基于动力驱动微粒群算法的液压矫直机PID控制参数优化

为兼顾微粒群算法收敛速度与跳出局部解的能力,利用阶段性搜索方式将算法搜索过程分为前、后两个不同阶段。在算法的前期搜索阶段,当前微粒受个体最优微粒与全局最优微粒的引力作用,在算法的后期搜索阶段引入中值导向加速度,提出一种动力驱动微粒群算法。最后,针对液压矫直机PID控制的参数优化问题,考虑控制信号、上升时间和误差量的关系,建立液压矫直机PID控制参数优化模型,利用动力驱动微粒群算法优化得到更好的参数组合,实现PID控制参数优化。

PID Control Parameters Optimization Based on Power Driven Particle Swarm Algorithm for Hydraulic Straightener

 For giving consideration of both particle swarm optimization algorithm convergence speed and its ability to jump out of a local solution, by using a phased search method, we divide an algorithm search process into two different stages: front and back. In the front of the algorithm, the current particle is attracted by the individual optimal particle and the global optimal particle, and a median-oriented acceleration is introduced into the algorithms later search stage. A power driven particle swarm optimization algorithm is proposed. Finally, aimed at parameters optimization of PID control of hydraulic straightener, the relationship among control signal, rise time and error amount is considered. A PID control parameters optimization model is established, and a better combination of parameters is obtained by the proposed algorithm, and it realizes PID control parameters optimization.