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
Partitioned model-based IMC design using JITL modeling technique
TL;DR: It is shown that PM-IMC using the database-updating JITL is more desirable owing to the relative ease in collecting the process data required to construct its initial database, while achieving comparable control performance as that obtained by PM- IMC using a just-in-time learning method with fixed-database.
Journal ArticleDOI
Robust stability of feedback linearised systems modelled with neural networks: dealing with uncertainty
TL;DR: A systematic procedure to analyse the stability robustness to modelling errors when a neural network model is integrated in an approximate feedback linearisation control scheme is presented, proving robust stability of the overall control system.
Journal ArticleDOI
Learning a Nonlinear Controller From Data: Theory, Computation, and Experimental Results
Lorenzo Fagiano,Carlo Novara +1 more
TL;DR: The problem of learning a nonlinear controller directly from experimental data is considered and a theoretical analysis shows that the error between the input issued by the existing controller and the input given by the learned one shall have low variability in order to achieve closed loop stability.
Journal ArticleDOI
Inversion control of non-linear systems with an inverse NARX model identified using genetic algorithms:
TL;DR: The inverse dynamics approach has been widely utilized in the control problem of various practical non-linear systems in recent years as discussed by the authors, and a feed-forward feedback control scheme has been proposed.
Proceedings ArticleDOI
Nonlinear internal model controller design for wastegate control of a turbocharged gasoline engine
TL;DR: A fourth-order nonlinear model which sufficiently describes the dynamic behavior of the turbocharged engine is implemented to serve as the model in the IMC structure and the controller based on structured quasi-LPV model inverse is designed to achieve boost pressure tracking.
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.