基于改进自抗扰控制的电动伺服系统机械谐振抑制方法

doi:10.12305/j.issn.1001-506X.2024.01.35

摘要/Abstract

摘要：

导弹发射和飞行过程中, 电动伺服系统受发动机点火、气动负载、舵面冲击等载荷作用下, 加大结构的磨损, 导致系统结构间隙的存在, 极大地降低了传动刚度, 并加剧谐振带来的危害。本文提出改进自抗扰控制器(active disturbance rejection controller, ADRC)以改善电动伺服系统的位置跟踪特性。首先分析了电动伺服系统的机械谐振机理, 设计了线性自抗扰控制器, 利用改进粒子群优化(particle swarm optimization, PSO)算法进行控制参数寻优, 并进行了仿真分析验证了该方法的可行性, 最后电动伺服系统试验结果验证了改进ADRC的有效性, 提高电动伺服系统的动态和稳态性能。

关键词: 电动伺服系统, 谐振, 自抗扰控制器, 改进粒子群优化算法

Abstract:

During missile launch and flight, the electric servo system is subjected to loads such as engine ignition, aerodynamic loods, and rudder impact. Increase of structural wear leads to the structural gaps in the system. It greatly reduces the transmission stiffness and aggravates the harm caused by resonance. This paper proposes an improved active disturbance rejection controller (ADRC) to improve the tracking performance of the electric servo system. First, the mechanism of mechanical resonance for the electric servo system is analyzed. Then, the linear ADRC is described and the method to select parameters is given by improved particle swarm optimization (PSO) algorithm. Furthermore, the feasibility of this controller is demonstrated through a simulation analysis. Finally, the experimental results of electric servo system indicate that the improved ADRC can improve stability and dynamic performance.

Key words: electric servo system, resonance, active disturbance rejection controller (ADRC), improved particle swarm optimization (PSO)

中图分类号:

V249.1

莫昱, 唐旭东. 基于改进自抗扰控制的电动伺服系统机械谐振抑制方法[J]. 系统工程与电子技术, 2023, 46(1): 309-317.

Yu MO, Xudong TANG. Research on mechanical resonance suppression of electric servo system based on an improved ADRC[J]. Systems Engineering and Electronics, 2023, 46(1): 309-317.

