction Error (RPE) method with the applied gradient calculation procedure.
Journal ArticleDOI
Control of MIMO nonlinear systems: A data-driven model inversion approach
Carlo Novara,Mario Milanese +1 more
TL;DR: The main features of the NIC approach can be summarized as follows: it does not require a physical model of the plant to control which, in many real-world situations, may be difficult to derive; it can guarantee a priori properties such as closed-loop stability and tracking error accuracy; it is general, numerically efficient and relatively simple.
Book ChapterDOI
An Adaptive Internal Model Control Based on LS-SVM
Changyin Sun,Jinya Song +1 more
TL;DR: LS-SVM regression based adaptive internal model control is used to control a benchmark nonlinear system and results show that the controller has simple structure, good control performance and robustness.
References
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Journal ArticleDOI
Approximation by superpositions of a sigmoidal function
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.
Book
Feedback Systems: Input-output Properties
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.
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Robust process control
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.
Journal ArticleDOI
Internal model control. A unifying review and some new results
Carlos E. García,Manfred Morari +1 more
Journal ArticleDOI
A multilayered neural network controller
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.