【摘要】 针对二自由度主动悬架比例-积分-微分(PID)控制器参数整定问题，引入粒子群算法，借助粒子群算法的全局搜索能力解决PID控制器参数整定问题，考虑到传统粒子群算法收敛速度较慢，设计了一种改进粒子群算法，根据悬架性能评价指标建立目标函数，分别模拟了随机路面激励输入和减速带式梯形冲击路面激励输入，并验证了基于改进粒子群算法优化的PID控制器的有效性。仿真结果表明：改进粒子群算法后目标函数的收敛速度明显提高；基于改进粒子群算法优化PID控制的主动悬架在不同激励输入条件下均具有较好的行驶平顺性；验证了改进粒子群算法的有效性并解决了PID控制器参数整定问题。更多还原

【Abstract】 A particle swarm algorithm is introduced for the parameter adjustment problem of 2-degree-of-freedom active suspension proportional-integral-derivative(PID)controller, and the parameter adjustment problem of PID controller is solved by the global search ability of particle swarm algorithm.The objective function is established according to the evaluation index of the suspension performance, and the random road excitation input and the speed bump trapezoidal impact road excitation input are simulated and the effectiveness of the PID controller optimized based on the improved particle swarm algorithm is verified.The simulation results show that the convergence speed of the objective function is significantly improved after the particle swarm algorithm is improved, and the active suspension with optimized PID control based on the improved particle swarm algorithm has better handling smoothness under different excitation input conditions, thus verifying the effectiveness of the improved particle swarm algorithm and solving the parameter tuning problem of the PID controller.更多还原