Research on Event-Triggered Adaptive Fuzzy Active Suspension Control Strategy

Abstract

With the continuous iteration and upgrade of intelligent vehicles, the active suspension system (ASS) has garnered increasing attention. The uncertainties in parameter and unknown disturbances present within the ASS pose new challenges for the precise control of the sprung mass. Additionally, as the number of vehicle controllers increases, the utilization of communication resources is becoming more important. To this end, this paper proposes an event-triggered adaptive fuzzy control strategy that achieves optimised control by identifying unknown parameters, compensating for time-varying disturbances, and conserving communication resources. Firstly, by combining adaptive control with fuzzy logic control (FLC), a parameter update law and control laws are designed to achieve online updating of unknown parameter and disturbance compensation, thereby enhancing the control accuracy and robustness of the ASS. Secondly, a relative threshold adaptive fuzzy control (RTAFC) strategy is further designed based on an event-triggered mechanism. This effectively enhances control accuracy and reduces the communication burden, while also avoiding Zeno behavior under the premise of ensuring stability. Finally, simulation verification is performed using MATLAB/Simulink, and further hardware-in-the-loop (HIL) testing is performed on the Quanser platform. Simulation and HIL test results demonstrate that the proposed control strategy achieves a 10% and 40% improvement in control accuracy compared to fixed-threshold adaptive fuzzy control (FTAFC) and model reference adaptive control (MRAC), respectively, and effectively reduces communication burden. Note to Practitioners—The control strategy proposed in this paper aims at achieving vehicle active suspension performance control and conserving communication resources under complex road surface input conditions. With the increasing popularity of ASS, developing controllers that can balance driver comfort, vehicle handling performance and communication resource utilisation has become important. For nonlinear system models, we have designed an adaptive control strategy based on the backstepping approach, which balances the robustness and coupling between subsystems. Meanwhile, combining FLC with adaptive control reduces the computational complexity of the controller and improves control performance. Additionally, the RTAFC control strategy, designed with event-triggered control (ETC) integration, can effectively reduce the communication burden while ensuring control effectiveness. On this basis, simulation and HIL testing have validated that this strategy can flexibly adapt to complex and variable operating conditions.