数据驱动的 LSTM 模型和车辆横向运动预测控制

IF 1.6 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Kyeong Hyeon Kim, Cheolmin Jeong, Junghyun Kim, Sanghyuk Lee, Chang Mook Kang
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引用次数: 0

摘要

本文提出了一种利用数据驱动神经网络模型设计模型预测控制(MPC)系统的方法。MPC 系统的性能在很大程度上取决于基础模型的精度。虽然在特定条件下对车辆动态模型进行线性化是可行的,但准确捕捉车辆的非线性动态(如转弯刚度以及轮胎与路面之间的相互作用)仍具有挑战性。为解决这些非线性动力学问题,本研究提出了通过长短期记忆(LSTM)模型估算二阶横向动态车辆模型的方法。与采用标称参数的传统线性化动力学模型相比,利用 LSTM 的数据驱动模型在模拟车辆横向运动方面表现出更高的准确性。最终,这一数据驱动的 LSTM 模型被纳入了 MPC 框架。与使用线性模型进行车辆横向动力学模拟的传统 MPC 系统相比,基于 LSTM 模型的 MPC 在跟踪精度方面表现更佳。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Data-Driven LSTM Model and Predictive Control for Vehicle Lateral Motion

Data-Driven LSTM Model and Predictive Control for Vehicle Lateral Motion

This article proposes a methodology for designing Model Predictive Control (MPC) systems utilizing data-driven neural network model. The performance of MPC systems is highly dependent on the precision of the underlying model. While linearizing a vehicle’s dynamic model is feasible under certain conditions, accurately capturing the vehicle’s nonlinear dynamics, such as cornering stiffness and the interaction between tires and road surface, remains challenging. To address these nonlinear dynamics, this study proposes the estimation of a second-order lateral dynamic vehicle model via Long Short Term Memory (LSTM) model. The data-driven model utilizing LSTM has demonstrated better accuracy in simulating lateral vehicle motion when compared to conventional linearized dynamics model that employs nominal parameters. Ultimately, this data-driven LSTM model was incorporated into an MPC framework. This LSTM model-based MPC revealed enhanced performance in tracking accuracy over traditional MPC systems that utilize linear models for vehicle lateral dynamics.

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来源期刊
Journal of Electrical Engineering & Technology
Journal of Electrical Engineering & Technology ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
4.00
自引率
15.80%
发文量
321
审稿时长
3.8 months
期刊介绍: ournal of Electrical Engineering and Technology (JEET), which is the official publication of the Korean Institute of Electrical Engineers (KIEE) being published bimonthly, released the first issue in March 2006.The journal is open to submission from scholars and experts in the wide areas of electrical engineering technologies. The scope of the journal includes all issues in the field of Electrical Engineering and Technology. Included are techniques for electrical power engineering, electrical machinery and energy conversion systems, electrophysics and applications, information and controls.
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