基于车辆多体动力学模型的循环神经网络和粒子群优化相结合的侧滑角估计方法

IF 2.6 2区 工程技术 Q2 MECHANICS Multibody System Dynamics Pub Date : 2024-02-19 DOI:10.1007/s11044-024-09973-5
Yu Sun, Yongjun Pan, Ibna Kawsar, Gengxiang Wang, Liang Hou
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引用次数: 0

摘要

车辆的主动安全系统通常依赖于对侧滑角和其他关键信号(如偏航率)的实时监控。由于传感器网络成本高且不切实际,因此无法直接测量车辆侧倾角。车辆侧滑可利用运动学、动力学或机器学习模型以及可用的车辆状态进行估计。本文结合了循环神经网络和粒子群优化(PSO)算法,以准确估计车辆侧倾角。首先,构建车辆动力学模型,对各种驾驶条件和道路环境下的车辆进行动力学模拟,以收集数据。其次,利用获得的车辆状态(包括速度、加速度、偏航率和转向)开发机器学习模型,以估计车辆侧滑角。利用长短期记忆神经网络(LSTM)和双向长短期记忆神经网络(BiLSTM)提出了两种机器学习模型。第三,采用 PSO 算法优化 LSTM 和 BilLSTM 模型的超参数,以提高估计精度。在数据集中加入高斯噪声,以评估估计模型的鲁棒性。结果表明,估计模型能够准确预测车辆的侧滑角。结果的 R^{2} 值大多大于 0.96。PSO 算法可以提高估计精度,其中 PSO-LSTM 模型的性能最好。
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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Combined recurrent neural networks and particle-swarm optimization for sideslip-angle estimation based on a vehicle multibody dynamics model

The active safety system of a vehicle typically relies on real-time monitoring of the sideslip angle and other critical signals, such as the yaw rate. The vehicle sideslip angle cannot be measured directly due to the high cost and impracticality of sensor networks. The vehicle sideslip can be estimated using kinematic, dynamic, or machine-learning models and available vehicle states. This paper combines recurrent neural networks and the particle-swarm optimization (PSO) algorithm to estimate the vehicle sideslip angle accurately. First, a vehicle-dynamics model is constructed to conduct dynamics simulations of vehicles under various driving conditions and road environments for data collection. Secondly, the obtained vehicle states, including velocity, acceleration, yaw rate, and steering, are used to develop machine-learning models that estimate the vehicle sideslip angle. Two machine-learning models are proposed using the long short-term memory neural network (LSTM) and the bidirectional long short-term memory neural network (BiLSTM). Thirdly, the PSO algorithm is employed to optimize the hyperparameters of the LSTM and BilLSTM models for enhanced estimation precision. The Gaussian noise is added to the datasets to evaluate the robustness of the estimation models. The results indicate that the estimation models are capable of accurately predicting the vehicle’s sideslip angle. The \(R^{2}\) values of the results are mostly greater than 0.96. The PSO algorithm can improve estimation precision, and the PSO-LSTM model performs the best.

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来源期刊
CiteScore
6.00
自引率
17.60%
发文量
46
审稿时长
12 months
期刊介绍: The journal Multibody System Dynamics treats theoretical and computational methods in rigid and flexible multibody systems, their application, and the experimental procedures used to validate the theoretical foundations. The research reported addresses computational and experimental aspects and their application to classical and emerging fields in science and technology. Both development and application aspects of multibody dynamics are relevant, in particular in the fields of control, optimization, real-time simulation, parallel computation, workspace and path planning, reliability, and durability. The journal also publishes articles covering application fields such as vehicle dynamics, aerospace technology, robotics and mechatronics, machine dynamics, crashworthiness, biomechanics, artificial intelligence, and system identification if they involve or contribute to the field of Multibody System Dynamics.
期刊最新文献
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