Adaptive neural tracking control for nonstrict-feedback nonlinear systems with unknown backlash-like hysteresis and unknown control directions

In this paper, a neural network-based adaptive tracking control problem is investigated for a class of nonstrict-feedback nonlinear systems in the presence of unknown backlash-like hysteresis nonlinearity, unmodeled dynamics, and unknown control directions. A state feedback controller is developed for the considered system by applying the adaptive backstepping technique and neural networks. The design difficulties exhibited in this paper due to unmodeled dynamics, unknown control directions, and nonstrict-feedback form are handled by resorting to a dynamic signal, a Nussbaum function, and the variable separation approach, respectively. It is shown that the designed adaptive controller can guarantee that all the signals remain bounded and that the desired signal can be tracked with a small domain of the origin. A numerical example and an example of a real plant for a one-link manipulator are provided to show the feasibility of the newly designed controller scheme.