摘要
针对线性自抗扰器参数较多、协调整定较为困难的缺点。提出一种基于余弦递减函数的量子粒子群搜索算法,该算法将余弦递减函数与量子粒子群搜索算法粒子的更新公式相结合,利用余弦递减函数随迭代次数增加可变的特性,既保留了量子粒子群原有的全局寻优能力,又有效克服了其局部寻优能力差的缺点。将本文提出的算法应用于一类分数阶混沌系统的线性自抗扰控制器参数优化。仿真结果表明:优化后的线性自抗扰控制器能够很好的抑制超调、减小稳态误差并具有较强抗干扰能力。
Linear active disturbance rejection control has many parameters and it is difficult to obtain appropriate parameters. Therefore, a quantum-behaved particle swarm optimization algorithm based on cosine decreasing function is proposed in this article for searching optimal LADRC parameters. In this algorithm, cosine decreasing function and updated equation of quantum-behaved particle swarm optimization algorithm are combined. By using the variable characteristic of cosine decreasing function with the increase of iteration times, the original global optimization ability of quantum particle swarm optimization is retained and its poor local optimization ability is overcome. And the simulation results show that the LADRC optimized by COSQPSO can suppress overshoot, reduce steady-state error, and has strong anti-jamming ability.
作者
黄宇
谢天
武蕊
Huang Yu;Xie Tian;Wu Rui(Hebei Engineering Research Center of Simulation&Optimized Control for Power Generation,North China Electric Power University,Baoding 071003,China)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2019年第10期2093-2102,共10页
Journal of System Simulation
基金
中央高校基本科研业务费专项资金(2015MS66)
关键词
分数阶混沌系统
线性自抗扰控制
量子粒子群搜索算法
余弦递减函数
fractional-order chaotic system
linear ADRC
quantum-behaved particle swarm searching algorithm
cosine decreasing function
