基于粒子群寻优的汽车自适应巡航预测控制

为进一步提升多目标自适应巡航系统预测控制精度，提出一种基于粒子群寻优的汽车自适应巡航预测控制算法.首先建立一种包含前车加速度扰动的自适应巡航系统车间纵向运动学模型，并对其线性离散化；其次综合车距误差、相对车速、自车加速度和冲击度，设计二次型多目标优化性能指标函数和多参数约束条件，构建自适应巡航预测控制优化命题；最后为便于问题求解，将目标函数和约束条件推导转化为以预测控制增量为优化变量的规范形式，并基于粒子群优化算法求解自适应巡航预测控制的最优控制律.通过Matlab/Simulink多工况仿真结果表明，粒子群算法求解的最优控制律能够控制自车保持更好的跟踪性和自适应性. 

Adaptive Cruise Predictive Control Based on Particle Swarm Optimization

To further improve the predictive control accuracy of multi-objective adaptive cruise system, an adaptive cruise predictive control algorithm based on particle swarm optimization was proposed. Firstly, a longitudinal kinematics model with front vehicle acceleration disturbance of adaptive cruise system was established and linearly discretized. Then, synthesizing the distance error, relative speed, acceleration and impact, a quadratic multi-objective optimization performance index function and multi-parameter constraints were designed, and an adaptive cruise predictive control optimization problem was constructed. Finally, in order to solve the problem easily, the objective function and constraints were deduced into a normative form with predictive control increment as the optimization variable, and the optimal control law of adaptive cruise predictive control was solved based on particle swarm optimization algorithm. The simulation results of Maltab/Simulink under multiple working conditions show that the optimal control law solved by particle swarm optimization algorithm can control the self-driving vehicle to maintain better tracking and self-adaptability.