Nonfragile Prescribed Performance Control of Robot Manipulators With Actuator Faults

This paper proposes a nonfragile prescribed performance control (PPC) scheme for robot manipulators with actuator faults, which can address the fragility problem of the existing prescribed performance control and guarantee the transient steady-state performance of the tracking error. Firstly, a novel performance function with small overshoot, finite time convergence, and an adjustment term is proposed. Its adjustment term can adjust the constraint range when the error approaches the boundary, thus avoiding the control singularity problem. Then, error transformation is employed to convert the tracking problem with performance constraints into a stabilization problem for the new system. On this basis, fuzzy neural networks are utilized to address the model uncertainty. Stability analysis of the designed controller is conducted utilizing the Lyapunov method. Finally, numerical simulations are employed to verify the effectiveness and superiority of the proposed scheme.