Finite-Time Adaptive Fault-Tolerant Control for Robot Manipulators With Guaranteed Transient Performance

Abstract

This article studies finite-time adaptive fault-tolerant control for uncertain robotic manipulator systems with guaranteed transient performance. Combining with backstepping method and neural network techniques, a novel finite-time adaptive fault-tolerant control method is presented, where neural networks are utilized to handle model uncertainties. By introducing an error transformation strategy and a performance function, the transient performance constraints of the system are converted into the stabilization problem of the unconstrained robot manipulator. In addition, adaptive fault-tolerant control weakens the effect of actuator failures on control performance, and a novel adaptive upper bound estimation strategy is adopted to compensate for neural network training errors and external disturbances. Subsequently, finite-time control ensures that the position tracking errors can converge to a small neighborhood around zero within a finite time and guarantees the required tracking performance. Finally, a simulation is conducted based on an actual two-link manipulator model to prove the superiority of our control approach, and the validity of the control approach is further verified on the Franka Emika Panda robot.