基于储层计算预测跳跃四分之一车模型中的非线性振荡

中文摘要 AI 辅助

车辆动力学的可靠预测对于自动驾驶控制和高级驾驶辅助系统等智能驾驶应用至关重要。用于农业和建筑环境的越野车辆尤其容易出现非线性行为，包括由于轮胎-路面接触的间歇性丧失而产生的分岔和混沌运动。预测此类动力学具有挑战性，因为它需要同时解析平滑非线性和与接触丧失相关的不连续切换。在本工作中，我们研究了储层计算（RC）——具体为回声状态网络（ESN）——用于数据驱动预测跳跃四分之一车模型的可行性。储层在少量点的时间序列数据上进行训练，并评估其重建分岔图、相空间吸引子以及周期和混沌状态下的时间轨迹的能力。训练后的储层定性地再现了倍周期通向混沌的路径，并捕捉了周期和混沌吸引子的几何结构。这些结果表明，储层计算是实际非光滑车辆系统中非线性动力学的一种可行的数据驱动预测器。

英文摘要

Reliable prediction of vehicle dynamics is essential for smart driving applications such as autonomous control and advanced driver-assistance systems. Off-road vehicles used in agricultural and construction settings are particularly prone to nonlinear behavior, including bifurcations and chaotic motion arising from intermittent loss of tire--road contact. Predicting such dynamics is challenging because it requires resolving both smooth nonlinearities and the discontinuous switching associated with contact loss. In this work, we investigate the feasibility of reservoir computing (RC) -- specifically an echo state network (ESN) -- for data-driven prediction of a jumping quarter-car model. The reservoir is trained on time-series data from a small number of points and evaluated on its ability to reconstruct bifurcation diagrams, phase-space attractors, and time trajectories across periodic and chaotic regimes. The trained reservoir qualitatively reproduces the period-doubling route to chaos, captures the geometric structure of periodic and chaotic attractors. These results demonstrate that reservoir computing is a feasible data-driven predictor of nonlinear dynamics in a practical, non-smooth vehicle system.

发表机构

• Tokyo University of Agriculture and Technology（东京农工大学）
• Amity University Haryana - Gurugram（Amity大学哈里亚纳邦-古鲁格拉姆校区）
• Nagoya Institute of Technology（名古屋工业大学）
• Central University of Rajasthan（中央拉贾斯坦大学）

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