Command-filter-based adaptive neural tracking control for strict-feedback stochastic nonlinear systems with input dead-zone

The problem of command-filter-based adaptive tracking control is investigated for a class of stochastic nonlinear systems with strict-feedback structure with input dead-zone in this paper. Radial basis function neural network (RBF NN) is employed to approximate the packaged unknown nonlinearities. In order to eliminate the influence of ‘the explosion of complexity’ which will exist in the conventional controller design process via backstepping technique, the control method of the command-filter is introduced. For the problem of input dead-zone which appears in the stochastic nonlinear systems, which will be dealt by a reasonable method, namely, the dead-zone nonlinearity can be regarded as a combination for a linear term and bounded disturbance-like term. Combined adaptive backstepping design algorithm and Lyapunov stability theorem, an adaptive neural command-filter controller is developed. The proposed control scheme reduces the calculation burden due to the repeated differentiation for the virtual control laws and guarantees all the closed-loop signals remain semi-globally uniformly ultimately bounded (SGUUB) in the sense of the four moment. And the tracking error converges to a small area near zero. Meanwhile, the effectiveness of the presented approach is proved by simulation results.