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Optimal control for automotive seat suspension system based on acceleration based particle swarm optimization

机译:基于加速粒子群算法的汽车座椅悬架系统最优控制

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Weighting matrices of most standard LQR controllers are determined by the designers according to their experience, and that usually makes controllers unable to achieve global optimum. To deal with this problem, a method of determining weighting matrices by acceleration based particle swarm optimization (APSO) was proposed. A six degree of freedom (DOF) half car model with “seat and human” system was established in this paper. The parameters of the seat suspension were optimized by APSO, and LQR control was carried out on the basis of the parameter optimization system. In this study, MATLAB/Simulink was used for the simulation of parameter optimization system and LQR control system. The vibration isolation performance of seat suspension system was indicated by the vertical acceleration of “seat and human” with the constraint of dynamic displacement of seat suspension. Results show that the controller based on APSO has better vibration-reducing properties than the LQR controller based on GA.
机译:大多数标准LQR控制器的加权矩阵由设计人员根据其经验确定,通常这会使控制器无法实现全局最优。为了解决这个问题,提出了一种通过基于加速度的粒子群算法(APSO)确定加权矩阵的方法。本文建立了具有“人与人”系统的六自由度(DOF)半车模型。通过APSO优化座椅悬架的参数,并在参数优化系统的基础上进行LQR控制。在本研究中,将MATLAB / Simulink用于参数优化系统和LQR控制系统的仿真。座椅悬架系统的隔振性能由“座椅和人”的垂直加速度表示,并受座椅悬架动态位移的限制。结果表明,与基于GA的LQR控制器相比,基于APSO的控制器具有更好的减振性能。

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