Output Feedback Adaptive Dynamic Surface Control for a Class of Nonlinear Systems With Input Saturation

An output feedback adaptive tracking scheme is proposed for a class of nonlinear systems with parameter uncertainty and input saturation. The unmeasured states are estimated by constructing filters, then dynamic surface control system is designed, which avoids explosion complexity of backstepping control. The effect of actuator saturation on system performance is compensated by neural network. It is shown that the proposed scheme can guarantee semi-global stability of the closed-loop system and the tracking error can be made arbitrarily small.