基于量子粒子群算法的主动悬架分数阶控制策略

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摘要

结合分数阶微积分理论，针对1/4车辆主动悬架模型，设计一种以车身垂向加速度为反馈变量的分数阶控制器。综合车身垂向加速、悬架动挠度和车轮相对动载荷3个指标，建立优化指标的无量纲评价函数。将分数阶控制器的比例系数、积分系数、积分阶次、微分系数及微分阶次当作五维空间粒子，采用量子粒子群算法(QPSO)确定最优粒子。利用MATLAB软件建立悬架系统仿真模型，分别对被动悬架、含整数阶控制器的主动悬架及含分数阶控制器的主动悬架进行时域和频域仿真研究，对比结果表明，相对于整数阶主动悬架与被动悬架，含分数阶控制器的主动悬架明显改善了汽车平顺性。基于量子粒子群算法的主动悬架分数阶控制策略能更有效地抑制车身共振、改善汽车乘坐舒适性。

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	许力
	曹青松
	张定军

关键词 ：主动悬架, 分数阶控制器, 量子粒子群算法(QPSO), 评价函数

Abstract：A fractional-order controller with vertical acceleration as feedback variable was designed for a 1/4 vehicle active suspension model.A dimensionless evaluation function was deduced by integration of three indexes of vertical acceleration, dynamic deflection and wheel relative dynamic load.The proportional coefficient, integral coefficient, integral order, differential coefficient and differential order of the fractional-order controller were taken as a five-dimensional space particle, and the quantum particle swarm optimization (QPSO) algorithm was utilized to search the optimal particle.A simulation model was established on MATLAB to research the characteristics of time-domain and frequency-domain of passive suspension, the active suspension with an integer-order controller and a fractional-order controller.The comparison results show that the ride comfort can be improved by the active suspension with the fractional-order controller compared with the passive suspension and another active suspension.The fractional-order control strategy of active suspension based on QPSO can effectively suppress body resonance and improve ride comfort.

Key words： active suspension fractional order controller quantum particle swarm optimization(QPSO) evaluation function

收稿日期: 2020-01-14 出版日期: 2021-08-28

引用本文:

许力，曹青松，张定军. 基于量子粒子群算法的主动悬架分数阶控制策略[J]. 振动与冲击, 2021, 40(16): 227-233.
XU Li, CAO Qingsong, ZHANG Dingjun. Fractional order control strategy of active suspension based on QPSO. JOURNAL OF VIBRATION AND SHOCK, 2021, 40(16): 227-233.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2021/V40/I16/227

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