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参考文献 27

1	徐永壮, 何华锋, 何耀民. 某导弹双摆喷管电动伺服系统运动分析及建模仿真[J]. 电光与控制, 2019, 26 (12): 74- 79. doi: 10.3969/j.issn.1671-637X.2019.12.015
	XU Y Z , HE H F , HE Y M . Motion analysis modeling and electric servo system with double pendulum nozzle for a certain type of missile[J]. Electronics Optics & Control, 2019, 26 (12): 74- 79. doi: 10.3969/j.issn.1671-637X.2019.12.015
2	栾龙. 电动伺服系统自抗扰控制系统设计[D]. 天津: 天津大学, 2019.
	LUAN L. Design of electromechanical actuator system based on active disturbance rejection control[D]. Tianjin: Tianjin University, 2019.
3	周志明, 林凡, 姚晓先, 等. 电动伺服系统机限环机理分析和抑制措施[J]. 系统工程与电子技术, 2021, 43 (3): 773- 778.
	ZHOU Z M , LIN F , YAO X X , et al. Analysis and suppression of the limit cycle about electric actuator[J]. Systems Engineering and Electronics, 2021, 43 (3): 773- 778.
4	KIM J , WHANG I H . Augmented three-loop autopilot structure based on mixed-sensitivity optimization[J]. Journal of Guidance, Control, and Dynamics, 2018, 43 (3): 748- 753.
5	杨明, 郝亮, 徐殿先. 双惯量弹性负载系统机械谐振机理分析及谐振特性快速辨识[J]. 电机与控制学报, 2016, 20 (4): 112- 120.
	YANG M , HAO L , XU D X . Analysis of mechanical resonance mechanism and fast identification of resonance characteristic for 2-mass system with elastic load[J]. Electric Machines and Control, 2016, 20 (4): 112- 120.
6	MURPHY B R , WDTANABLE I . Digital shaping filters for reducing machine vibration[J]. IEEE Trans. on Robotics and Automation, 1992, 8 (2): 289- 289.
7	巩凤娇. 伺服系统中抑制机械谐振方法的研究[D]. 北京: 北京理工大学, 2016.
	GONG F J. Research on mechanical resonance suppression for servo systems[D]. Beijing: Beijing Institute of Technology, 2016.
8	李家骏, 王淦泉. 滑模观测器在大惯量扫描镜系统中抗谐振的应用[J]. 控制理论与应用, 2020, 37 (12): 2638- 2644. doi: 10.7641/CTA.2020.90928
	LI J J , WANG G Q . Application of sliding mode observer in large inertia scanning mirror resonance system[J]. Control Theory & Applications, 2020, 37 (12): 2638- 2644. doi: 10.7641/CTA.2020.90928
9	蔡佳秀. 交流伺服系统谐振抑制与多轴运动控制方法研究[D]. 南京: 东南大学, 2019.
	CAI J X. AC servo system for resonance suppression and multi-axis motion control method research[D]. Nanjing: Southeast University, 2019.
10	刘铠源, 杨明. 基于改进有源阻尼的柔性关节机械臂伺服系统振动抑制策略[J]. 电机与控制学报, 2020, 26 (7): 20- 28.
	LIU K Y , YANG M . Vibration suppression strategy for manipulator servo with flexible joints based on improved active damping[J]. Electric Machines and Control, 2020, 26 (7): 20- 28.
11	CHEN Y Y , YANG M , LONG J , et al. Analysis of oscillation frequency deviation in elastic coupling digital drive system and robust notch filter strategy[J]. IEEE Trans. on Industrial Electronics, 2019, 66 (1): 90- 101. doi: 10.1109/TIE.2018.2825300
12	杨影, 张杰鸣, 徐国卿. 转速负反馈在伺服系统机械谐振抑制中的应用研究[J]. 电工技术学报, 2018, 33 (23): 5459- 5469.
	YANG Y , ZHANG J M , XU G Q . Application research on speed negative feedback in mechanical resonance suppression in servo system[J]. Transaction of China Eletrotechnical Society, 2018, 33 (23): 5459- 5469.
13	刘伟华, 包晓军, 刘远曦, 等. 雷达伺服驱动机械传动系统谐振分析[J]. 机电工程技术, 2022, 51 (1): 46- 49.
	LIU W H , BAO X J , LIU Y X , et al. Resonance analysis on mechanical transmission system of servo drive of radar[J]. Mechanical & Electrical Engineering Technology, 2022, 51 (1): 46- 49.
14	邓二凡. 基于自抗扰控制技术的永磁交流伺服系统谐振抑制研究[D]. 广州: 华南理工大学, 2019.
	DENG E F. Research on resonance suppression of permanent magnet AC servo system based on active disturbance rejection control[D]. Guangzhou: South China University of Technology, 2019.
15	QU L Z , QIAO W , QU L Y . Active-disturbance-rejection-based sliding-mode current control for permanent-magnet synchronous motors[J]. IEEE Trans. on Power Electronics, 2020, 36 (1): 751- 760.
16	ZHAO Y , LIU Y , MA D . Output regulation for switched systems with multiple disturbances[J]. IEEE Trans. on Circuits and Systems Ⅰ: Regular Papers, 2020, 67 (12): 5326- 5335. doi: 10.1109/TCSI.2020.3013627
17	GUO H Y , CAO D P , CHEN H , et al. Model predictive path following control for autonomous cars considering a measurable disturbance: implementation, testing, and verification[J]. Mechanical Systems and Signal Processing, 2019, 118, 41- 60. doi: 10.1016/j.ymssp.2018.08.028
18	MURAMATSU H , KATSURA S . An adaptive periodic-disturbance observer for periodic-disturbance suppression[J]. IEEE Trans. on Industrial Informatics, 2018, 14 (10): 4446- 4456. doi: 10.1109/TII.2018.2804338
19	刘小波, 阮文华, 齐治国, 等. 折叠舵外翼气动特性研究[J]. 空气动力学学报, 2021, 39 (1): 112- 120.
	LIU X B , RUAN W H , QI Z G . Study on aerodynamics of a fol-ding wing[J]. Acta Aerodynamica Sinica, 2021, 39 (1): 112- 120.
20	龚文全, 曾岳南, 黄之锋, 等. 基于改进型陷波器的伺服系统谐振抑制研究[J]. 机电工程, 2020, 37 (7): 845- 850. doi: 10.3969/j.issn.1001-4551.2020.07.021
	GONG W Q , ZENG Y N , HUANG Z F , et al. Resonance suppression of servo system based on improved notch filter[J]. Journal of Mechanical & Electrical Engineering, 2020, 37 (7): 845- 850. doi: 10.3969/j.issn.1001-4551.2020.07.021
21	YADAV S , EKBAL A , SAHA S . Information theoretic-PSO-based feature selection: an application in biomedical entity extraction[J]. Knowledge and Information Systems, 2019, 60 (3): 1453- 1478. doi: 10.1007/s10115-018-1265-z
22	HUDA R K , BANKA H . Efficient feature selection and classification algorithm based on PSO and rough sets[J]. Neural Computing and Applications, 2019, 31, 4287- 4303. doi: 10.1007/s00521-017-3317-9
23	NGUYEN H B , XUE B , LIU I , et al. New mechanism for archive maintenance in PSO-based multi-objective feature selection[J]. Soft Computing, 2016, 20 (10): 3927- 3946. doi: 10.1007/s00500-016-2128-8
24	DESAI J, NGUYEN B H, XUE B. Multi-label feature selection using particle swarm optimization: novel local search mechanisms[C]//Proc. of the IEEE Symposium Series on Computational Intelligence, 2020: 1762-1769.
25	HUA Z Y , ZHOU Y C . Exponential chaotic model for generating robust chaos[J]. IEEE Trans. on Systems, Man, and Cybernetics: Systems, 2021, 51 (6): 3713- 3724.
26	陈志刚, 梁涤青, 邓小鸿, 等. Logistic混沌映射性能分析与改进[J]. 电子与信息学报, 2016, 38 (6): 1547- 1551.
	CHEN Z G , LIANG D Q , DENG X H , et al. Performance analysis and improvement of Logistic chaotic mapping[J]. Journal of Electronics & Information Technology, 2016, 38 (6): 1547- 1551.
27	IVONA B , PREDRAG S . An improved chaotic firefly algorithm for global numerical optimization[J]. International Journal of Computational Intelligence Systems, 2018, 12 (1): 131- 138.

