摘要
针对传统变论域模糊控制存在过度依赖专家经验、伸缩因子参数不能自适应调整的问题,提出一种车辆主动悬架自适应变论域T-S模糊控制策略,从而提高车辆的行驶平顺性。结合神经网络和T-S模糊推理建立基于自适应神经模糊推理的一阶T-S模糊控制器,利用神经网络的自学习特性产生完善的模糊规则,进而在传统函数型伸缩因子的基础上,将系统误差和误差变化率作为动态参数引入伸缩因子中,实现伸缩因子参数的自适应调整,解决了传统函数型伸缩因子因参数确定难度大导致控制效果差的问题。通过随机工况下的仿真分析和基于相似理论的缩尺实验,对所提出算法的有效性和工况自适应性进行了验证。结果表明,所提出的自适应变论域T-S模糊控制策略具有较强的工况适应性,在不同车速、路面激励下均可有效提高车辆的平顺性并保证轮胎接地安全性。
The traditional variable universe fuzzy control relies on expert experience and the expansion factor can not be adjusted adaptively for vehicle active suspension.An adaptive variable universe T-S fuzzy control is proposed to improve vehicle ride comfort.Combining neural network into T-S fuzzy inference system,a firstorder T-S fuzzy controller based on an adaptive neuro-fuzzy inference system is established.And the perfect fuzzy rules are generated by the self-learning characteristics of neural network.Then,on the basis of the traditional functional expansion factor,the system error and error change rate are introduced into the expansion factor as dynamic parameters to realize the adaptive adjustment of expansion factor parameters,which can avoid producing control effect caused by the difficulty in determining the traditional functional expansion factor param-eters.The effectiveness and adaptability of the proposed algorithm are verified by simulation analysis of random and bump roads and scale experiments based on similarity theory under multiple working conditions.The research results show that the proposed adaptive variable universe T-S fuzzy control strategy has strong adaptability,thereby effectively improving the vehicle ride comfort and handling stability under different vehicle speeds and road excitations.
作者
李韶华
季广港
冯桂珍
王贺
LI Shaohua;JI Guanggang;FENG Guizhen;WANG He(State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures,Shijiazhuang Tiedao University Shijiazhuang,050043,China;School of Traffic and Transportation,Shijiazhuang Tiedao University Shijiazhuang,050043,China;Key Laboratory of Mechanical Behavior Evolution and Control of Traffic Engineering Structures in Hebei Shijiazhuang,050043,China;School of Rail Transportation,Shandong Jiaotong University Ji′nan,250000,China)
出处
《振动．测试与诊断》
EI
CSCD
北大核心
2024年第4期733-739,828,共8页
Journal of Vibration,Measurement & Diagnosis
基金
国家自然科学基金重点资助项目(U22A20246)。
关键词
主动悬架
变论域
伸缩因子
T-S模糊控制
神经模糊系统
active suspension
variable universe
expansion factor
T-S fuzzy control
neuro fuzzy system
