基于改进粒子群优化算法的多目标自适应巡航控制

跟驰过程中,在保证安全性的前提下为了提升自适应巡航控制(ACC)系统的舒适性和燃油经济性,研究了多目标自适应巡航控制算法。在建立车间纵向运动学模型的基础上,根据模型预测控制理论,设计综合考虑安全性、舒适性、燃油经济性以及车辆自身限制等因素的目标函数和约束条件,并引入松弛因子向量软化硬约束边界解决无可行解问题。进一步在滚动优化环节中,引入具有求解多约束问题能力的改进粒子群优化算法进行求解。通过数值仿真对比分析,结果表明,基于改进粒子群优化算法的多目标自适应巡航控制算法能有效提高燃油经济性和行车舒适性。结合CarSim搭建模型进行联合仿真,验证算法有效。

Multi-Objective Adaptive Cruise Control Based on Improved Particle Swarm Optimization Algorithm

In order to improve the comfort and fuel economy of the adaptive cruise control (ACC) system, a multi-objective adaptive cruise control algorithm was studied. Based on the longitudinal kinematics model and the model predictive control (MPC) theory, the objective function and constraints were selected considering factors such as safety, comfort, fuel economy and vehicle conditions. The relaxation vector factor was introduced to soften the hard constraints for resolving infeasibilities.Furthermore, in the rolling optimization process, an improved particle swarm optimization (PSO) algorithm for multi-constraint problems was proposed. The results of numerical simulation and comparative analysis show that the multi-objective adaptive cruise control based on the improved particle swarm optimization algorithm can effectively improve fuel economy and driving comfort. In the end the joint simulation of CarSim and the model is conducted to verify the effectiveness of the proposed algorithm.