Adaptive neural decentralised control for switched interconnected nonlinear systems with backlash-like hysteresis and output constraints

This paper considers the issue of adaptive neural decentralised tracking control for a class of output-constraint switched interconnected nonlinear systems with unknown backlash-like hysteresis control input. First, neural networks (NNs) are applied to approximate unknown nonlinear functions, and an NNs switched state observer is designed to estimate unmeasured system states. Then, the dynamic surface control technique is used to avoid the influence of explosion of complexity. In addition, the problem of output constraints is solved by introducing the barrier Lyapunov functions. Based on the Lyapunov stability theory, all signals in the switched closed-loop system can be verified to be uniformly ultimately bounded under the proposed control method. Moreover, the system output can track the target trajectory well within a small bounded error. Finally, a numerical simulation result is given to illustrate the effectiveness of the adaptive decentralised control scheme.