Control timeliness, safety, and robustness are critical performance metrics in modern industrial and cooperative robotics, significantly impacting the effectiveness of cooperative tasks. However, external disturbances, actuator faults, and unknown reference input signals can significantly degrade the above critical performance metrics. To address these challenges, this article investigates the human-in-the-loop finite-time practical consensus tracking problem for networked robot manipulators in the presence of external disturbances and actuator faults. In the considered framework, the reference trajectory is generated by a human-in-the-loop system, where the human operator actively regulates the signal via an unknown control input that is inaccessible to all manipulators. To address this challenge, an extended state observer (ESO) is designed to simultaneously estimate the system states and the unknown human control input. Leveraging the observer estimates, an adaptive fault-tolerant control scheme is subsequently developed to guarantee that all manipulators achieve practical consensus tracking, with the tracking errors converging to prescribed residual sets. The proposed approach ensures that both the estimation and tracking errors converge within the finite time. Rigorous Lyapunov-based analysis is conducted to establish the finite-time stability of the closed-loop system. Simulation results are presented to validate the effectiveness and robustness of the proposed control strategy.
Han et al. (Thu,) studied this question.