基于改进蜜獾优化算法的PID参数整定

doi:10.16180/j.cnki.issn1007-7820.2023.12.007

Abstract

Abstract:

As a swarm intelligence algorithm simulating the predator-prey behavior of honey badger, honey badger algorithm has many problems, such as easy to fall into local optimal solutions, and the number of iterations required. In view of the shortcomings of honey badger algorithm, a cloud honey badger algorithm (CHBA) combining gravity search algorithm and normal cloud technology is proposed. The density factor of the original honey badger algorithm that controls the individual search range of the honey badger is replaced by the acceleration in the gravitational search algorithm to improve the rationality of the individual search range of the honey badger and accelerate the search iteration speed. The normal cloud algorithm is used to generate a new batch of honey badgers with the expectation of the best position of the honey badger between generations, so as to improve the population diversity and avoid falling into local optimization. At the same time, the generation range of the new honey badger is adaptively adjusted to avoid local optimization. Twenty three benchmark functions are selected to test the proposed algorithm. From the optimization results of single peak, multi peak and fixed dimension multi peak functions, the step response PID(Proportion Integration Differentiation) parameters of first-order delay system, non minimum phase system and first-order minimum delay system are optimized and compared, and the results show that CHBA algorithm has better performance in search efficiency and iteration accuracy.

Key words: PID, honey badger algorithm, parameter setting, swarm intelligence algorithm, unstable object, Simulink simulation, intelligent algorithm, gravitational search algorithm

CLC Number:

TP273

HU Tao,JIANG Quan. PID Parameter Tuning Based on Improved Honey Badger Optimization Algorithm[J].Electronic Science and Technology, 2023, 36(12): 46-54.

Figures/Tables 14

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Table 1.

Table 2.

Table 3.

Table 4.

Figure 5.

Figure 6.

Table 5.

Figure 7.

Table 6.

Figure 8.

