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汽车安全与节能学报

汽车安全与节能学报 ›› 2019, Vol. 10 ›› Issue (3): 285-292.DOI: 10.3969/j.issn.1674-8484.2019.03.003

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基于分段仿射模型的非线性悬架预测控制

胡启国，陆  伟   

  1. （重庆交通大学 机电与车辆工程学院，重庆 400074，中国）
  • 收稿日期:2019-01-15 出版日期:2019-09-30 发布日期:2019-10-01
  • 作者简介:第一作者： 胡启国(1968—)，男( 汉)，重庆，教授。E-mail: swpihqg@126.com
  • 基金资助:

    国家自然科学基金资助项目(51375519)；重庆市基础科学与前沿技术研究专项项目(cstc2015jcyjBX0133)。

Nonlinear suspension predictive control based on piecewise affine model

HU Qiguo, LU Wei   

  1. (School of Mechanotronics & Vehicle Engineering, Chongqing Jiaotong University, Chongqing 400074, China)
  • Received:2019-01-15 Online:2019-09-30 Published:2019-10-01

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摘要：

       为了避免车辆通过不平路面时发生悬架击穿，提出了一种基于多模型预测控制的车辆非线性 悬架主动控制方法。建立了1/4非线性悬架模型，采用基于改进粒子群算法的数据聚类和参数辨识， 建立了主动力和车身位移关系的线性分段仿射(PWA) 模型。通过多模型预测控制理论研究半主动悬架 PWA模型的滚动时域优化控制问题，得到最优控制信号。利用Matlab/Simulink 进行随机路面和正 弦凸起路面仿真。结果表明：采用多模型预测控制可以使得车辆在遇到不平路面时保持稳定的车身姿 态，同时很好地控制悬架动行程，减小了悬架击穿的概率。

关键词: 车辆运行平顺性 , 车辆悬架 , 粒子群算法 , 预测控制 , 参数辨识 , 非线性悬架 , 线性分段仿射

Abstract:

 An active control method for the vehicle nonlinear suspension was proposed based on the multimodel predictive control to avoid the suspension breakdown when the vehicle passes through the rough road. A 1/4 nonlinear suspension model was established with a linear piece wise affine (PWA) model related to the active force and the body displacement by using data clustering and parameter identification based on the improved particle swarm algorithm. The rolling time domain optimal control problem of the semi-active suspension was investigated based on the multi-model predictive control theory to obtain the optimal control signals. The simulation of random road and sinusoidal road surface was carried out in Matlab/Simulink. The results show that the adoption of multi-model control could make the vehicle maintain stable body posture when driving through the rough road, and the suspension dynamic travel is well controlled at the same time, which reduces the probability of suspension breakdown. 

Key words:  , vehicle ride comfort , vehicle suspension ,  particle swarm optimization , predictive control , parameter identification , nonlinear suspension ,  linear piece wise affine