Abstract
AbstractThis article studies the problem of performance‐guaranteed adaptive fuzzy optimal compensator control for stochastic affine nonlinear systems with dead‐zone and unknown nonlinear dynamics. First, by using the error transformation techniques, the original system is transformed into an equivalent unconstraint system. Then, a feedforward fuzzy compensator is constructed to offset the influence raised by unknown dead‐zone. By designing identifier‐critic‐actor construction reinforcement learning, an adaptive fuzzy optimal performance constraint compensate control algorithm is presented. The developed control scheme can ensure that all signals of the controlled system are uniformly ultimately bounded, and the output can track the reference signal with a prescribed accuracy. Finally, simulation results are given to illustrate the effectiveness of the developed control algorithm and theorem.
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