Journal ArticleDOI
Neural networks for nonlinear internal model control
Kenneth J. Hunt,D. Sbarbaro +1 more
- Vol. 138, Iss: 5, pp 431-438
Reads0
Chats0
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
More filters
Proceedings ArticleDOI
A neural vector control for induction machine
TL;DR: In this article, a neural network scheme was proposed for field orientation and torque control of the induction machine, which was derived using a gain scheduling technique, by means of a discrete time-variant model that was obtained for several operating conditions.
Journal ArticleDOI
Experimental Application of Partitioned Model-Based Control to pH Neutralization
TL;DR: In this article, a nonlinear control law is applied to an experimental pH neutralization process and a partitioned model is used to capture the dynamic behavior of the process, consisting of a linear ARX model and a non-linear neural network model.
Journal Article
Neural Network Control for Teaching Purposes
TL;DR: The comparison of four control schemes based on neural networks for teaching purposes is studied, based on computer simulations of linear and nonlinear single-input single-output (SISO) typical plants, to teach different aspects of neural control.
Book ChapterDOI
Artificial Neural Networks
TL;DR: This chapter dedicated to artificial neural networks, which were considered a promising approach to find good learning algorithms to solve practical application problems in the early days of artificial intelligence (AI).
References
More filters
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.