Prescribed-time adaptive event-triggered control for robot manipulators based on command filtering

This article investigates the prescribed-time tracking control of uncertain manipulator systems with external disturbances. Combining with command filtering and neural network techniques, a novel prescribed-time adaptive event-triggered control scheme is proposed, where neural networks are utilized to handle manipulators model uncertainties. Based on a piecewise function, a sufficient condition for prescribed-time stability is provided, which decouples the convergence domain and the settling time into separately preset parameters. The proposed approach not only avoid the "explosion of complexity" problem, but also deal with filter errors by using an error compensation strategy. In addition, an adaptive estimation strategy is developed to compensate for external disturbances, and an event trigger mechanism is introduced to save system communication resources. Finally, the superiority of our proposed prescribed-time control approach is demonstrated via simulation results.