Fuzzy prescribed performance tracking control for stochastic Markovian jump nonlinear systems with unmodeled dynamics and actuator faults

Adaptive fuzzy prescribed performance fault-tolerant tracking control for unmodeled stochastic Markovian jump nonlinear systems with actuator failures is studied in this article. As online approximators, fuzzy logic systems (FLSs) are utilized to approximate unknown nonlinear functions and unavailable Markovian switching nonlinearities. To keep the control performance, an ameliorated finite-time prescribed boundary function was incorporated into the controller design, such that the tracking error can be suppressed by the decay boundary function. And the settling time and the size of the prescribed performance set can be arbitrarily predefined according to actual needs. A dynamic signal is employed to tackle the design difficulty of unmodeled dynamics. In view of gain fault and bias fault, an adaptive estimation plan is introduced to estimate the unknown fault parameters online such that the actual controller can be appropriately designed. The formulated fault-tolerant tracking control tactic can keep the boundedness of all signals in spite of Markovian switching, unmodeled dynamics, stochastic disturbances and actuator faults. Finally, the simulations verify the feasibility of the proposed tactic.