Adaptive sliding model control with non-fragile prescribed performance for uncertain robot manipulators

Most existing control methods are designed for systems without uncertainties or disturbances. To address the uncertainties in the robot manipulator, an adaptive sliding mode control (SMC) strategy utilizing practical fixed-time and non-fragile prescribed performance control (PPC) is designed. We consider both the uncertainties and disturbances on the robot system, compensating for them separately. An adaptive law is introduced to approximate the parameters of the system. Specifically, a readjusted prescribed funnel is introduced to overcome the fragility of traditional PPC. Furthermore, we design a new strategy for the system under bounded uncertainty. Both strategies are proven to ensure that the position tracking error remains within the prescribed performance boundary (PPB) during the entire process. The stability of the robot system is proved using Lyapunov function. Finally, the performance of proposed control strategies is demonstrated by comparative simulations, validating the enhanced performance of the proposed approach in practical applications.