Neural Networks Predictive Controller Using an Adaptive Control Rate

Ahmed Mnasser, Faouzi Bouani and Mekki Ksouri
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Ahmed Mnasser: Faculty of Sciences of Tunis, Tunis El Manar University, Tunis, Tunisia
Faouzi Bouani: Analysis, Conception and Control of Systems Laboratory, National Engineering School of Tunis, Tunis El Manar University, Tunis, Tunisia
Mekki Ksouri: Analysis, Conception and Control of Systems Laboratory, National Engineering School of Tunis, Tunis El Manar University, Tunis, Tunisia

International Journal of System Dynamics Applications (IJSDA), 2014, vol. 3, issue 3, 127-147

Abstract: A model predictive control design for nonlinear systems based on artificial neural networks is discussed. The Feedforward neural networks are used to describe the unknown nonlinear dynamics of the real system. The backpropagation algorithm is used, offline, to train the neural networks model. The optimal control actions are computed by solving a nonconvex optimization problem with the gradient method. In gradient method, the steepest descent is a sensible factor for convergence. Then, an adaptive variable control rate based on Lyapunov function candidate and asymptotic convergence of the predictive controller are proposed. The stability of the closed loop system based on the neural model is proved. In order to demonstrate the robustness of the proposed predictive controller under set-point and load disturbance, a simulation example is considered. A comparison of the control performance achieved with a Levenberg-Marquardt method is also provided to illustrate the effectiveness of the proposed controller.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jsda00:v:3:y:2014:i:3:p:127-147

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