Model-free fractional-order adaptive prescribed performance control for mechatronic systems with actuator failure

Abstract In this paper, a model-free fractional-order adaptive prescribed performance controller (MFF-APPC) is designed for mechatronic systems in presence of uncertainties, external disturbances and actuator failure. The MFF-APPC is composed of ultra-local model (ULM)-based time-delay estimation (TDE), Proportional-Differential (PD) controller and fractional-order adaptive prescribed performance compensator (FAPPC). The ULM is utilized to approximate the complex mechatronic system in an extra-short sliding time window and TDE is employed to observe and eliminate the lumped disturbance. Then, PD controller is designed to stabilize the closed-loop system. Correspondingly, the TDE-based PD (TDE-PD) controller is constructed. However, there always exists estimation error under TDE technique and the tracking performance cannot be guaranteed. Hence, the FAPPC is designed and introduced into TDE-PD controller to eliminate the effect of estimation error on control performance and constrain the tracking error with prescribed convergence time and precision. Moreover, an adaptive parameter is designed to approximate the upper bound of estimation error in TDE and thus reduces the unwanted chattering on the switching manifold. Correspondingly, the MFF-APPC is constructed. Furthermore, the stability of closed-loop system under the proposed MFF-APPC is analyzed by using Lyapunov theorem. To validate the proposed method, the numerical simulation on 2-DOF robotic manipulator and co-simulation on virtual prototype of 7-DOF iReHave upper limb exoskeleton are completed. Through the compared simulation results with supervising switching technique based adaptive model-free controller (SST-AMFC) and robust fault tolerant controller (RFTC), the proposed strategy shows better trajectory tracking with prescribed performance.