Event‐Triggered Tracking Control of Nonlinear Nonparametric Systems for Time‐Varying Reference Signals

In this paper, we investigate the tracking control problem for nonlinear nonparametric systems. An adaptive control strategy is designed incorporating an event‐triggered mechanism, and the average transmission rate of the event detector is obtained. The designed control algorithm is essentially a stochastic approximation algorithm with expanding truncations, in which the inserted event detector decides whether to trigger the update of the control input by setting a threshold value. Under the given assumptions, the proposed control strategy is able to achieve mean tracking performance for both deterministic and stochastic reference signals, which are dealt with in a unified framework for convergence analysis. In addition, the optimality of the proposed control algorithm is established in terms of asymptotic long‐run average of squared tracking error. Finally, the correctness of the theoretical results is verified through numerical simulations.