Neural‐based adaptive event‐triggered tracking control for flexible‐joint robots with random noises
Shuzhen Diao, Wei Sun, Shun‐Feng Su
Abstract
Abstract In this study, a novel adaptive neural network control scheme is proposed to resolve the tracking control problem for flexible‐joint robots with random noises. More precisely, the controlled system in this study is a multi‐input and multi‐output stochastic nonlinear system, employing the traditional backstepping design to study such a system will greatly increase the amount of calculation. To resolve this problem, the command filtered technology is applied to the adaptive neural network design framework. More importantly, with the aid of the event‐triggered strategy, the proposed control algorithm can reduce the communication burden to a certain extent. Besides, the proposed method can also ensure that the tracking error converges to a small neighborhood of the origin. Finally, the simulation example is given to verify the effectiveness of the proposed algorithm.