References 22

[1]	Mirjalili S, Lewis A. The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95(5):51-67. doi: 10.1016/j.advengsoft.2016.01.008
[2]	Ziegler J G, Nichols N B. Optimum settings for automatic controllers[J]. Transactions of American the Society of Mechanical Engineers, 1942, 64(4):759-768.
[3]	Cohen G H, Coon G A. Theoretical consideration of retarded control[J]. Transactions of American the Society of Mechanical Engineers, 1953, 75(5):827-833.
[4]	郝万君, 强文义, 胡林献, 等. 基于改进粒子群算法的PID参数优化与仿真[J]. 控制工程, 2006(5):429-432.
	Hao Wanjun, Qiang Wenyi, Hu Linxian, et al. Optimization and simulation of PID parameters based on improved particle swarm algorithms[J]. Control Engineering of China, 2006(5):429-432.
[5]	曾国辉, 杜涛, 黄勃, 等. 基于蚁狮优化算法分数阶PI的PMSM矢量控制[J]. 电力电子技术, 2021, 55(5):120-123,145.
	Zeng Guohui, Du Tao, Huang Bo, et al. Vector control of PMSM based on ant lion optimization algorithm of fractional-order PI[J]. Power Electronics, 2021, 55(5):120-123,145.
[6]	耿文波, 周子昂. 改进粒子群算法优化的BLDCM调速系统研究[J]. 控制工程, 2019, 26(9):1636-1641.
	Geng Wenbo, Zhou Ziang. Research on improved particle swarm optimization for BLDCM speed control system[J]. Control Engineering of China, 2019, 26(9):1636-1641.
[7]	Hashim F A, Houssein E H, Hussain K, et al. Honey b-adger algorithm:New metaheuristic algorithm for solving optimization problems[J]. Mathematics and Computers in Simulation, 2022, 192(2):84-110. doi: 10.1016/j.matcom.2021.08.013
[8]	Han E, Ghadimi N. Model identification of proton exc-hange membrane fuel cells based on a hybrid convolutional neural network and extreme learning machine optimized by improved honey badger algorithm[J]. Sustainable Energy Technologies and Assessments, 2022, 52(8):102-105.
[9]	Kapner D J, Cook T S, Adelberger E G, et al. Tests of the gravitational inverse-square law below the dark energy length scale[J]. Physical Review Letters, 2007, 98(2):211-221.
[10]	Esmat R, Hossein N, Saeid S. GSA:A gravitational search algorithm[J]. Information Sciences, 2009, 179(13):2232-2248. doi: 10.1016/j.ins.2009.03.004
[11]	单梁, 强浩, 李军, 等. 基于Tent映射的混沌优化算法[J]. 控制与决策, 2005(2):179-182.
	Shan Liang, Qiang Hao, Li Jun, et al. Chaotic optimization algorithm based on Tent map[J]. Control and Decision, 2005(2):179-182.
[12]	李德毅, 孟海军, 史雪梅. 隶属云和隶属云发生器[J]. 计算机研究与发展, 1995, 32(6):15-20.
	Li Deyi, Meng Haijun, Shi Xuemei. Membership clouds and membership cloud generators[J]. Journal of Computer Research and Development, 1995, 32(6):15-20.
[13]	Mirjalili S, Gandomi A H, Mirjalili S Z, et al. Salp swarm algorithm:A bio-inspired optimizer for engineering design problems[J]. Advances in Engineering Software, 2017, 114(12):163-191. doi: 10.1016/j.advengsoft.2017.07.002
[14]	Kennedy J, Eberhare R C. Particle swarm optimization[C]. Perth: Proceedings of IEEE International Conference on Neural Network, 1995:1942-1948.
[15]	Mirjalili S, Lewis A. The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95(2):51-67. doi: 10.1016/j.advengsoft.2016.01.008
[16]	Mirjalili S. The ant lion optimizer[J]. Advances in Engineering Software, 2015, 83(5):80-98. doi: 10.1016/j.advengsoft.2015.01.010
[17]	Li D Y, Liu C Y, Gan W Y. A new cognitive model:Cloud model[J]. International Journal of Intelligent Systems, 2009, 24(3):357-375. doi: 10.1002/int.v24:3
[18]	张铸, 张仕杰, 饶盛华, 等. 基于自适应正态云模型的引力樽海鞘群算法[J]. 控制与决策, 2022, 37(2):344-352.
	Zhang Zhu, Zhang Shijie, Rao Shenghua, et al. Gravity salp swarm algorithm based on adaptive normal cloud model[J]. Control and Decision, 2022, 37(2):344-352.
[19]	崔金玲, 吴迪. 基于正态云模型的自适应果蝇优化算法[J]. 河南理工大学学报(自然科学版), 2016, 35(5):697-705.
	Cui Jinling, Wu Di. Adaptive fruit fly optimization algorithm based on normal cloud model[J]. Journal of Henan Polytechnic University(Natural Science), 2016, 35(5):697-705.
[20]	Zhu M, Yang C, Li W. Autotuning algorithm of particle swarm PID parameter based on D-Tent chaotic model[J]. Journal of Systems Engineering and Electronics, 2013, 24(5):828-837. doi: 10.1109/JSEE.2013.00096
[21]	刘晨旻, 王亚刚. 基于连续空间的萤火虫算法改进[J]. 电子科技, 2022, 35(2):40-45.
	Liu Chenmin, Wang Yagang. Optimization of firefly al-gorithm based on continuous space[J]. Electronic Science and Technology, 2022, 35(2):40-45.
[22]	吴强, 张伟, 杨慧婷, 等. 基于天牛须算法的粒子群算法在PID参数整定上的应用[J]. 电子科技, 2020, 33(6):18-23.
	Wu Qiang, Zhang Wei, Yang Huiting, et al. Application of particle swarm optimization based on beetle antennae search algorithm in PID parameter tuning[J]. Electronic Science and Technology, 2020, 33(6):18-23.

函数	CHBA		HBA		SSA		PSO		WOA		ALO
函数	平均值	标准差	平均值	标准差	平均值	标准差	平均值	标准差	平均值	标准差	平均值	标准差
F₁	4.10×10^-212	7.60×10^-201	4.50×10^-125	2.40×10^-124	1.68×10^-4	3.67×10^-4	2.32×10^-3	4.47×10^-3	1.23×10^-62	6.16×10^-62	1.52×10^-1	3.72×10^-1
F₂	3.01×10^-99	1.65×10^-98	2.20×10^-67	7.12×10^-67	4.13×10⁰	3.20×10⁰	1.03×10^-1	1.47×10^-1	7.25×10^-47	2.93×10^-46	9.39×10²	4.81×10³
F₃	7.00×10^-198	4.80×10^-177	1.67×10^-86	8.90×10^-86	3.84×10³	1.70×10³	1.32×10²	4.45×10¹	5.72×10⁴	1.67×10⁴	7.89×10³	3.14×10³
F₄	8.29×10^-98	4.49×10^-97	2.06×10^-51	1.03×10^-50	1.75×10¹	4.30×10⁰	1.53×10⁰	2.96×10^-1	5.48×10¹	2.75×10¹	2.14×10¹	5.84×10⁰
F₅	2.50×10¹	1.77×10^-1	2.54×10¹	1.29×10⁰	5.24×10²	7.20×10²	1.01×10²	4.69×10¹	2.84×10¹	3.52×10^-1	7.29×10²	6.82×10²
F₆	8.25×10^-9	5.79×10^-9	1.50×10^-1	2.19×10^-1	2.56×10^-4	5.21×10^-4	1.78×10^-3	1.99×10^-3	8.52×10^-1	3.47×10^-1	6.30×10^-2	6.6×10^-2
F₇	3.47×10^-4	2.87×10^-4	6.62×10^-4	5.80×10^-4	2.67×10^-1	9.40×10^-2	2.63×10^-1	1.04×10^-1	5.01×10^-3	8.82×10^-3	5.32×10^-1	1.60×10^-1
结果	—		7/0/0		7/0/0		7/0/0		7/0/0		7/0/0

