Economic Predictive Cruise Control For Vehicle Multi-performance Based On InPA-SQP Algorithm

Vehicle intelligent driving technology is a hot issue in intelligent transportation systems,as the product of intelligent driving technology,Adaptive Cruise Control(ACC)System is designed to release human drivers' workload,alleviating driver fatigue driving and improve road mobility.Recently,fuel economy and driving comfort beginning to attract the attention of researchers which make multi-performance cruise control of vehicles become an important research content.The multi-performance cruise controller considers not only driving economy,but also driving safety and comfort.To design vehicle multi-performance controller with advanced algorithm can effectively alleviate road congestion,reduce traffic accidents and improve driving economy,therefore,the study of vehicle performance is of great significance.Based on the existing ACC control technology,this thesis proposes a new control strategy to achieve safe,efficient,economical and comfortable driving according to the multi-performance of vehicle cruising.The main research work in this paper is as follows:(1)The existing ACC system focuses on cruise control,but in the urban road,vehicle driving involves many driving performances such as safety,tracking ability,fuel economy and comfort driving.This thesis considers multi-objective of ACC system,and then the relationship between vehicle cruise' multiple performances are analyzed qualitatively from a theoretical point of view.Finally,multiple cruising performance of vehicles are classified and expressed by mathematical formulas.(2)Due to the conflicts of multiple performances in vehicle cruise system,all performance are optimal is impossible.For vehicle systems,this is essentially a multi-objective optimization problem.The traditional method to solve multi-objective problems is weighted average method,which transforms multi-objective problems into single-objective optimization problems.In this method,the selection of weighting coefficients is considered to be modified according to different driving scenarios,which is almost impossible to achieve in the actual operation process.In order to avoid weight selection,a utopia-tracking based predictive cruise controller is designed by combining the utopia method(which is a multi-objective processing method)with model predictive control(MPC).It not only solves the defect of weight selection by weighting methods,but also does not affect the performance of the system.(3)ACC system is a complex nonlinear system,and the corresponding optimal control problem is a non-convex nonlinear programming problem.Because the nonlinear programming problem needs to be solved repeatedly in each sampling period,the computational complexity is very large.However,the ACC system needs real-time response,so the computational complexity needs to be reduced.In this thesis,the perturbation-based sequential quadratic programming(InPA-SQP)algorithm is adopted to solve the multi-objective predictive cruise control problem which combines utopia point method with model predictive control.The simulation results show that the algorithm is efficient.(4)To optimize the fuel economy of the connected vehicles under urban road conditions,a predictive control strategy for fuel economy is proposed.Based on the singal and timing information in the intersections,an optimal economic target speed is calculated,and on this basis,combining with the utopia point method and model predictive control algorithm,a finite horizon fuel economy optimal control problem is defined to ensure the traffic,economy and driving safety of the road.Finally,the effectiveness of the strategy in urban road is verified by simulating the actual scene.