通过机械数据驱动的主动悬架道路预览控制解锁智能车辆的最佳乘坐舒适性

IF 8 1区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Cheng Wang, Guanyu Tao, Xiaoxian Cui, Quan Yao, Xinran Zhou, Konghui Guo
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引用次数: 0

摘要

乘坐舒适性对智能汽车的采用至关重要,而主动悬架系统是其改进的核心。道路预览感知可以进一步提高悬架性能,但有效地将道路数据整合到控制策略中仍然具有挑战性。本文介绍了一种融合微分几何分析和深度强化学习(DRL)的机制数据驱动的道路预览控制策略DGRL-RP。首先,微分几何阐明了未来道路信息如何影响悬架动力学,从而将其分解为机制和数据驱动模块。在数据驱动模块中,专家引导的软硬模块(通过软硬约束的组合对多尺度状态输入进行规范化并强制执行执行器限制)与双延迟深度确定性策略(TD3-SH)算法和确定性经验跟踪(DET)机制集成在一起,以加速学习和收敛。以人体加速度均方根(RMS)作为性能指标的仿真实验表明,DGRL-RP比模型预测控制(MPC)高71.73%,比TD3-SH高65.39%。此外,它在不同场景下保持90%以上的舒适性优化,表明了优越的控制性能和强泛化。DGRL-RP为驾驶舒适性优化提供了新颖的解决方案,并将主动悬架控制推向更高的智能和精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unlocking optimal ride comfort in intelligent vehicles via mechanism-data-driven active suspension road preview control
Ride comfort is vital for intelligent vehicle adoption, and active suspension systems are central to its improvement. Road preview perception can further enhance suspension performance, yet effectively integrating road data into control strategies remains challenging. This paper introduces DGRL-RP, a mechanism-data-driven road preview control strategy that merges differential geometry analysis with deep reinforcement learning (DRL). First, differential geometry elucidates how future road information influences suspension dynamics, enabling decomposition into mechanism and data-driven modules. In the data-driven module, an expert-guided soft-hard module—which normalizes multi-scale state inputs and enforces actuator limits through combined soft and hard constraints—is integrated with the Twin Delayed Deep Deterministic Policy (TD3-SH) algorithm and a Deterministic Experience Tracking (DET) mechanism to accelerate learning and convergence. Simulation experiments using the root mean square (RMS) of body acceleration as the performance metric demonstrate that DGRL-RP outperforms Model Predictive Control (MPC) by 71.73% and TD3-SH by 65.39%. Moreover, it maintains over 90% comfort optimization across diverse scenarios, illustrating superior control performance and strong generalization. DGRL-RP offers a novel solution for ride comfort optimization and advances active suspension control toward greater intelligence and precision.
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来源期刊
Advanced Engineering Informatics
Advanced Engineering Informatics 工程技术-工程:综合
CiteScore
12.40
自引率
18.20%
发文量
292
审稿时长
45 days
期刊介绍: Advanced Engineering Informatics is an international Journal that solicits research papers with an emphasis on 'knowledge' and 'engineering applications'. The Journal seeks original papers that report progress in applying methods of engineering informatics. These papers should have engineering relevance and help provide a scientific base for more reliable, spontaneous, and creative engineering decision-making. Additionally, papers should demonstrate the science of supporting knowledge-intensive engineering tasks and validate the generality, power, and scalability of new methods through rigorous evaluation, preferably both qualitatively and quantitatively. Abstracting and indexing for Advanced Engineering Informatics include Science Citation Index Expanded, Scopus and INSPEC.
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