Improved Switching Event‐Triggered Adaptive Predefined‐Time Tracking Control for Nonlinear Switched Systems

ABSTRACT

This paper proposes an adaptive event‐triggered tracking control scheme for a class of nonlinear switched systems within the predefined time. Neural networks (NNs) are employed to approximate the uncertain functions. Compared with the traditional switching threshold strategy, an improved event‐triggered mechanism is developed to avoid threshold mismatch during dynamic processes while effectively enhancing the system performance. During the controller design process, using a polynomial fitting method in the neighborhood of zero ensures the continuity of the controller and avoids singularity problems. The boundedness of all signals within a predefined time is proven through the construction of a common Lyapunov function via event‐triggered adaptive backstepping control, and the Zeno behavior is excluded. Finally, with the aid of the simulation results, the effectiveness of the designed control technique is shown.