Adaptive neural tracking control for switched stochastic pure-feedback nonlinear systems with backlash-like hysteresis

In this paper, an adaptive neural tracking control approach is proposed for a class of switched stochastic pure-feedback nonlinear systems with backlash-like hysteresis. In the design produce, an affine variable is constructed, which avoids the use of the mean value theorem, and the additional first-order low-pass filter is employed to deal with the problem of explosion of complexity. Then a common Laypunov function (CLF) and a state feedback controller is explicitly obtained for all subsystems. It is proved that the proposed controller guarantees all signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error remains an adjustable neighborhood of the origin.