Event-Triggered Robust Tracking Control for Robotic Manipulators with Uncertain Dynamics: A Finite-Time Adaptive Neural Learning Method
摘要
This paper proposes a finite-time adaptive neural learning-based event-triggered robust tracking control for robotic manipulators with uncertain dynamics. First, a nominal system is introduced, and an appropriate cost function is designed to successfully transform the original robust control problem into an optimal control problem for the nominal system. Meanwhile, the optimal controller is synergistically designed with an event-triggered mechanism, significantly reducing the computational resources required for the robotic system. Next, a novel parameter update law is developed using adaptive control techniques to ensure the finite-time convergence of the neural network. Rigorous stability analysis proves the finite-time convergence of the parameters and the uniform ultimate boundedness of the tracking error. Finally, the effectiveness of the proposed control algorithm is validated through simulation experiments.