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带耦合机构的串联式履带车辆直驶稳定性控制

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带耦合机构的串联式履带车辆直驶稳定性控制
Straight Driving StabilityControl of Series Tracked Vehicle with Coupling Mechanism
投稿时间:2018-06-07
DOI:10.15918/j.tbit1001-0645.2018.增刊1.025
中文关键词:耦合机构直驶稳定性神经网络PI控制滑移率
English Keywords:coupling mechanismstraight driving stabilityneural network PI controllerslip rate
基金项目:国家自然科学基金资助项目(51775040)
作者单位
刘辉北京理工大学 机械与车辆学院, 北京 100081
张聪北京理工大学 机械与车辆学院, 北京 100081
韩立金北京理工大学 机械与车辆学院, 北京 100081
曹福辉内蒙古第一机械集团有限公司, 内蒙古, 包头 140032
王伟达北京理工大学 机械与车辆学院, 北京 100081
侯旭朝北京理工大学 机械与车辆学院, 北京 100081
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中文摘要:
首先分析了串联式混合动力履带车辆耦合机构转矩与转速的输出特性及其对车辆直线行驶稳定性的影响,并利用MATLAB/Simulink仿真平台建立了整车动力学模型,其中考虑了履带的滑移率对附着系数的影响,其次为了提高车辆的直驶稳定性设计了相应的控制策略,即通过检测两侧车速的变化来计算两电机输出转矩的补偿量,并针对PI控制器参数适应性差的问题,通过神经网络自学习的功能实时优化PI控制器的控制参数.最后利用Simulink模型进行仿真,结果表明,神经网络PI控制能够保证车辆的直驶偏移量控制在0.5%左右,满足国家标准中最大直驶偏移量为1%的要求.
English Summary:
In this paper, the output torque and speed characteristics of coupling mechanism and the influence on the driving stability of the hybrid tracked vehicle were analyzed firstly. Then a vehicle dynamics model was established based on the MATLAB/Simulink platform, considering the effect of slip ratio of a vehicle on the adhesion coefficient. To improve the straight driving stability, a control strategy was designed to calculate the compensation of motor torque by detecting speed changes on both sides of the vehicle. To solve the problem of parameter adaptability of PI controller, the self-learning function of neural network was applied to optimize PI controller parameters in real time. Finally, simulation analysis was carried out based on Simulink model. The simulation results show that, the horizontal displacement of the tracked vehicle can be controlled in 0.5%, which meets the requirements of national standards.
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