An adaptive cascade trajectory tracking control for over-actuated autonomous electric vehicles with input saturation  

作　　者：GUO JingHua LUO YuGong WANG JingYao LI KeQiang CHEN Tao 

机构地区：[1]School of Aerospace Engineering,Xiamen University,Xiamen 361005,China [2]State Key Laboratory of Automotive Safety and Energy,Tsinghua Universit,Bejing 100084,China [3]China Automotive Engineering Research Institute Co,Ltd,Chongqing 401122,China

出　　处：《Science China(Technological Sciences)》2019年第12期2153-2160,共8页中国科学（技术科学英文版）

基　　金：supported by the National Basic Research Project of China(Grant Nos.2016YFB0100900&2016YFB0101101);the National Natural Science Foundation of China(Grant Nos.U1564208,61803319&61304193);the Natural Science Foundation of Fujian Province(Grant No.2017J01100)

摘　　要：This paper presented a novel adaptive cascade nonlinear trajectory tracking control scheme of over-actuated autonomous electric vehicles involving input saturation. First, a nonlinear vehicle dynamic model with input saturation is established, which can accurately describe the features of uncertainties and coupling of autonomous electric vehicles, and the hyperbolic tangent function is designed to estimate the saturation function for dealing with the input saturation problem. Then, a novel adaptive cascade trajectory tracking control scheme is designed. An adaptive neural network-based terminal sliding control law is proposed for producing the generalized force/moment in real-time, the asymptotic stability of this adaptive control system is proven by Lyapunov theory, and a quasi-newton distribution law is designed to determine the optimum tire forces that guarantee the actual generalized forces/moment are close to the desired values. Finally, simulation results demonstrate the effectiveness of the proposed control scheme.This paper presented a novel adaptive cascade nonlinear trajectory tracking control scheme of over-actuated autonomous electric vehicles involving input saturation. First, a nonlinear vehicle dynamic model with input saturation is established, which can accurately describe the features of uncertainties and coupling of autonomous electric vehicles, and the hyperbolic tangent function is designed to estimate the saturation function for dealing with the input saturation problem. Then, a novel adaptive cascade trajectory tracking control scheme is designed. An adaptive neural network-based terminal sliding control law is proposed for producing the generalized force/moment in real-time, the asymptotic stability of this adaptive control system is proven by Lyapunov theory, and a quasi-newton distribution law is designed to determine the optimum tire forces that guarantee the actual generalized forces/moment are close to the desired values. Finally, simulation results demonstrate the effectiveness of the proposed control scheme.

关 键 词：autonomous electric vehicles input saturation over-actuated DISTRIBUTION adaptive terminal sliding control 

分 类 号：O17[理学—数学]