Adaptive output feedback reinforcement learning control for continuous time switched stochastic nonlinear systems with unknown control coefficients and full-state constraints

In this paper, the problem of adaptive neural network (NN) reinforcement learning (RL) tracking control is investigated for the continuous time (CT) switched stochastic nonlinear systems with unknown control coefficients and full-state constraints. First, a set of reconstructed states are defined to handle the unknown control coefficients, and switched state observers are developed to estimate unmeasurable reconstructed states. Then, to improve the tracking performance, based on the minimal learning parameter (MLP) method and the RL control design technique, the adaptive RL controller is developed by the backstepping method. Finally, the boundedness of the tracking error and all signals is demonstrated via the average dwell time (ADT) method and tangent type time-varying barrier multiple Lyapunov functions. The effectiveness of the proposed scheme is verified by two examples.