首页|基于GWO-PSO算法的四旋翼飞行器LADRC参数整定

基于GWO-PSO算法的四旋翼飞行器LADRC参数整定

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针对线性自抗扰控制器参数整定繁琐的问题，提出了一种基于灰狼算法与粒子群算法联合改进(GWO-PSO)的控制器参数整定方法。将粒子群算法中的粒子种群替换为灰狼算法中的灰狼种群，提高其全局搜索能力;改进收敛因子使其以指数形式变化，提高其寻优精度;在绝对误差积分准则(ITAE)的基础上改进适应度函数，使其与超调量和系统误差相关联，并根据线性自抗扰控制器控制的四旋翼飞行器模型特点，将控制器参数ω0、ωc和b0作为改进算法中的种群进行整定。仿真结果表明，经改进算法优化的控制器，能够满足控制系统的动态性能和稳态性能要求。
LADRC Parameter Tuning for Quadcopter Based on GWO-PSO Algorithm
A controller parameter tuning method based on the joint improvement of Grey Wolf Optimization Algorithm and Particle Swarm OptimizationAlgorithm(GWO-PSO)is proposed for the problem that the parameter tuning of the Linear Active Disturbance Rejection Controller is too cumbersome.Replacing the particle population in the Particle Swarm OptimizationAlgorithm with the gray wolf population in the Grey Wolf Optimization Algorithm to improve its global search capability,and the convergence factor is improved so that it varies in an exponential form to improve its optimization finding accuracy.The adaptation function is improved based on the integral of absolute value of error criterion(ITAE)so that it is associated with the overshoot and the system error,and the controller parameters,ω0、ωc and b0 are tuned as populations in the improved algorithm according to the characteristics of the quadcopter model controlled by a linear active disturbance rejection controller.The simulation results show that the controller optimized by the improved algorithm can meet the dynamic performance and steady-state performance requirements of the control system.

linear active disturbance rejection controllerjoint improvement of Grey Wolf Optimization Algorithm and Particle Swarm Optimizationalgorithmparameter tuningquadcopter

赵志伟、赵犇、葛超、王蕾、么洪波

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华北理工大学电气工程学院,河北 唐山 063210

唐山学院人工智能学院,河北 唐山 063010

唐山学院智能与信息工程学院,河北 唐山 063010

线性自抗扰控制 灰狼算法与粒子群算法联合改进 参数整定 四旋翼飞行器

河北省自然科学基金唐山市人才基金资助项目

F2021209006A2021110015

2024

10.3969/j.issn.1002-0640.2024.06.019

火力与指挥控制
火力与指挥控制研究会,火力与指挥控制专业情报网

火力与指挥控制

CSTPCD北大核心
影响因子：0.312
ISSN：1002-0640
年,卷(期)：2024.49(6)