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
Control of the penicillin production using fuzzy neural networks
E. Gómez Sánchez,M.J. Arauzo Bravo,J.M. Cano Izquierdo,Yannis Dimitriadis,J. Lopez Coronado,M.J. Lopez Nieto +5 more
TL;DR: Addresses the control of a penicillin fermentation pilot plant using internal model control (IMC) strategies with modules based on a FasArt neuro-fuzzy system, which features fast, stable learning and shows good MIMO identification, suitable for development of the modules in IMC.
Proceedings ArticleDOI
Internal model control based on extreme learning ANFIS for nonlinear application
K V Shihabudheen,G. N. Pillai +1 more
TL;DR: This paper proposes implementation of Non Linear Internal Model Controller (NIMC) using ELANFIS algorithm and shows that proposed controller produces good generalization along with perfect trajectory tracking compared to conventional ANfIS algorithm.
Book ChapterDOI
Control of Robotic Manipulators Using Neural Networks — A Survey
P. Gupta,N. K. Sinha +1 more
TL;DR: The purpose of this chapter is to provide an overview of the research being done in the area of neural network approaches to control of robotic manipulators.
Book ChapterDOI
RBF based induction motor control with a good nonlinearity compensation
TL;DR: The success of the proposed control scheme has been demonstrated by experimental results; induction motor has been able to track the prescribed speed trajectory with rather small errors and good stability under properly loading conditions.
Journal ArticleDOI
A multi-switching mode intelligent hybrid control of electro-hydraulic proportional systems:
Kong Xiangzhen,Hasan Majumdar,Faye Zang,Jiang Shouyong,Wu Qingzhen,Wenjun Zhang,Wenjun Zhang +6 more
TL;DR: There is evidence that the proposed multi-switching mode intelligent hybrid control is very effective and potentially useful to other electro-hydraulic proportional systems.
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.
Book
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.