基于IPSO-MPC的无人驾驶车辆纵向运动控制

Longitudinal motion control of unmanned vehicle based on IPSO-MPC

针对无人驾驶车辆纵向控制中的速度跟踪精度问题,提出一种基于改进的粒子群优化算法(improved particle swarm optimization,IPSO)的模型预测控制方法。首先,在模型预测控制中将问题转换为二次规划问题,使用粒子群算法进行求解,并通过分层纵向控制器,实现对速度的跟踪控制。为降低其陷入局部最优解的风险,引入了随机权重策略和学习因子调整策略。其次,为了提高粒子寻优的速度,保存了上一时刻的最优粒子序列作为下一时刻粒子的群体极值。最后,为了验证算法的有效性,通过Simulink/CarSim建立了联合仿真平台,仿真结果表明,该算法有效提高了车辆速度跟踪的控制精度,最大误差减小了0.2747 km/h。

A model predictive control method based on an improved particle swarm optimization algorithm(IPSO)was proposed to solve the speed tracking accuracy problem in the longitudinal control of unmanned vehicles.Firstly,the model predictive control converted the problem into a quadratic programming problem,used particle swarm algorithm to solve it,and implemented the speed tracking control through the layered longitudinal controller.The random weight strategy and the learning factor adjustment strategy were used to reduce the risk of falling into a local optimal solution.Secondly,the optimal particle sequence at the previous moment was preserved as the population extreme value of the particle at the next moment to improve the speed of particle searching.Finally,a co-simulation platform was established through Simulink/CarSim to verify the effectiveness of the algorithm.Simulation results show that the algorithm effectively improves the control accuracy of vehicle speed tracking,and the maximum error is reduced by 0.2747 km/h.