Cooperative control for FWID-EVs with active suspension under extreme conditions by using off-policy safe reinforcement learning

IF 6.6 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

ISA transactions Pub Date : 2026-02-01 Epub Date: 2026-01-07 DOI:10.1016/j.isatra.2026.01.010

Zhijiang Gao, Pak Kin Wong, Fuqiu Zhou, Jing Zhao

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引用次数: 0

Abstract

The unbalanced distribution of tyre normal forces induced by lateral load transfer poses a significant threat to vehicle handling stability under extreme conditions. However, the mitigation of such imbalance is inherently constrained by the suspension support capability. This work proposes a novel off-policy safe reinforcement learning-based cooperative (OSRC) controller that coordinates direct yaw control (DYC) and active suspension system (ASS) to optimize the distribution of tyre normal forces and enhance the handling stability of the four-wheel independent drive electric vehicle (FWID-EV) under extreme conditions. First, a DYC-ASS cooperative architecture is proposed to optimize the distribution of tyre normal forces in the FWID-EV system. Subsequently, a combined tyre-slip-angle and phase-portrait (CTP) method is proposed to characterize the relationship between the tyre force saturation and the lateral stability region (LSR). Based on a two-player game, the OSRC controller is proposed to achieve the cooperation between the DYC and the ASS and enhance the handling stability of the FWID-EV under extreme conditions. Finally, the effectiveness of the proposed OSRC controller is examined via the rapid control prototyping tests.

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基于非策略安全强化学习的fwidd - ev主动悬架极端工况协同控制

侧向载荷传递引起的轮胎法向力的不平衡分布对极端工况下车辆的操纵稳定性构成重大威胁。然而，这种不平衡的缓解受到悬架支撑能力的固有约束。本文提出了一种新型的非策略安全强化学习协同（OSRC）控制器，该控制器协调直接偏航控制（DYC）和主动悬架系统（ASS），以优化轮胎法向力的分布，提高四轮独立驱动电动汽车（FWID-EV）在极端条件下的操纵稳定性。首先，提出DYC-ASS协同架构，优化FWID-EV系统中轮胎法向力的分布；随后，提出了轮胎滑移角与相位画像相结合的方法来表征轮胎力饱和度与横向稳定区（LSR）的关系。为了实现DYC与ASS的协同工作，提高fwidd - ev在极端工况下的操控稳定性，提出了基于二人博弈的OSRC控制器。最后，通过快速控制原型测试验证了所提出的OSRC控制器的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ISA transactions 工程技术-工程：综合

CiteScore

11.70

自引率

12.30%

发文量

824

审稿时长

4.4 months

期刊介绍： ISA Transactions serves as a platform for showcasing advancements in measurement and automation, catering to both industrial practitioners and applied researchers. It covers a wide array of topics within measurement, including sensors, signal processing, data analysis, and fault detection, supported by techniques such as artificial intelligence and communication systems. Automation topics encompass control strategies, modelling, system reliability, and maintenance, alongside optimization and human-machine interaction. The journal targets research and development professionals in control systems, process instrumentation, and automation from academia and industry.