LAUSR

This article considers the event-triggered control for the active seat suspension system with time-varying full-state constraints. Consider that communication resources may be limited, this article proposes a dynamic relative threshold strategy to reduce the communication burden of actuator and controller. Compared with the fixed value as a trigger condition, the dynamically changing thresholds as trigger conditions are more general and universal. The time-varying full-state constraint problem is solved by using the barrier Lyapunov function. In addition, the radial basis function neural networks are employed to approximate the unknown terms. Then, all signals in the resulted system are bounded, and the Zeno behavior can be avoided successfully. Moreover, all the system states satisfy their corresponding constraint condition. Finally, the feasibility and rationality of this method are proved by the simulation analysis of a real example of a seat suspension system.