Affiliations: [a] School of Mechanical and Precision Instrument Engineering, Xi’an University Of Technology, Xi’an University of Technology, Xi’an, Shaanxi, China
| [b] Key Laboratory of Bridge Detection Reinforcement Technology Ministry of Communications and Research Center of Highway Large Structure Engineering on Safety, Ministry of Education, Chang’an University, Xi’an, Shaanxi, China
Correspondence:
[*]
Corresponding author. Yongjun Zhou, Key Laboratory of Bridge Detection Reinforcement Technology Ministry of Communications and Research Center of Highway Large Structure Engineering on Safety, Ministry of Education, Chang’an University, Xi’an, Shaanxi, China. E-mail: [email protected].
Abstract: 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.
