Enhanced Variable Universe Fuzzy Control of Vehicle Active Suspension Based on Adaptive Contracting–Expanding Factors

Ji, Guanggang; Li, Shaohua; Feng, Guizhen; He, Weidong

doi:10.1007/s40815-023-01549-3

Cited by 1 publication

(1 citation statement)

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“…One is to design the controller using an integrated system model that includes actuator dynamics [ 11 ], and the other is to consider system delays during the controller design process. Ji et al [ 12 ] proposed an improved variable universe fuzzy control strategy with real-time adjustment of the contracting–expanding factor parameters to improve the ride comfort of vehicles. Some scholars have proposed the use of delay compensation techniques to reduce or offset the negative effects of delays [ 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Research on Deep Reinforcement Learning Control Algorithm for Active Suspension Considering Uncertain Time Delay

Wang,

Zhao

et al. 2023

Sensors

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The uncertain delay characteristic of actuators is a critical factor that affects the control effectiveness of the active suspension system. Therefore, it is crucial to develop a control algorithm that takes into account this uncertain delay in order to ensure stable control performance. This study presents a novel active suspension control algorithm based on deep reinforcement learning (DRL) that specifically addresses the issue of uncertain delay. In this approach, a twin-delayed deep deterministic policy gradient (TD3) algorithm with system delay is employed to obtain the optimal control policy by iteratively solving the dynamic model of the active suspension system, considering the delay. Furthermore, three different operating conditions were designed for simulation to evaluate the control performance: deterministic delay, semi-regular delay, and uncertain delay. The experimental results demonstrate that the proposed algorithm achieves excellent control performance under various operating conditions. Compared to passive suspension, the optimization of body vertical acceleration is improved by more than 30%, and the proposed algorithm effectively mitigates body vibration in the low frequency range. It consistently maintains a more than 30% improvement in ride comfort optimization even under the most severe operating conditions and at different speeds, demonstrating the algorithm’s potential for practical application.

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