基于改进粒子群–退火算法的车辆横摆稳定性控制策略

Vehicle Yaw Stability Control Strategy Based on Improved Particle Swarm Optimization-Simulated Annealing Algorithm

摘要: 车辆的稳定性控制直接影响着驾驶员的驾驶体验与行车安全，是汽车安全领域的研究焦点。为了解决车辆在转向时对横摆稳定性控制的需求，本文引入分层控制架构，上层使用基于改进粒子群–退火算法(SA-PSO)的线性二次型调节器(LQR)来计算附加横摆力矩；下层力矩分配层通过有效集方法求解二次规划问题以获取最优的四轮力矩。改进粒子群–退火算法建立了惯性权重与退火温度之间的映射关系，同时引入Metropolis概率接受准则，并基于种群多样性动态触发随机扰动，显著提升了算法全局寻优能力与跳出局部最优的效率。仿真结果表明，所提出的SA-PSO-LQR方法在保持车辆稳定性的同时，有效改善了适应度函数的收敛性能与控制效果。

Abstract: Vehicle stability control directly affects the driving experience and safety of drivers, making it a research focus in the field of automotive safety. To address the need for yaw stability control during vehicle steering, this paper introduces a hierarchical control architecture. The upper layer employs a Linear Quadratic Regulator (LQR) based on an improved Simulated Annealing-Particle Swarm Optimization (SA-PSO) to calculate additional yaw moments. The lower moment distribution layer solves a quadratic programming problem using the active set method to obtain optimal four-wheel torque. The improved Simulated Annealing-Particle Swarm Optimization establishes a mapping relationship between inertia weight and annealing temperature, introduces the Metropolis probability acceptance criterion, and dynamically triggers random perturbations based on population diversity, significantly enhancing the algorithm’s global optimization capability and efficiency in escaping local optima. Simulation results show that the proposed SA-PSO-LQR method effectively improves the convergence performance and control effectiveness of the fitness function while maintaining vehicle stability.