改进粒子群算法在磁轴承中的研究

Study of improved particle swarm algorithm in magnetic bearing

针对主动磁悬浮轴承系统PID控制器参数设计问题,提出一种改进粒子群优化算法来实现PID参数的最优化。该算法以标准粒子群算法为基础,采用非线性自适应调节权值法以平衡算法的全局搜索和局部改良能力;同时采用带有动态扰动项的粒子速度更新公式,增加了粒子的随机性和多样性,帮助粒子在迭代后期跳出局部最优。并在MATLAB/Simulink中搭建系统模型进行仿真实验。仿真结果表明:与标准粒子群算法和遗传算法相比,该算法收敛到最优参数值的速度更快,PID控制系统的响应速度更快,具有更好的动态性能和稳态性能。

Aiming at the PID controller parameters of active magnetic bearing system, an improved particle swarm optimization algorithm is proposed to realize optimization of it. This algorithm is based on the particle swarm optimization, and adopts the method of nonlinear adaptive weight to balance the ability of the global search and local improvement. In addition, the velocity updating formula with dynamic disturbance is adopted to increase the randomness and diversity of the particles, which helps the particles to jump out of the local optimum at the end of the iteration. The system model is built to simulate the experiment in MATLAB/Simulink. The results show that compared with the particle swarm algorithm and genetic algorithm, this algorithm can faster converge to the optimal values, the PID controller can faster response and have better dynamic and steady -state performance.