Stochastic learning control for nonlinear systems

A learning control algorithm for complex systems is proposed. This control algorithm is based on: (i) artificial neural network; (ii) a quadratic control criterion. The neural network plays the role of the controller and the weights are adjusted using stochastic approximation techniques. Both unconstrained and constrained control objectives are considered. The Lagrange approach is used to deal with the constrained case problem. This control strategy presents another, characteristic: robustness. It is able to deal with process parameters variation. No process model is used for control purposes. The feasibility and the performance of the control algorithm are illustrated by an example: the control of the level of a conic tank that exhibits a high nonlinearity characteristic