Event‐triggered‐NN control for nonlinear system with unknown gain and disturbance

Abstract

In this article, the problem of tracking control for nonlinear system with unknown gain and disturbance is studied. First, to compensate for the adverse effects of disturbance, a disturbance observer via sliding‐mode is designed to estimate the disturbance, and the finite‐time convergence of estimation error is ensured under certain conditions. Then, an event‐triggered neural networks controller based on estimated disturbance is presented to achieve tracking control. Among which, the Nussbaum technique is employed to deal with the unknown gain, and the super‐twisting estimator is introduced to estimate the derivative of virtual control input. Then a time‐varying event‐triggered strategy is designed to reduce communication load. Furthermore, the stability analysis shows that the close‐loop signals are bounded, the tracking error converges to a small compact set, and no Zeno phenomenon occurs. Finally, two examples are provided to illustrate the effectiveness of the proposed new design schemes.