Adaptive Particle Swarm Algorithm for the Parameters Tuning Offractional Order PID Controller

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Adaptive Particle Swarm Algorithm for the Parameters Tuning Offractional Order PID Controller

更新时间：2026-09-04

- Adaptive Particle Swarm Algorithm for the Parameters Tuning Offractional Order PID Controller
- Vol. 33, Issue 4, Pages: 108-112(2016)
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- Published：2016
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自适应粒子群优化分数阶PID控制器的参数整定[J]. 微电子学与计算机, 2016, 33(4): 108-112, 116.

Adaptive Particle Swarm Algorithm for the Parameters Tuning Offractional Order PID Controller[J]. 2016, 33(4): 108-112.
自适应粒子群优化分数阶PID控制器的参数整定[J]. 微电子学与计算机, 2016, 33(4): 108-112, 116. DOI：

Adaptive Particle Swarm Algorithm for the Parameters Tuning Offractional Order PID Controller[J]. 2016, 33(4): 108-112. DOI：

摘要

针对分数阶PID控制器参数整定较为复杂的问题

提出了一种自适应粒子群优化(PSO)方法

实现分数阶PID控制器参数的整定.在该算法中首先将待整定的控制器参数的粒子种群分成3个子群

并通过引入的粒子群聚集度因子和进化度因子

分别动态调整子群的惯性权值和规模

依据系统时域性能指标设定寻优目标函数

通过迭代计算来实现控制器参数的整定.最后将自适应粒子群优化分数阶PID控制器的方法分别应用于整数阶和分数阶被控系统进行时域性能仿真分析

实验结果表明提出的方法能够较好地提高控制系统的性能指标

并具有较强的抗干扰能力.

Abstract

An parameter adaptive tuning particle swarm optimization method is proposed for the complexity of fractional order controller parameter tuning.In this algorithm

the parameter population was divided into three subpopulations

and the aggregation degree factor and the evolution speed factor are introduced in this algorithm.The individuals number and inertia weight of the subpopulations were dynamically adjusted according to the evolution speed factor and aggregation degree factor respectively

and the parameters tuning is implemented through the iteration calculation of optimization objective function.Simulation and analysis are carried on with the integral order objects model and fractional order objects model.These results show that the proposed method can provide better performance in time domain for the system and is better in the capability of anti-jamming.

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