Predictive neuro-control of uncertain systems: design and use of a neuro-optimizer |
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Authors: | Jean-Pierre Vila [Author Vitae],Vé rè ne Wagner [Author Vitae] |
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Affiliation: | Laboratoire d'Analyse des Systèmes et de Biométrie, INRA-ENSAM, 2 Place P. Viala, 34060 Montpellier, France |
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Abstract: | We consider the problem of predictive control of uncertain stochastic discrete I/O systems. Given a model identification procedure able to give accurate output system estimates, e.g. a neural network approximation, we use another feedforward neural network to generate at each time step a constrained optimal control. Dynamic backpropagation is used to improve when necessary the controller network parameters. Both system and controller neural structures are first selected off-line by a statistical Bayesian procedure in order to make the predictive control minimizing process more efficient. The issue of stochastic stability of the closed-loop is considered. We developed this approach for the tracking control of such uncertain systems as biotechnological processes. Actual and simulated predictive neuro-control case studies in this field of application are proposed as illustrations. A comparison with a more classic quasi-Newton-based approach is also proposed, showing the interest of this neuro-control approach. |
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Keywords: | Predictive control Uncertain systems Neural network Bayesian selection |
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