Stochastic Approximation Based Adaptive Neural Control for a Class of Nonlinear Systems

H. Mekki, M. Chtourou, and N. Derbel


Adaptive control, neural networks, nonlinear system, stochastic


This article presents an adaptive multilayer neural network-based controller that feedback-linearizes the system for a class of single- input single-output (SISO) and multi-input multi-output (MIMO) continuous-time nonlinear systems. Control action is used to achieve tracking performances for state-feedback linearizable unknown non- linear system. The control structure consists of a feedback lineariza- tion portion provided by neural networks (NN). In the standard problem of feedback-based control, the cost to minimize is a func- tion of the output derivatives. When the cost function depends on the output error, the gradient method cannot be applied to adjust the neural network parameters. In this context, the stochastic approximation approach allows computation of the cost function derivatives. In order to show the feasibility and performance of this control scheme, two applications are chosen as nonlinear case studies.

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