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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Journal ArticleDOI
Modeling and control of non-linear systems using soft computing techniques
TL;DR: This work will concentrate on the pioneering neuro-fuzzy system, Adaptive Neuro-Fuzzy Inference System (ANFIS), which is first used to model non-linear knee-joint dynamics from recorded clinical data and is then used for the design and evaluation of various intelligent control strategies.
Journal ArticleDOI
Adaptive control of discrete-time nonlinear systems using recurrent neural networks
TL;DR: A novel MRNN structure is proposed to approximate the unknown nonlinear input-output relationship, using a dynamic back propagation (DBP) learning algorithm to synthesise a control technique for model reference control purposes.
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.
Proceedings ArticleDOI
Nonlinear adaptive control using neural networks: estimation with a smoothed form of simultaneous perturbation gradient approximation
James C. Spall,J.A. Cristion +1 more
TL;DR: In this article, the authors consider the problem of developing adaptive controllers for general dynamic systems with unknown governing equations and develop a solution for an important class of such problems, and introduce a modification to simultaneous perturbation stochastic approximation that is based on smoothing gradient approximations across iterations.
References
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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.