Observer-based adaptive prescribed performance tracking control for nonlinear systems with unknown control direction and input saturation

In this paper, the problem of observer-based adaptive tracking control is investigated for a class of nonlinear systems with unknown control direction, input saturation and tracking error constraint. The Nussbaum function is employed to address the unknown control direction and a state observer is constructed by neural networks (NNs) to estimate the unmeasurable states. A new error constraint transformation is proposed to guarantee that the tracking error satisfies the prescribed performance. Then, a novel adaptive prescribed performance neural network (NN) output feedback tracking control method is designed. It is proved that the designed controller can guarantee the boundedness of all the signals in the closed-loop system and the prescribed time-varying tracking performance. Finally, simulations on two examples are performed to illustrate the efficiency of the proposed control method.