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Comparison of Performance Effectiveness of Generalized Predictive Control Algorithms Developed for a Simplified Ground Vehicle Suspension System

机译:用于简化地架悬架系统开发的广义预测控制算法的性能效能的比较

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This paper discusses the research conducted by the Army Research Laboratory (ARL) - Vehicle Technology Directorate (VTD) on advanced suspension control. ARL-VTD has conducted research on advanced suspension systems that will reduce the chassis vibration of ground vehicles while maintaining tire contact with the road surface. The purpose of this research is to reduce vibration-induced fatigue to the Warfighter as well as to improve the target aiming precision in theater. The objective of this paper was to explore the performance effectiveness of various formulations of the Generalized Predictive Control algorithm in a simulation environment. Each version of the control algorithm was applied to an identical model subjected to the same disturbance input and compared to a baseline passive suspension system. The control algorithms considered include a Generalized Predictive Controller (GPC) with Implicit Disturbances, GPC with Explicit Disturbances, and GPC with Preview Control. The suspension model used was a two-degree-of-freedom (2 DOF) quarter car model with a given set of vehicle parameters. The performance of the developed control algorithms were compared based on their effectiveness in controlling peak acceleration and overall average acceleration through a range of vehicle speed. The algorithms demonstrated significant improvements in the chassis acceleration of the quarter-car model.
机译:本文讨论了陆军研究实验室(ARL) - 车辆技术总局(VTD)对先进暂停控制进行的研究。 ARL-VTD对先进的悬架系统进行了研究,该系统将减少地面车辆的底盘振动,同时保持与路面的轮胎接触。本研究的目的是将振动引起的抗战者疲劳降低,以及改善剧院瞄准精度的目标。本文的目的是探讨各种配方的仿真环境中的各种配方的性能有效性。将每个版本的控制算法应用于经过相同干扰输入的相同模型,并与基线被动悬架系统相比。所考虑的控制算法包括具有隐式干扰的广义预测控制器(GPC),具有显式干扰的GPC,以及具有预览控制的GPC。使用的悬架模型是一种自由度(2 DOF)四分之一车型,具有给定的一组车辆参数。比较了通过一系列车速控制峰值加速度和总体平均加速度的效力,比较了开发控制算法的性能。该算法表明了四分之一汽车模型的底盘加速度的显着改进。

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