Asymptotic Continuous Sliding Mode Control for Uncertain Robotic Manipulators with Prescribed Performance

In this paper, a prescribed performance continuous sliding mode control strategy is proposed for the trajectory tracking problem of uncertain robotic manipulators. First, a prescribed performance function is introduced, and the constrained tracking error is transformed into an unconstrained form through the error transformation method to meet the prescribed performance requirements. Then, a three-layer feedforward neural network was adopted to approximate the uncertainties present in the system. On this basis, a continuous non-singular terminal sliding mode controller with prescribed performance is designed. The controller guarantees that the tracking error asymptotically converges to zero after entering the predefined small area near the origin. The asymptotic stability of the closed-loop system is proved by Lyapunov functional method. The effectiveness of the developed control strategy is verified through numerical simulation.