Switching event‐triggered adaptive finite‐time control for nonlinear networked systems

Abstract This paper addresses the adaptive finite‐time tracking control problem for networked systems subject to sensor faults and backlash‐like hysteresis. Firstly, command filters are introduced into the control design process to simplify the design process and relax the assumptions required by the traditional backstepping method. Subsequently, an adaptive neural network‐based compensation controller is developed by integrating radial basis function neural networks (RBF NNs) with the backstepping control approach to effectively compensate for both hysteresis and sensor faults. Moreover, to optimize resource utilization and enhance transmission efficiency, an event‐triggered control scheme with a switching threshold strategy is further considered. The developed control strategy ensures the boundedness of all signals within the closed‐loop system and guarantees that the tracking error converges to a small neighborhood of the origin in finite time. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control scheme.