Event‐Triggered Prescribed Tracking for Non‐Affine Systems With Dead‐Zone Input

ABSTRACT In this article, the problem of predefined‐time event‐triggered tracking control for a category of non‐affine systems with dead‐zone input constraints is investigated. First, a model transformation is introduced based on the semi‐bounded conditions, which eliminates the differentiability and boundedness restrictions on the non‐affine functions. By using the transformed model, extended neural networks (NNs) are employed to handle overflow variables arising from the transformation, while simultaneously compensating for approximation errors. Moreover, an adaptive compensation scheme is adopted to estimate unknown parameters in the dead‐zone input. By accounting for the combined effects of dead‐zone input and prescribed performance constraints, an event‐triggered mechanism is constructed that effectively reduces the burden on communication resources. Based on the iterative design, the proposed control scheme ensures that the tracking error converges to a predefined region within a fixed time. Finally, simulation results demonstrate the superior performance over existing methods.