基于记忆的基于Lyapunov环的汽车座椅悬架系统故障采样控制方案

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Arulselvan Srinidhi, R. Raja, Jehad Alzabut, O. Bagdasar
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引用次数: 0

摘要

采用基于记忆的采样数据控制(MSDC)策略,结合李雅普诺夫循环函数,研究了执行器故障情况下汽车座椅悬架系统的控制问题。虽然以前的研究主要集中在悬架控制或执行机构容错上,但很少有人将基于记忆的控制与专门针对汽车座椅悬架系统(VSSS)的容错策略结合起来。本研究通过构建三自由度(DOF)座椅悬架模型和设计一种新的依赖于记忆的采样数据控制器来填补这一空白。该方法通过引入记忆参数,并通过H∞$$ {H}_{\infty } $$控制理论处理扰动衰减和输出约束,提高了稳定性和振动控制。在各种道路条件下验证的仿真结果显示,在乘坐舒适性、道路持稳性和车辆操控性方面有显著改善,优于传统的控制策略,而且均方根(RMS)值也表明振动显著减少。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Memory-Based Sampled-Data Control Scheme for Vehicle Seat Suspension System With Actuator Faults via a Looped Lyapunov Approach

This paper addresses the problem of controlling the Vehicle Seat Suspension System (VSSS) under actuator faults using a memory-based sampled-data control (MSDC) strategy, incorporating looped Lyapunov functions. While previous works have focused on suspension control or actuator fault tolerance individually, few have combined memory-based control with fault-tolerant strategies specifically for Vehicle Seat Suspension Systems (VSSS). This study fills this gap by constructing a 3-Degrees of Freedom (DOF) seat suspension model and designing a novel memory-dependent sampled-data controller. The proposed method improves stability and vibration control by incorporating memory parameters and handling disturbance attenuation and output constraints through H $$ {H}_{\infty } $$ control theory. Simulation results, validated under various road conditions, show significant improvements in ride comfort, road holding, and vehicle handling, outperforming traditional control strategies and also the Root Mean Square (RMS) values indicate significant reduction in vibration.

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来源期刊
International Journal of Robust and Nonlinear Control
International Journal of Robust and Nonlinear Control 工程技术-工程:电子与电气
CiteScore
6.70
自引率
20.50%
发文量
505
审稿时长
2.7 months
期刊介绍: Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.
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