Design & Implementation Of Efficient Model Predictive Control Algorithms For Vehicle Cruise Systems

The adaptive Cruise Control(ACC)system is an advanced auxiliary driving system,which can effectively reduce the driver's driving burden,probability of mis-operation and fatigue driving situation.It can not only improve the ride comfort and safety,but also increase the road capacity,reduce congestion and save fuel consumption.The conventional ACC system aims to keep the desired inter-distance and relative velocity during tracking the preceding vehicle.Facing the increasingly severe traffic environment,many efforts have been done to improve the efficient control algorithms of cruise systems in the past decades.Efficient cruise control algorithms of vehicles can bring considerable improvement to the traffic situation.Therefore,how to design and implement efficient ACC systems has become an important issue of intelligent transportation as ACC has a significant application value and great significance.Based on the existing ACC system technology,we focus on the design of high-efficiency model predictive controller for ACC of vehicles.This thesis presents an efficient strategy for multi-objective predictive cruise control by combing the stair-like algorithm and the barrier function-method control technology.The performance of the vehicle cruise system is obviously improved and outperforms the traditional ACC algorithms.The main work and innovations of the thesis are as follows(1)Based on qualitative analysis of vehicle longitudinal dynamics,the cruise control of the vehicle is realized by using the incremental stair-like predictive control algorithm.In the cruise control algorithm,the control sequences are parameterized by the current control variables,which the dimension of the decision variables of predictive cruise control is determined by the current variables,thus greatly reducing the burden of calculation.It can effectively reduce online computation burdens and obtain smooth control output and improve ride comfort.(2)At present,most of the vehicle cruise control systems are optimized by minimizing one single performance objective.But the vehicle cruise system has multiple performance objectives,which can not meet the optimization between multiple performance objectives.For traditionalACC system,the performance of ACC is conflicting between multiple objectives.The weight method is used to reflect by the importance of one functionality,that is,focusing on one aspect of performance is bound to degrade the other.In order to avoid the negative optimization method,this thesis adopts the stair-like control,and re-plans the acceleration and deceleration of the vehicle,which aims to obtain a smooth speed/acceleration curve;That helps to improve the ride comfort of the vehicle,as well as achieving the purpose of saving fuel consumption.(3)Based on the observation of the preceding vehicle's acceleration and the stair-like model predictive control algorithm,the quadratic programming problem is reconstructed by the barrier function-method,and the solution method is simplified by Schur complement and matrix elimination method.Compared with the traditional ACC cruise controller,the new algorithm proposed can handle complicated traffic issue efficiently.(4)This thesis developed an animation software to validate the presented vehicle cruise control system,which can be used to compare and analyze the control effect of various adaptive cruise control algorithms.Based on the proposed algorithm,the programming language Python is used to compile a visual software for validating the algorithm,which are demonstrated the car following behaviors and the real-time numerical changes under the proposed cruise control algorithm dynamically.