Abstract
Under complex working conditions, vehicle adaptive cruise control (ACC) systems with fixed weight coefficients cannot guarantee good car following performance under all conditions. In order to improve the tracking and comfort of vehicles in different modes, a fuzzy model predictive control (Fuzzy-MPC) algorithm is proposed. Based on the comprehensive consideration of safety, comfort, fuel economy and vehicle limitations, the objective function and constraints are designed. A relaxation factor vector is introduced to soften the hard constraint boundary in order to solve this problem, for which there was previously no feasible solution. In order to maintain driving stability under complex conditions, a multi-objective optimization method, which can update the weight coefficient online, is proposed. In the numerical simulation, the values of velocity, relative distance, acceleration and acceleration change rate under different conditions are compared, and the results show that the proposed algorithm has better tracking and stability than the traditional algorithm. The effectiveness and reliability of the Fuzzy-MPC algorithm are verified by co-simulation with the designed PID lower layer control algorithm with front feedforward and feedback.
Highlights
Advanced Driver Assistant Systems (ADAS) [1] are currently widely deployed in automobiles
Chan et al [4] used a distributed arithmetic structure based on FPGA-based high-efficiency PID as the adaptive cruise control (ACC) control algorithm
Naus et al [7] selected followability, driving comfort and fuel economy as the system optimization goals in the multi-objective ACC system designed based on MPC, so that the vehicle can improve the fuel economy on the premise of safe following and driving comfort
Summary
Advanced Driver Assistant Systems (ADAS) [1] are currently widely deployed in automobiles. Wu et al [14] designed an ACC system based on an active braking algorithm to improve vehicle safety and comfort. Satoru et al [16] designed the lower controller of the ACC system based on PID control It can keep the actual acceleration of the ACC system vehicle consistent with the expected acceleration output through the upper controller of the ACC system. The longitudinal kinematics model between the two vehicles is established, and the objective function and constraint conditions are designed considering the vehicle speed, acceleration, acceleration rate of change, distance error and relative speed. The lower controller adopts a threshold-based switching strategy, uses PID feedback control to minimize the acceleration error, and designs a braking force controller and a throttle opening controller. Ξma fin, ξmjerink , ξ u min lower bound hard constraint
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