动态步长FOA的PID参数整定方法研究

Research on PID Parameter Adjustment Method with Dynamic Step FOA

为了解决基本果蝇优化算法(FOA)因固定搜索步长而对比例积分微分(PID)参数整定收敛精度不高且搜寻效率低的问题,将Logistic(t)的变换函数lgt(t)引入FOA中。由该变换函数确定自适应步长,提出一种动态步长果蝇优化算法(DSFOA)。DSFOA中果蝇个体搜索步长会随着迭代次数的增加而动态地变化。该算法在迭代前期使用大步长,具有更高的全局搜索效率;在迭代后期使用小步长,具有较强的局部寻优能力。这可以提高收敛精度,实现对全局搜索和局部搜索过程的优化。二阶系统仿真测试结果表明,相比于FOA,DSFOA寻优过程产生的PID参数使系统性能更优,能快速、有效地搜索到PID最优参数且鲁棒性好。该结果验证了DSFOA的有效性与合理性。

To solve the problem that the basic fruit fly optimization algorithm(FOA)has low convergence accuracy and low search efficiency of proportional integral differential(PID)parameter tuning due to the fixed search step,the transformation function of lgt(t)of Logistic(t)is introduced into the FOA.In dynamic step fruit fly optimization algorithm(DSFOA),the search step length of individual fruit flies’changes dynamically as the number of iterations increases.The algorithm is to use large step size in the early iterations with higher global search efficiency and small step size in the late iterations with stronger local search capability.This can improve the convergence accuracy and realize the optimization of both global search and local search processes.The second-order system simulation test results show that the PID parameters generated by the DSFOA search process result in better system performance compared to FOA,which can search for the optimal PID parameters quickly and efficiently with good robustness.The results validate the effectiveness and reasonableness of DSFOA.