基于GA-PSO的智能汽车横向LQR控制器优化设计

Optimization Design of Lateral LQR Controller for Intelligent Vehicle Based on GA-PSO

针对线性二次型调节器(LQR)在智能汽车横向控制中,系数矩阵Q和R选取困难导致的控制精度低和参数整定效率低的问题,提出了一种遗传粒子混合优化(GA-PSO)方法。基于车辆二自由度模型设计了横向LQR控制器和前馈控制器,以该模型下控制器自身能量损失函数作为代价函数对系数矩阵进行优化,并对比了GA-PSO和粒子群优化(PSO)算法的优化效果。CarSim/Simulink联合仿真结果表明,经GA-PSO算法优化后的控制器跟踪精度和计算效率分别提高了47.06%和63.54%,且优化后的控制器具有较强的鲁棒性。

In order to solve the problem of low control accuracy and low parameter tuning efficiency caused by difficulty in selecting coefficient matrix Q and R of Linear Quadratic Regulator(LQR)in lateral control of intelligent vehicle,this paper proposed an optimization method of genetic particle mixing(Genetic Algorithm-Particle Swarm Optimization,GA-PSO).A lateral LQR controller and a feed-forward controller were designed based on the two-degree-offreedom model of the vehicle.The coefficient matrix was optimized using the LQR controller’s own energy loss function as the cost function.The algorithm optimization results of GA-PSO and PSO were compared.The CarSim/Simulink co-simulation shows that the GA-PSO optimized controller improves the tracking accuracy and computing efficiency by 47.06%and 63.54%,respectively,and the optimized controller has strong robustness.