基于鲁棒控制的汽车悬架系统主动控制

IF 0.5 Q4 AUTOMATION & CONTROL SYSTEMS
Ali Khudhair Al-Jiboory,  Zaid S. Hammoudi
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引用次数: 0

摘要

本文提出了一种新的鲁棒状态反馈\({{\mathcal{H}}_{\infty }}\)控制设计方法,用于主动座椅悬架系统,旨在提高不确定路况下乘客的舒适性。我们的方法最大限度地减少了道路干扰对人体垂直加速度的影响,同时明确考虑了悬架行程偏转的约束,以确保系统的可靠性。我们将悬架系统建模为一个三自由度系统,并根据线性矩阵不等式(lmi)导出了新的综合条件。仿真结果表明,与被动悬架系统和极点放置控制器相比,我们的策略性能优越,最大奇异值的峰值从57.107降低到4.554。此外,我们的控制器将最大控制力从1000 N(对于极点放置控制器)降低到640.5 N,表明提高了能源效率。这些结果突出了乘客舒适度、悬架偏转管理和控制方面的重大改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Active Control of Vehicle Suspension System with Robust Control

Active Control of Vehicle Suspension System with Robust Control

In this paper, we propose a novel robust state-feedback \({{\mathcal{H}}_{\infty }}\) control design method for active seat suspension systems, aiming to enhance passenger comfort in uncertain road conditions. Our approach minimizes the impact of road disturbances on vertical acceleration experienced by the human body, while explicitly considering constraints on suspension stroke deflection to ensure system reliability. We model the suspension system as a three degree-of-freedom (3-DOF) system and derive new synthesis conditions in terms of Linear Matrix Inequalities (LMIs). Simulation results demonstrate the superior performance of our strategy compared to a passive suspension system and a pole-placement controller, achieving a reduction in the peak of the maximum singular value from 57.107 to 4.554. Additionally, our controller reduces the maximum control force from 1000 N (for the pole-placement controller) to 640.5 N, indicating improved energy efficiency. These results highlight significant improvements in passenger comfort, suspension deflection management, and control effort.

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来源期刊
AUTOMATIC CONTROL AND COMPUTER SCIENCES
AUTOMATIC CONTROL AND COMPUTER SCIENCES AUTOMATION & CONTROL SYSTEMS-
CiteScore
1.70
自引率
22.20%
发文量
47
期刊介绍: Automatic Control and Computer Sciences is a peer reviewed journal that publishes articles on• Control systems, cyber-physical system, real-time systems, robotics, smart sensors, embedded intelligence • Network information technologies, information security, statistical methods of data processing, distributed artificial intelligence, complex systems modeling, knowledge representation, processing and management • Signal and image processing, machine learning, machine perception, computer vision
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