Fixed-time prescribed performance tracking control for manipulators against input saturation

In this work, we pay attention to investigating fixed-time trajectory tracking with prescribed performance for a multi-degree-of-freedom manipulator system subjected to unknown dynamics and input saturation. The radial basis function neural network (RBFNN) is applied to online compensate for the unknown dynamics of the system. In order to guarantee the transient and steady-state performance of the trajectory tracking control, a prescribed performance function (PPF) is used to transform the tracking error. Based on the transformed error, a fixed-time auxiliary system is proposed to compensate for the input saturation impact. Using the compensation error, a non-singular terminal sliding surface is designed, and the corresponding fixed-time control scheme is also proposed. By Lyapunov theorem, it is proved that the reaching phase of the sliding manifold can be completed in finite time, and the stability of the closed-loop system is analyzed. Experimental results verify the effectiveness of the proposed method.