基于卡尔曼滤波模型预测控制算法的汽车悬架系统

Q4 Engineering
A. Patra
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引用次数: 0

摘要

本文的目的是为车辆悬架系统(VSS)制定一个卡尔曼滤波模型预测控制器(KFMPC),通过保留因不愉快和弯曲的道路而产生的眩晕来提高乘坐舒适性。为了构造KFMPC,研究了VSS的四阶状态空间结构。在该策略中,利用卡尔曼滤波对传统模型预测控制器(CMPC)进行了重新设计,提高了控制的执行力。通过与其他知名控制策略的对比结果检验,确定了KFMPC升级控制执行的批准。相对结果明显表明,所推荐的策略在稳定范围内监测VSS动态的准确性、稳定性和鲁棒性较好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A vehicle suspension system based on Kalman filtering model predictive control algorithm
The aim of this manuscript is to formulate a Kalman filtering model predictive controller (KFMPC) for a vehicle suspension system (VSS) to enhance the ride luxury by retaining the stuns because of an unpleasant and crooked road. For the formulation of the KFMPC, a 4th order state-space structure of the VSS is deliberated. In this strategy, the conventional model predictive controller (CMPC) is redesigned by use of the Kalman filter to upgrade the control execution. The approval of the upgraded control execution of KFMPC is set up by comparative outcome examination with other well-known control strategies. The relative outcomes obviously reveal the better execution of the recommended strategy to monitor the VSS dynamics inside a steady range as for the accuracy, stability, and robustness.
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来源期刊
International Journal of Advanced Mechatronic Systems
International Journal of Advanced Mechatronic Systems Engineering-Mechanical Engineering
CiteScore
1.20
自引率
0.00%
发文量
5
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