Predictive Control for improving vehicle handling and stability

M. Wasim, A. Kashif, A. Awan, M. M. Khan, Syed M. Tahir Zaidi
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引用次数: 3

Abstract

In the modern era of automotive industry, Active Front Steering (AFS) control is in hot pursuit due to its increased inbuilt vehicle maneuverability, reduced risk of instability and thereby significant reduction in number of accident occurrences. In this paper, a novel control strategy of Model Predictive Controller (MPC) with robust Optimal Guaranteed Cost Controller (OGCC) is introduced for AFS control. In the proposed scheme, MPC is designed to compensate for the effect of the driver's steering input, modeled as a known disturbance. Thus, providing the optimal solution by satisfying control input constraint. The OGCC controller renders the system robustness to the effect of system uncertainties, while achieving acceptable performance for tracking the desired vehicle trajectory. The complete controller scheme achieves the best results with RMS error <; 10-4 and thus whilst guaranteeing the stability of vehicle in the presence of uncertain environment. Numerical simulation results are performed to the effectiveness of the proposed approach over other conventional techniques.
预测控制,提高车辆操控性和稳定性
在现代汽车工业中,主动前轮转向(AFS)控制因其增加了车辆的内在机动性,降低了不稳定的风险,从而大大减少了事故发生的数量而受到人们的追捧。本文提出了一种新的模型预测控制器(MPC)与鲁棒最优保证成本控制器(OGCC)相结合的控制策略。在提出的方案中,MPC被设计为补偿驾驶员转向输入的影响,建模为已知的干扰。从而给出了满足控制输入约束的最优解。OGCC控制器使系统对系统不确定性的影响具有鲁棒性,同时获得可接受的跟踪期望车辆轨迹的性能。完整的控制器方案在RMS误差<;10-4,从而保证车辆在不确定环境下的稳定性。数值仿真结果表明,该方法与其他传统方法相比是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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