列车主动悬挂多速率系统鲁棒预测控制方法研究

发布时间：2018-06-13 14:18

本文选题：主动悬挂 + 多速率系统　；参考：《华东交通大学》2016年硕士论文

【摘要】：随着列车快速发展,悬挂系统控制性能将直接影响到列车运行稳定性和安全性,主动悬挂系统是目前减振降噪技术研究的热点对象,其能改善列车横向和垂向振动。然而现有的主动悬挂研究中往往忽视多速率现象,本文是在相关研究背景下,开展多速率主动悬挂控制技术研究,设计多速率主动悬挂系统鲁棒预测控制器,以期优化主动悬挂减振性能。本文首先考虑列车在运行时受外界激扰输入影响,将修改后的美国高速轨道谱作为列车垂向随机激扰;考虑到多轴车因素和多轴激励响应因素,建立了横向-垂向9自由度主动悬挂动力学模型;并计算主动悬挂模型舒适度指标,验证模型合理性。在连续时域模型的基础上,离散化得到单速率悬挂模型,进而采用提升技术建立多速率主动悬挂模型;开展基于LMI优化的状态反馈多速率主动悬挂系统鲁棒预测控制算法研究,将得到的多速率状态空间模型作为预测控制模型,为列车多速率主动悬挂系统设计鲁棒预测控制器;对比分析车体质心垂向、点头角和侧滚角加速度等输出时域响应结果,多速率比单速率系统具备更好的响应性能,其减振效果优于单速率系统,从而验证了多速率系统设计的合理性;采用鲁棒预测控制后,多速率控制前后控制效果相差3倍,验证了所设计的控制器效果,并且能在一定程度上验证鲁棒预测控制实时优化特性;验证多速率主动悬挂系统的可控可观测性和分析了其鲁棒稳定性;在基本采样周期一定的情况下,利用遗传算法得到多速率主动悬挂系统性能最优的采样周期,并采用这一最优采样周期优化列车在运行时的控制性能。在主动悬挂系统建模、鲁棒预测控制器设计及仿真等理论研究基础上,按比例搭建基于磁流变阻尼器主动悬挂试验系统装置,在试验台上开展振动实验,实验验证了试验台的振动效果和鲁棒预测算法对车体振动的控制效果,从振动响应频率与输出特性曲线对比分析了单速率和多速率系统,验证理论分析结果。
[Abstract]:With the rapid development of the train, the control performance of the suspension system will directly affect the stability and safety of the train operation. Active suspension system is a hot topic in the research of vibration and noise reduction technology, which can improve the lateral and vertical vibration of the train. However, the existing active suspension research often ignores the multi-rate phenomenon. In this paper, the multi-rate active suspension control technology is studied, and the robust predictive controller of multi-rate active suspension system is designed. In order to optimize the performance of active suspension damping. In this paper, first of all, considering that the train is affected by the external excitation input while running, the modified American high-speed track spectrum is considered as the vertical random disturbance of the train, and the factors of multi-axle train and multi-axis excitation response are taken into account in this paper. The dynamic model of lateral vertical nine degrees of freedom active suspension is established and the comfort index of active suspension model is calculated to verify the rationality of the model. On the basis of continuous time domain model, single rate suspension model is obtained by discretization, and then multi-rate active suspension model is established by lifting technology. The robust predictive control algorithm of state feedback multi-rate active suspension system based on LMI optimization is studied. Taking the multi-rate state space model as the predictive control model, the robust predictive controller is designed for the multi-rate active suspension system of the train, and the output time domain response results such as vertical center of mass, nodding angle and roll angle acceleration of the body are compared and analyzed. The response performance of multi-rate system is better than that of single-rate system, and its damping effect is better than that of single-rate system, which verifies the rationality of the design of multi-rate system. The effectiveness of the designed controller is verified, and the real-time optimization characteristics of robust predictive control are verified to a certain extent, the controllable observability of multi-rate active suspension system is verified and its robust stability is analyzed. Under the condition of the basic sampling period, the optimal sampling period of multi-rate active suspension system is obtained by genetic algorithm, and the optimal sampling period is used to optimize the control performance of train at running time. Based on the theoretical research of active suspension system modeling, robust predictive controller design and simulation, an active suspension test system based on magnetorheological damper is built in proportion, and the vibration experiment is carried out on the test bed. The vibration effect of the test bed and the control effect of the robust prediction algorithm on the car body vibration are verified by experiments. The single rate system and the multi-rate system are compared and analyzed from the vibration response frequency and the output characteristic curve, and the theoretical analysis results are verified.
【学位授予单位】：华东交通大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：U270.33;TP273

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