Abstract
A novel adaptive data driven control strategy is proposed for general discrete non-linear systems. The controller is designed based upon the Simultaneous Perturbation Stochastic Approximation (SPSA) method, and is constructed through use of a Function Approximator (FA), which is fixed as a neural network here. In this novel control strategy, the parametric estimation is designed to be adaptive with convergence analysis, and the control ability has been greatly improved. The proposed control method is finally applied into the non-linear tracking problems, as well as near-optimal control problems for discrete-time non-linear systems. Simulation comparison tests were conducted on typical non-linear plants, through which, the convergence and feasibility of the proposed adaptive data driven control strategy are well demonstrated.
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