Gradient-based adaptive state tracking for non-strict feedback discrete systems with dynamic event-triggering

This paper addresses communication efficiency challenges in discrete-time control systems by proposing a dynamic event-triggered mechanism integrated with gradient-based adaptive control for state tracking. The developed method significantly reduces the communication load while circumventing the difficulties in proving stability inherent in traditional Lyapunov-based adaptive approaches when handling discrete-time adaptive state tracking problems under non-strict feedback structures. Meanwhile, through an input-to-state stability (ISS) analysis, the uniform ultimate boundedness of all signals is rigorously established, ensuring parameter estimation accuracy and tracking performance after introducing the event-triggered mechanism. Comparative results demonstrate that the proposed method effectively reduces the communication frequency and improves communication efficiency while maintaining tracking performance.