Adaptive TCS design using immune-WOA with dynamic load clustering for drive wheels under vertical dynamic loads

Abstract

Vehicle traction control is the cornerstone for enhancing driving performance, ensuring road safety, and enabling intelligent driving. For off-road vehicles, variable road roughness imposes varying degrees of vertical dynamic loads on the wheels and affects changes in vehicle traction. Through simulation experiments, this paper found that the slip rate control performance of vehicle traction control systems traditionally designed for static loads exhibited significant variations when dynamic loads were introduced. To address this issue, vertical dynamic wheel load data under different vehicle speeds and road classifications were subjected to clustering, leading to the determination of three as the optimal number of clusters. The integration of the immune whale algorithm for the design of corresponding traction control systems for different dynamic load categories, and their subsequent comparison with traditional controllers, revealed a significant enhancement in slip rate control performance. Under Class I dynamic loads, the control performance on all road surfaces achieved the best results, reaching a maximum of 63.4%. Consideration of the dynamic variations in dynamic loads caused by changes in vehicle speed led to the design of a global controller capable of adapting to different dynamic load conditions, based on the aforementioned classification controller. The final simulation verification confirmed the maintained excellent control performance of this adaptive controller under various operating conditions.