Distributed Adaptive Gain-Varying Finite-Time Event-Triggered Control for Multiple Robot Manipulators With Disturbances

Abstract

This article investigates the consensus tracking of uncertain multiple robot manipulators with disturbances, in which a distributed adaptive gain-varying finite-time event-triggered strategy is given. To build the connections between error information and control gains, the well-designed dynamic gain functions are added to the static gains; then, the antidisturbance ability of the networked system is strengthened when there are strong disturbances. The command filters are employed to avoid the direct differentials of virtual controllers, and the compensation strategy with dynamic gains removes the filtering errors compared with traditional backstepping-based algorithms. The event-triggered mechanism is further used to reduce the waste of communication resources by avoiding frequent controller updating for each manipulator in the complex system. Three simulation examples are used to demonstrate its effectiveness.