Adaptive cruise control method based on improved grey prediction

被引:3
作者
Zhou, Chunguo [1 ]
Qiao, Ning [1 ]
Mao, Jin [1 ]
Zeng, Zhicheng [1 ]
Zhou, Yongjun [2 ,3 ]
机构
[1] Xian Univ Technol, Sch Mech & Precis Instrument Engn, Xian, Shaanxi, Peoples R China
[2] Changan Univ, Key Lab Bridge Detect Reinforcement Technol Minis, Minist Educ, Xian, Shaanxi, Peoples R China
[3] Changan Univ, Res Ctr Highway Large Struct Engn Safety, Minist Educ, Xian, Shaanxi, Peoples R China
基金
中国国家自然科学基金;
关键词
Adaptive cruise; multi-performance objective optimization; model predictive control; grey prediction;
D O I
10.3233/JIFS-221690
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In order to improve the comprehensive performance of adaptive cruise control system in the car-following process and take the safety into account, an improved model predictive control algorithm considering multi-performance objective optimization is designed. In the prediction model part, the grey Verhulst model with saturation state is introduced to predict the acceleration disturbance of the preceding vehicle, and the particle swarm optimization algorithm is used to estimate the parameters, which is then applied to the car following model. The control problem is transformed into a quadratic programming problem with multiple constraints through multi-objective quadratic performance index, and the vector constraint management method is introduced to solve the problem of no feasible solution caused by hard constraints. The emergency acceleration, deceleration and stable following are simulated. Finally, the Worldwide Harmonized Light Vehicles Test Cycle is co-simulated. The results show that the improved model predictive control algorithm can improve the tracking capability, fuel economy and comfort of adaptive cruise system.
引用
收藏
页码:543 / 553
页数:11
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