基于改进粒子群算法的快速反射镜自抗扰控制

doi:10.12305/j.issn.1001-506X.2021.12.31

摘要/Abstract

摘要：

为了提高快速反射镜(fast steering mirror, FSM)的跟踪精度和扰动抑制能力, 建立了基于自抗扰控制器(active disturbance rejection control, ADRC)的FSM闭环控制系统, 并对ADRC的参数整定方法进行研究。由于ADRC参数用试凑法整定效率低, 用粒子群优化(particle swarm optimization, PSO)算法整定容易陷入局部最优, 因此提出一种惯性权重基于箕舌线调整的改进PSO(improved PSO, IPSO)算法。通过系统仿真实验, 在考虑卫星平台振动的情况下, 采用基于IPSO、PSO的ADRC和传统比例-积分-微分(proportion integration differentiation，PID)控制器分别控制FSM。结果表明, 在跟踪不同频率的高频正弦信号时, 采用基于IPSO的ADRC控制的FSM跟踪精度相较其他两种控制器控制FSM的跟踪精度均有明显提升。

关键词: 快速反射镜, 自抗扰控制, 粒子群, 惯性权重, 箕舌线函数

Abstract:

In order to improve the tracking accuracy and disturbance suppression ability of fast steering mirror (FSM), a FSM closed-loop control system based on active disturbance rejection control (ADRC) is established, and the parameter setting method of ADRC is studied. Because the setting efficiency of ADRC parameters by trial and error method is low, and it is easy to fall into local optimization by particle swarm optimization (PSO), an improved PSO (IPSO) algorithm based on skip tongue line adjustment of inertia weight is proposed. Through the system simulation experiment, considering the vibration of satellite platform, ADRC and traditional proportion integration differentiation (PID) controller based on IPSO and PSO are used to control FSM respectively. The results show that when tracking high-frequency sinusoidal signals with different frequencies, the tracking accuracy of FSM controlled by ADRC based on IPSO is significantly improved compared with the other two controllers.

Key words: fast steering mirror, active disturbance rejection control (ADRC), particle swarm, inertial weight, versoria function

中图分类号:

TP273
TP18

冯建鑫, 王雅雷, 王强, 胥彪. 基于改进粒子群算法的快速反射镜自抗扰控制[J]. 系统工程与电子技术, 2021, 43(12): 3675-3682.

Jinxin FENG, Yalei WANG, Qiang WANG, Biao XU. Fast steering mirror ADRC based on improved particle swarm optimizer[J]. Systems Engineering and Electronics, 2021, 43(12): 3675-3682.

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函数名	理论最小值	平均值				最小值
函数名	理论最小值	IPSO	PSO	DA	GA	IPSO	PSO	DA	GA
Rastrigion	0	9.894 9×10^-10	0.079 8	0.265 4	0.001 4	0	9.851 2×10^-6	0	3.438 4×10^-8
Schaffer	0	0.001 7	0.004 9	0.005 5	0.001 5	0	8.619 4×10^-6	0	5.605 5×10^-12
Rosenbrock	0	0.007 8	0.617 1	3.097 5	0.113 2	1.486 1×10^-15	0.005 5	8.803 8×10^-4	0.001 2
Sphere	0	0	3.924 6×10^-5	0	5.635 8×10^-6	0	1.944 0×10^-7	0	2.548 4×10^-9
Beale	0	2.506 5×10^-16	1.707 6×10^-4	0.069 3	0.002 3	0	3.306 1×10^-8	0	5.449 8×10^-10

算法	β₀₁	β₀₂	β₀₃	β₁	β₂	minfit
IPSO	14 509.755 4	2.233 1×10⁸	8.117 5×10¹¹	97.045 6	0.081 6	1.143 8×10^-9
PSO	10 688.412 9	1.728 3×10⁸	9.554 8×10¹¹	135.420 0	0.120 0	3.347 9×10^-9

算法	100 Hz	150 Hz	200 Hz
IPSO-ADRC	0.085	0.616	2.129
PSO-ADRC	0.779	2.624	6.384
PID	3.109	6.991	12.503

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