基于滑模观测器的车辆半主动座椅悬架系统H∞最优控制

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摘要针对考虑人体模型的五自由度半主动座椅悬架系统，提出了一种基于滑模观测器的H∞最优控制器。由于座椅悬架系统的未建模动态、参数不确定性和作动器未知输入在同一个通道内，所有扰动可视为集总干扰。通过结合低通滤波技术构造辅助变量，建立增广系统设计滑模观测器，成功实现了利用直接可测量加速度信息对集总干扰的估计，进而利用H∞技术对滑模观测器估计误差和振动输入引起的干扰进行抑制。冲击工况和随机路面激励下的仿真结果表明：(1)滑模观测器能够精确估计座椅悬架系统的未知扰动；(2)带有滑模观测器干扰补偿的H∞最优控制相较无补偿的H∞控制和无控制，座椅悬架的性能指标与乘坐舒适性都有明显的改善。
关键词：座椅悬架；半主动控制；滑模观测器；H∞最优控制

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	王小龙1
	黄晋英1
	吕海峰1
	魏成思2

关键词 ：座椅悬架；半主动控制；滑模观测器；H&infin, 最优控制

Abstract：A sliding mode observer based H∞ optimal controller for five-degree-of-freedom semi-active seat suspension with a human body model is proposed in this paper. The un-modeled dynamics, parameter uncertainty and actuator unknown input of the seat suspension system can be regarded as the lumped disturbance since they are in the same channel. A sliding mode observer for estimating the lumped disturbance of the seat suspension is designed based on the augmented system with acceleration via a low-pass filter, and then the H∞ technique was employed to attenuate the disturbance caused by estimation error of the sliding mode observer and vibration excitation. The simulations under bump and random excitations show that: (1) The designed sliding mode observer can accurately estimate the disturbance of the seat suspension system; (2) compared with H∞ controller only and uncontrolled, the H∞ optimal controller with a sliding mode observer can obtain much better performance and ride comfort.
Key words: seat suspension; semi-active control; sliding mode observer; H∞optimal control

Key words： seat suspension semi-active control sliding mode observer H∞optimal control

收稿日期: 2021-05-24 出版日期: 2022-07-15

引用本文:

王小龙1，黄晋英1，吕海峰1，魏成思2. 基于滑模观测器的车辆半主动座椅悬架系统H∞最优控制[J]. 振动与冲击, 2022, 41(13): 246-251.
WANG Xiaolong1, HUANG Jinying1, L Haifeng1, WEI Chengsi2. H∞ optimal control of vehicle semi-active seat suspension system based on sliding mode observer. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(13): 246-251.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2022/V41/I13/246

[1] Choi S B , Han Y M . Vibration control of electrorheological seat suspension with human-body model using sliding mode control[J]. Journal of Sound & Vibration, 2007, 303(1-2):391-404.
[2] Sun Shuaishuai, Ning Donghong, Yang J, et al. A seat suspension with a rotary magnetorheological damper for heavy duty vehicles[J]. Smart Materials and Structures, 2016, 25(10):105032.
[3] Ning Donghong , Sun Shuaishuai, Wei Lidui et al. Vibration reduction of seat suspension using observer based terminal sliding mode control with acceleration data fusion[J]. Mechatronics, 2017, 44:71-83.
[4] Lathkar M S , Shendge P D , Phadke S B . Active Control of Uncertain Seat Suspension System Based on a State and Disturbance Observer[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017, PP(99):1-11.
[5] Shin D K , Phu D X , Choi S M , et al. An adaptive fuzzy sliding mode control of MR seat suspension with human body model[J]. Journal of Intelligent Material Systems & Structures, 2015, 27(7):925-934.
[6] 杨成云,浮洁,余淼.具有随机干扰的车辆座椅悬架系统的鲁棒H∞控制[J].重庆大学学报,2012,35(06):9-14.
Yang Chengyun, Fu Jie, Yu Miao. Robust H∞ control for vehicle seat suspension with stochastic disturbance[J]. Journal of Chongqing University,2012,35(06):9-14.
[7] 张志勇, 刘鑫, 黄彩霞等. 具有参数不确定性的车辆座椅悬架H∞输出反馈半主动控制[J]. 振动与冲击, 2013, 32(014):93-97.
Zhang Zhiyong,Liu Xin, Huang Caixia et al. H∞ output feedback semi-active control for a vehicle seat suspension with parametric uncertainty[J]. Journal of vibration and shock, 2013, 32(014):93-97.
[8] 吕振鹏,毕凤荣,XU Wang等.车辆半主动座椅悬架自适应模糊滑模控制[J].振动与冲击,2021,40(02):265-271.
Lu Zhenpeng, Bi Fengrong,Xu Wang et al. Adaptive fuzzy sliding mode control for semi-active seat suspensions[J]. Journal of vibration and shock, 2021,40(02):265-271.
[9] Tan C P , Edwards C . Sliding Mode Observers for Robust Detection and Reconstruction of Actuator and Sensor Faults[J]. International Journal of Robust & Nonlinear Control, 2010, 13(5):443–463.
[10] Shtessel Y, Edwards C, Fridman L, et al. Sliding mode control and observation[M]. New York: Springer New York, 2014.
[11] Draguna Vrabie, Kyriakos G. Vamvoudakis and Frank L. Lewis. Optimal Adaptive Control and Differential Games by Reinforcement Learning Principles[M]. The Institution of Engineering and Technology, London, 2014.
[12] Wang Xiaolong. Semi-active adaptive optimal control of vehicle suspension with a magneto- rheological damper based on policy iteration [J]. Journal of Intelligent Material Systems and Structures, 2018, 29 (2): 255-264.