摘要
提出一种基于梯度PSO算法的PID参数整定方法。结合PSO算法迭代中的梯度信息,对惯性权重适当调整,提高了收敛速度。在算法出现停滞时,对全局最优变异,避免陷入局部最优。并提出算法微粒运动轨迹收敛的充分条件。典型测试函数结果显示,该方法能有效改善微粒群算法的搜索性能。将采用该算法的PID控制器应用于一个二阶船舶控制装置,计算结果表明该PID控制器可以获得更好的控制性能指标,具有较好的使用价值。
A novel method of optimizing PID parameter by using gradient particle swarm optimization algorithm is put forward. This method can enhance the convergence rate by tuning the inertia weight based on analyzing the gradient information in iteration process. When the optimum information of the swarm is stagnant, the global best is mutated in its minus gradient direction to change the searching direction of the swarm and reduce the possibility of trapping in local optimum. The sufficient condition for the convergence of particle trajectories in this algorithm is presented. Typical function optimization problem results show that this method possesses good convergent performance. And searching optimal parameters of PID controller in a second-order ship control system verify that the proposed method can obtain more satisfied performance eriterion.
出处
《科学技术与工程》
2009年第9期2463-2467,共5页
Science Technology and Engineering
基金
上海市优秀青年基金项目(B-8101-09-0031)
上海海洋大学博士启动基金(A-2400-08-0296)资助
关键词
PID控制器
微粒群优化
梯度
参数优化
PID controller particle swarm optimization gradient,parameter optimization
