车辆横向稳定的混合参数变模型预测控制

T. Besselmann, P. Rostalski, M. Morari
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引用次数: 6

摘要

本文将混合参数变模型预测控制(HPV-MPC)的概念应用于车辆的横向稳定。在MPC环境中,参数变意味着采用非恒定的、参数变的系统矩阵的预测模型。在被调查的场景中,汽车在结冰的道路上在侧面阵风下的位移需要减轻。为了探索可能减少在线计算和控制性能的固有退化,横向动力学的非线性模型以各种方式近似。使用从全非线性模型(作为最大可实现性能的指示)到线性模型的预测模型的控制器之间的比较已经进行了。特别强调了混合参数变化预测模型,以研究其在计算量和控制性能方面的潜力。
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Hybrid Parameter-Varying Model Predictive Control for lateral vehicle stabilization
In this paper the concept of Hybrid Parameter-Varying Model Predictive Control (HPV-MPC) is applied for lateral vehicle stabilization. Parameter-varying in the MPC context means that a prediction model with non-constant, parameter-varying system matrices is employed. In the investigated scenario, the displacement of a car on an icy road under a side wind gust shall be mitigated. In order to explore a possible reduction of online computations and the inherent degradation of control performance, the nonlinear model of the lateral dynamics is approximated in various ways. A comparison between controllers using prediction models varying from the full nonlinear model, as an indication for the maximal achievable performance, to a linear model has been performed. Particular emphasis was put on the hybrid parameter-varying prediction model, to investigate their potential in terms of computational effort and control performance.
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