Adaptive Neural Learning Prescribed-Time Control for Teleoperation Systems With Output Constraints

In this paper, the control performance of the teleoperation system subjected to dynamics uncertainty and external disturbance is investigated. To improve control performance, an adaptive neural learning prescribed-time controller was developed, which ensures that the system’s output tracks the desired trajectory with a predetermined accuracy within a user-defined time. Unlike other general finite-time or fixed-time controllers, the predetermined convergence time can be exactly obtained rather than approximated. Moreover, the proposed control scheme can solve the issue with and without constraints uniformly. With the aid of the Lyapunov method, the stability of the system is analyzed. Finally, the effectiveness of the proposed method is further verified by numerical simulations.