Event-Triggered Adaptive Finite-Time Control for Active Suspension Systems With Prescribed Performance
Qiang Zeng, Jun Zhao
Abstract
In this article, we address the event-triggered based adaptive finite-time control problem for active suspension systems (ASSs) over the resource-constrained controller area network (CAN), which is the most extensively employed in-vehicle communication network in automotive systems. The control aim is to develop an event-triggered algorithm to reduce the communication burden from the CAN and meanwhile improve the suspension performances. Specifically, a novel finite-time performance function is presented to guarantee that the tracking error is retained in a small region at any prespecified time. It is shown that under the proposed control framework, ride comfort, suspension space limitation, and handling stability are all ensured. Then, the established event-triggered mechanism based on the relative threshold method can avoid Zeno behavior. Finally, all the signals are bounded for the closed-loop ASSs. The usefulness of the presented adaptive control strategy is demonstrated through the simulation results.