函数名称	数学公式	理论最优解	搜索区域	迭代代数	收敛条件
Sphere	$f_1(x)=\sum\limits_{i=0}^D \boldsymbol{x}_i^2$	0	[-100, 100]	100	1.0e-4
Rastrigrin	$f_3(x)=\sum\limits_{i=0}^D\left[\boldsymbol{x}_i-10 \cos \left(2 {\rm{\mathsf{π}}} \boldsymbol{x}_i\right)+10\right]$	0	[-5.12, 5.12]	100	1.0e-4

函数	维数	平均迭代次数	平均最优适应值	最优适应值
改进 PSO	10	68	1.59e-07	8.17e-09
	20	183	3.13e-06	3.48e-07
	30	362	1.44e-05	2.61e-06
基本 PSO	10	70	7.35e-06	4.13e-10
	20	-	0.010 2	0.001 3
	30	-	0.036 8	0.014 6

函数	维数	平均迭代次数	平均最优适应值	最优适应值
改进 PSO	10	82.2	2.60e-06	0.00e+00
	20	200	3.27e-06	7.71e-08
	30	324	8.91e-05	3.07e-08
基本 PSO	10	105	4.50e-04	6.74e-10
	20	-	1.969 6	0.276 1
	30	-	7.969 1	1.939 9

分析项目	迭代0次	迭代50次
带宽/Hz	33	45.5
上升时间/ms	28.3	22
超调量/%	14.7	5.3
最大速度/((°)/s)	618	774

频率范围/Hz	过载或振幅
10~36	±0.75 mm
36~150	4 g
150~400	6 g
400~2 000	8 g