函数	CHBA		HBA		SSA		PSO		WOA		ALO
函数	平均值	标准差	平均值	标准差	平均值	标准差	平均值	标准差	平均值	标准差	平均值	标准差
F₈	-1.05×10⁴	9.95×10²	-8.60×10³	6.87×10²	-7.18×10³	8.20×10²	-4.34×10³	1.34×10³	-9.72×10³	1.79×10³	-5.69×10³	7.34×10²
F₉	0	0	0	0	6.42×10¹	2.28×10¹	6.30×10¹	1.76×10¹	0	0	8.95×10¹	3.02×10¹
F₁₀	8.88×10^-16	0	1.33×10⁰	5.06×10⁰	3.43×10⁰	1.1307	7.85×10^-1	6.47×10^-1	4.56×10^-15	3.30×10^-15	9.63×10⁰	3.66×10⁰
F₁₁	0	0	0	0	5.25×10^-2	3.33×10^-2	7.47×10^-3	9.25×10^-3	9.09×10⁰	4.97×10^-2	2.47×10^-1	1.37×10^-1
F₁₂	6.91×10^-3	2.63×10^-2	3.72×10^-3	3.64×10^-3	1.04×10¹	4.33×10⁰	2.43×10^-2	8.01×10^-2	4.23×10^-2	2.12×10^-2	1.88×10¹	8.78×10⁰
F₁₃	1.19×10^-2	2.33×10^-2	7.53×10^-1	3.16×10^-1	2.76×10¹	1.63×10¹	9.07×10^-3	1.13×10^-2	9.32×10^-1	3.18×10^-1	4.89×10¹	1.47×10¹
结果	—		3/3/0		6/0/0		6/0/0		6/0/0		6/0/0

函数	CHBA		HBA		SSA		PSO		WOA		ALO
函数	平均值	标准差	平均值	标准差	平均值	标准差	平均值	标准差	平均值	标准差	平均值	标准差
F₁₄	9.98×10^-1	0	9.98×10^-1	0	9.98×10^-1	2.38×10^-16	9.98×10^-1	1.37×10^-10	2.56×10⁰	2.65×10⁰	2.13×10⁰	2.34×10^-1
F₁₅	3.26×10^-5	8.98×10^-6	3.58×10^-3	5.32×10^-3	8.90×10^-4	2.77×10^-5	3.25×10^-3	7.25×10^-3	5.75×10^-5	3.81×10^-4	5.12×10^-4	1.56×10^-4
F₁₆	-1.03×10⁰	5.15×10^-16	-1.03×10⁰	1.25×10^-14	-1.03×10⁰	1.48×10^-8	-1.03×10⁰	7.12×10^-16	-1.03×10⁰	5.23×10^-7	-1.03×10⁰	2.58×10^-12
F₁₇	3.97×10^-1	0	3.97×10^-1	0	3.97×10^-1	7.52×10^-15	3.97×10^-1	0	3.97×10^-1	2.99×10^-5	3.97×10^-1	1.97×10^-13
F₁₈	3.00×10⁰	2.47×10^-15	4.8×10⁰	6.8501	3.00×10⁰	3.65×10^-13	3.00×10⁰	1.96×10^-15	3.00×10⁰	1.21×10^-4	3.00×10⁰	9.32×10^-13
F₁₉	-3.86×10⁰	2.54×10^-15	-3.75×10⁰	2.67×10^-1	-3.86×10⁰	2.11×10^-7	-3.86×10⁰	2.58×10^-15	-3.84×10⁰	3.04×10^-2	-3.86×10⁰	5.86×10^-12
F₂₀	-3.27×10⁰	6.03×10^-2	-3.17×10⁰	3.05×10^-1	-3.24×10⁰	7.04×10^-2	-3.26×10⁰	6.70×10^-2	-3.21×10⁰	1.20×10^-1	-3.26×10⁰	6.11×10^-2
F₂₁	-9.55×10⁰	2.05×10⁰	-9.34×10⁰	2.49×10⁰	-7.40×10⁰	3.50×10⁰	-6.89×10⁰	3.43×10⁰	-7.61×10⁰	2.49×10⁰	-5.60×10⁰	2.23×10⁰
F₂₂	-8.88×10⁰	3.13×10⁰	-8.48×10⁰	3.30×10⁰	-8.04×10⁰	3.44×10⁰	-8.16×10⁰	3.05×10⁰	-6.90×10⁰	2.92×10⁰	-6.59×10⁰	3.48×10⁰
F₂₃	-6.56×10⁰	4.07×10⁰	-8.11×10⁰	3.51×10⁰	-8.09×10⁰	3.57×10⁰	-8.34×10⁰	3.44×10⁰	-5.95×10⁰	2.92×10⁰	-5.86×10⁰	3.71×10⁰
结果	—		7/2/1		8/1/1		8/1/1		9/1/0		10/0/0

