基于纵向车速估算的商用车ABS神经网络滑模控制
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摘要
针对商用车防抱死制动系统(ABS)控制中纵向车速难以直接获得,提出了强跟踪容积卡尔曼滤波(STCKF)算法对制动过程中的纵向车速进行估算。然后根据ABS控制需求,提出了商用车ABS神经网络滑模控制算法,利用滑模算法对ABS的滑移率进行控制,再利用神经网络对滑模控制器的参数进行自适应调节。最后通过Matlab/Simulink与TruckSim联合仿真,分别在高、中、低附着系数路面和对开路面上进行仿真验证。仿真结果表明:强跟踪容积卡尔曼滤波算法对纵向车速的估算较为精确,ABS神经网络滑模控制效果良好。
It is difficult to directly obtain the longitudinal vehicle speed of commercial vehicle in the antilock braking system(ABS). In order to estimate the longitudinal speed in the braking process,a strong tracking-cubature Kalman filter algorithm was proposed. Then, according to the ABS control requirements,a commercial vehicle ABS neural network sliding control algorithm was established. It uses the sliding mode algorithm to control the slip rate of ABS,and uses the neural network to adjust the parameters of the sliding control algorithm. Through Matlab/Simulink and TruckSim co-simulation,simulations tests were conducted on high, medium, low, and off-road surfaces, respectively. The simulation results show that the strong tracking cubature Kalman filter algorithm is accurate in estimating the longitudinal speed,and the neural network sliding control algorithm of ABS has good effect.
It is difficult to directly obtain the longitudinal vehicle speed of commercial vehicle in the antilock braking system(ABS). In order to estimate the longitudinal speed in the braking process,a strong tracking-cubature Kalman filter algorithm was proposed. Then, according to the ABS control requirements,a commercial vehicle ABS neural network sliding control algorithm was established. It uses the sliding mode algorithm to control the slip rate of ABS,and uses the neural network to adjust the parameters of the sliding control algorithm. Through Matlab/Simulink and TruckSim co-simulation,simulations tests were conducted on high, medium, low, and off-road surfaces, respectively. The simulation results show that the strong tracking cubature Kalman filter algorithm is accurate in estimating the longitudinal speed,and the neural network sliding control algorithm of ABS has good effect.
引文
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[2]Antonov S,Fehn A,Kugi A.Unscented Kalman filter for vehicle state estimation[J].Vehicle System Dynamics,2011,49(9):1497-1520.
[3]Reif K,Renner K,Saeger M.Using the unscented kalman filter and a non-linear two-track model for ve-hicle state estimation[J].IFAC Proceedings Vol-umes,2008,17(1):8570-8575.
[4]金贤建,殷国栋,陈南,等.分布式驱动电动汽车的平方根容积卡尔曼滤波状态观测[J].东南大学学报:自然科学版,2016,46(5):992-996.Jin Xian-jian,Yin Guo-dong,Chen Nan,et al.State observation of distributed drive electric vehicle using square root cubature Kalman filter[J].Journal of Southeast University(Natural Science Edition),2016,46(5):992-996.
[5]Li Jing,Zhang Jia-xu.Vehicle Sideslip Angle estima-tion based on hybrid kalman filter[J].Mathematical Problems in Engineering,2016(3):1-10.
[6]Sun Ren-Yun,Wang Bo,Zhan Yong-Fu.Fuzzy con-trol of automobile ECBS on varying pavement[J].Ad-vanced Materials Research,2012(383-390):7338-7344.
[7]Drakunov S,Ozguner U,Dix P,et al.ABS control using optimum search via sliding modes[J].IEEETransactions on Control Systems Technology,2002,3(1):79-85.
[8]何祥坤,季学武,杨恺明,等.基于集成式线控液压制动系统的轮胎滑移率控制[J].吉林大学学报:工学版,2018,48(2):364-372.He Xiang-kun,Ji Xue-wu,Yang Kai-ming,et al.Tire slip control based on integrated-electro-hydraulic braking system[J].Journal of Jinlin University(Engi-neeringandTechnologyEdition),2018,48(2):364-372.
[9]张炳超.商用车气制动ABS鲁棒控制方法研究[D].重庆:重庆大学机械工程学院,2011.Zhang Bing-chao.A study of robust control method for pneumatic brake ABS on commercial vehicles[D].Chongqing:College of Mechanical Engineering,Chongqing University,2011.
[10]戴彦.汽车ABS滑移率的模糊滑模控制研究[J].机械设计与制造,2015(6):80-82.Dai Yan.Study on fuzzy sliding mode control of anti-skid-brake system based on slip ratio[J].Machinery Design&Manufacture,2015(6):80-82.
[11]Li Qiu-rong,Sun Feng.Strong tracking cubature Kalman filter algorithm for GPS/INS Integrated Navi-gation System[C]∥IEEE International Conference on Mechatronics and Automation.IEEE,2013:1113-1117.
[12]Xia Bi-zhong,Wang Hai-qing,Wang Ming-wang,et al.A new method for state of charge estimation of lithium-ion battery based on strong tracking cubature kalman filter[J].Energies,2015,8(12):13458-13472.
[13]朱为文.某客车复合制动系统控制策略研究[D].长春:吉林大学汽车工程学院,2017.Zhu Wei-wen.Study on control of composite braking system for a bus[D].Changchun:College of Automo-tive Engineering,Jilin University,2017.
[14]Dugoff H,Fancher P S,Segel L.An analysis of tire traction properties and their influence on vehicle dy-namic performance[C]∥SAE Papers,700377.
[15]李秋荣.改进容积卡尔曼滤波算法及其导航应用研究[D].哈尔滨:哈尔滨工程大学自动化学院,2015.Li Qiu-rong.Research on improved cubatrue Kalman filter and its application in navigation[D].Harbin:Col-lege of Automation,Harbin Engineering University,2015.
[16]王建敏,董小萌,吴云洁.高超声速飞行器RBF神经网络滑模变结构控制[J].电机与控制学报,2016,20(5):103-110.Wang Jian-min,Dong Xiao-meng,Wu Yun-jie.Hy-personic flight vehicle of sliding mode variable struc-ture control based on RBF neural network[J].Electric Machines and Control,2016,20(5):103-110.