车辆AFS模糊神经网络系统稳定性控制研究  

Research on Stability Control of Vehicle AFS Fuzzy Neural Network System

作　　者：唐焱[1] 裴翔 蒋占四[1] 肖蓬勃 TANG Yan;PEI Xiang;JIANG Zhan-si;XIAO Peng-bo(School of Mechanical and Electrical Engineering,Guilin University of Electronic Technology,Guangxi Guilin 541004,China;Key Laboratory of Engines,Tianji University,Tianji 300027,China)

机构地区：[1]桂林电子科技大学,机电工程学院,广西桂林541004 [2]天津大学,内燃机燃烧学国家重点实验室,天津300072

出　　处：《机械设计与制造》2019年第A01期64-68,共5页Machinery Design & Manufacture

基　　金：国家自然科学基金(51565008)

摘　　要：针对车辆主动前轮转向系统(AFS)日趋先进的稳定性控制需求,研究了基于相对模糊控制具有更强自适应性与鲁棒性的模糊神经网络理论控制方法,采用附加前轮转角闭环控制策略,设计了自适应模糊神经网络控制器。并且针对目前车辆AFS稳定性控制研究缺少同时考虑AFS变传动比功能的问题,设计了固定横摆角速度增益下的变传动比规律。在Matlab/Simulink与Carsim搭建的主动前轮转向车辆模型上,采用典型与复杂两种工况对控制方法进行验证。结果表明:基于模糊神经网络控制的AFS控制方法相对模糊控制、无控制可以在车辆转向行驶时更好地提高车辆操纵稳定性,且对复杂工况有很好的鲁棒性。Aiming at the increasingly advanced stability control requirements of active front steering system (AFS)vehicle, a fuzzy neural network theory control method based on relative fuzzy control with stronger self-adaptability and robustness is studied.And a adaptive fuzzy neural network controller is designed by using the closed loop control strategy of additional front wheel angle. And aiming at the lack of variable transmission ratio in the research of AFS vehicle stability control at present, the AFS ideal transmission ratio law under the constant yaw angular velocity gain is designed.In the model of active front wheel steering vehicle built by Matlab/Simulink and Carsim, the control method is verified under two typical and complex working conditions. The results show that the AFS control method based on fuzzy neural network control is relatively fuzzy control, and no control can improve the vehicle handling stability better when vehicle steering , and has good robustness to complex operating conditions.

关 键 词：AFS 模糊神经网络 附加转角 变传动比 横摆角速度 联合仿真 

分 类 号：TH16[机械工程—机械制造及自动化] U461.6[机械工程—车辆工程]