LAUSR

Abstract This paper focuses on the problem of a class of nonlinear stochastic switched non-lower triangular systems with input saturation. A novel adaptive neural tracking controller is developed by constructing the appropriately common Lyapunov function and applying backstepping technique. The difficulties in the design process are how to deal with the non-lower triangular structure and input saturation. In response to these questions, the variable separation technique is used to address the problem of non-lower triangular structure and the input saturation function is approximated by the efficient dynamical system. Anything else, neural networks, as universal function approximators, are employed to estimate the unknown continuous functions. Finally, it is shown that all signals in the resulting closed-loop system are uniformly bounded and the tracking error converges to a small neighbourhood around zero. In order to highlight the effectiveness of the presented control strategy, two vivid simulation examples are presented at the end.