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ROBUST ADAPTIVE CONTROL USING REINFORCEMENT LEARNING FOR NONLINEAR SYSTEM WITH INPUT CONSTRAINTS

This paper proposes a novel approach to design a controller in discrete time for the class of uncertain nonlinear systems in the presence of magnitude constrains of control signal which are treated as the saturation nonlinearity. A associative law between reinforcement learning algorithm based on adaptive NRBF neural networks and the theory of robust control H is set up in a novel control structure, in which the proposed controller allows learning and control on-line to compensate multiple uncertain nonlinearities as well as minimizing both the H tracking performance index function and the unknown nonlinear dynamic approximation errors. The novel theorem of robust stabilization of the closed-loop system is declared and proved. Simulation results verify the theoretical analysis. ∞