摘要
车辆在行驶过程中由于受车载、路况、车体运动状态、环境变化等因素影响,使得通过建立数学模型来精确描述车辆动态过程变得非常困难,导致传统基于模型的控制方法难以适应车辆行驶过程中复杂的动态变化因素。针对车辆的动态目标位置跟踪特点,通过构建车辆动态目标位置的运动学模型,采用自适应预测控制方法(Model-free Adaptive Predictive Control,简称MFAPC)研究车辆弯道保持系统中的动态目标位置跟踪问题,并基于MFAPC方法实现控制器的设计。控制仿真结果表明,相比PID控制方法,采用无模型自适应预测控制方法对车辆动态目标位置进行动态跟踪控制,其跟踪精度更高,控制过程更加平稳,从而使车辆驾驶过程具有更好的舒适性。
The vehicle motion was a dynamic process involves road condition, motion status and various environmental factors, which makes it very difficult to build mathematic model for moving vehicle in traditional ways. To solve this problem, the model-free adaptive predictive control（MFAPC）was applied in it to build the dynamic Kinect model for vehicle. Combined with the position tracking principles, MFAPC was able to yield the control inputs for vehicle control. This method was verified in simulation process, and results demonstrated that MFAPC yielded better performance and more stable in vehicle position tracking problems when compared with traditional PID control method.
作者
莫舒玥
MO Shu-yue(Guangxi Vocational ＆ Technical College of Communications,Department of Automotive Engineering,Guangxi Nanning 530022,China)
出处
《机械设计与制造》
北大核心
2018年第12期96-99,104,共5页
Machinery Design & Manufacture
基金
广西自然科学基金项目(2014jjBA60017)
关键词
动态目标位置
运动学模型
自适应预测控制
PID控制
Dynamic Target Position
Kinematic Model
Model Free Adaptive Predictive Control
PID Control
