针对空气悬架控制中的问题,采用Fuzzy-PID复合控制技术,即把模糊推理运用于PID参数的整定,对半主动空气悬架加以研究。设计了Fuzzy-PID控制器,用于半主动空气悬架1/4车辆模型控制的Matlab/Simulink仿真模拟和台架试验。仿真模型中借助S...针对空气悬架控制中的问题,采用Fuzzy-PID复合控制技术,即把模糊推理运用于PID参数的整定,对半主动空气悬架加以研究。设计了Fuzzy-PID控制器,用于半主动空气悬架1/4车辆模型控制的Matlab/Simulink仿真模拟和台架试验。仿真模型中借助S函数和Fuzzy Inference System Toolbox构建Fuzzy-PID模块,仿真结果表明:与传统的PID控制仿真比较,该控制策略下的半主动空气悬架能降低簧上质量加速度和悬架动行程,具有较好的鲁棒性,使车辆平顺性有一定程度的提高。台架试验与仿真结果基本吻合。展开更多
To improve the ride quality and enhance the control efficiency of cars’semi-active air suspensions(SASs)under various surfaces of soft and rigid roads,a machine learning(ML)method is proposed based on the optimized r...To improve the ride quality and enhance the control efficiency of cars’semi-active air suspensions(SASs)under various surfaces of soft and rigid roads,a machine learning(ML)method is proposed based on the optimized rules of the fuzzy control(FC)method and car dynamic model for application in SASs.The root-mean-square(RMS)acceleration of the driver’s seat and car’s pitch angle are chosen as the objective functions.The results indicate that a soft surface obviously influences a car’s ride quality,particularly when it is traveling at a high-velocity range of over 72 km/h.Using the ML method,the car’s ride quality is improved as compared to those of FC and without control under different simulation conditions.In particular,compared with those cars without control,the RMS acceleration of the driver’s seat and car’s pitch angle using the ML method are respectively reduced by 30.20% and 19.95% on the soft road and 34.36% and 21.66% on the rigid road.In addition,to optimize the ML efficiency,its learning data need to be updated under all various operating conditions of cars.展开更多
文摘针对空气悬架控制中的问题,采用Fuzzy-PID复合控制技术,即把模糊推理运用于PID参数的整定,对半主动空气悬架加以研究。设计了Fuzzy-PID控制器,用于半主动空气悬架1/4车辆模型控制的Matlab/Simulink仿真模拟和台架试验。仿真模型中借助S函数和Fuzzy Inference System Toolbox构建Fuzzy-PID模块,仿真结果表明:与传统的PID控制仿真比较,该控制策略下的半主动空气悬架能降低簧上质量加速度和悬架动行程,具有较好的鲁棒性,使车辆平顺性有一定程度的提高。台架试验与仿真结果基本吻合。
基金The National Key Research and Development Plan(No.2019YFB2006402)Talent Introduction Fund Project of Hubei Polytechnic University(No.17xjz01R)Key Scientific Research Project of Hubei Polytechnic University(No.22xjz02A)。
文摘To improve the ride quality and enhance the control efficiency of cars’semi-active air suspensions(SASs)under various surfaces of soft and rigid roads,a machine learning(ML)method is proposed based on the optimized rules of the fuzzy control(FC)method and car dynamic model for application in SASs.The root-mean-square(RMS)acceleration of the driver’s seat and car’s pitch angle are chosen as the objective functions.The results indicate that a soft surface obviously influences a car’s ride quality,particularly when it is traveling at a high-velocity range of over 72 km/h.Using the ML method,the car’s ride quality is improved as compared to those of FC and without control under different simulation conditions.In particular,compared with those cars without control,the RMS acceleration of the driver’s seat and car’s pitch angle using the ML method are respectively reduced by 30.20% and 19.95% on the soft road and 34.36% and 21.66% on the rigid road.In addition,to optimize the ML efficiency,its learning data need to be updated under all various operating conditions of cars.