整定方法	K_P	K_I	K_D	ITAE
CHBA	1.020 8	0.755 8	0.303 2	1.089 0
HBA	0.654 7	0.538 0	0.008 3	2.358 3
SSA	1.311 4	1.188 8	0.458 7	1.654 2
PSO	1.113 0	0.834 5	0.379 1	1.175 8
WOA	0	0.312 1	0	11.713 7
ALO	1.035 8	0.763 1	0.312 6	1.089 9

整定方法	K_P	K_I	K_D	ITAE
CHBA	1.072 9	0.414 8	0.908 5	3.585 0
HBA	1.489 9	0	0	46.946 7
SSA	0.818 9	0.327 4	0.550 3	5.016 8
PSO	1.143 0	0.407 2	1.003 5	3.778 1
WOA	0	0.273 3	2.272 8	25.865 3
ALO	1.075 6	0.416 6	0.915 0	3.585 4

PID Parameter Tuning Based on Improved Honey Badger Optimization Algorithm

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 14

References 22

Related Articles 15

Metrics

Comments

Recommended 0

[1]	DING Xiangxin,ZHANG Wei,WANG Yagang. PID Controller Tuning of Time Delay Integral System Based on Multi Dominant Pole Method [J]. Electronic Science and Technology, 2023, 36(8): 56-64.
[2]	YUAN Zhenbo,BAI Bo,ZHANG Xiaowei,LUO Liujun,SHANG Tao. Automatic Alignment System for Visible Light Communication Based on Image Processing [J]. Electronic Science and Technology, 2023, 36(6): 8-15.
[3]	ZHU Mingji,ZHANG Huilin,MA Lixin. Control Strategy of Ammonia Desulfurization Based on Smith Predictive Fuzzy PID [J]. Electronic Science and Technology, 2023, 36(5): 80-87.
[4]	LU Dongxiang. Research Progress of Node Assignment Optimization Strategy in Road Traffic Network [J]. Electronic Science and Technology, 2023, 36(3): 81-86.
[5]	XU Bangxian,LIU Xiaobo,HAN Xiangmin,QIU Zhi,TANG Hui,FAN Jinwei. Parameter Optimization Design Method of Coupled Inductor Zeta Converter [J]. Electronic Science and Technology, 2023, 36(1): 88-94.
[6]	WANG Peiyu,MA Lixin. Research on Permanent Magnet Synchronous Motor Servo System Based on Fuzzy Neural Network [J]. Electronic Science and Technology, 2022, 35(6): 83-88.
[7]	LI Meng,MA Lixin. Research on Predictive Ammonia Desulfurization Control System [J]. Electronic Science and Technology, 2022, 35(5): 60-65.
[8]	REN Xinrui,MA Lixin. Research on Main Steam Temperature Control System Based on Fuzzy PID Load Tracking [J]. Electronic Science and Technology, 2021, 34(5): 18-23.
[9]	HE Zhonglin,ZHOU Ping. Research on Sorting of Retired Lithium Battery Based on Radial Basis Function Neural Network [J]. Electronic Science and Technology, 2021, 34(2): 38-44.
[10]	LI Lin,XIE Ming,HUANG Song. Design of Modular Parallel Current Sharing Single-Phase Inverter [J]. Electronic Science and Technology, 2021, 34(10): 27-31.
[11]	GUO Jie,JIN Hai,SHEN Xinge. Optimization Control of Quadrotor UAVs Based on Neural Network PID Algorithm [J]. Electronic Science and Technology, 2021, 34(10): 51-55.
[12]	WANG Jiaren,WANG Yagang. Research on Complex Control Algorithms for Non-Minimum Phase Systems [J]. Electronic Science and Technology, 2021, 34(1): 55-59.
[13]	WU Qiang,ZHANG Wei,YANG Huiting,WANG Chaoying. Application of Particle Swarm Optimization Based on Beetle Antennae Search Algorithm in PID Parameter Tuning [J]. Electronic Science and Technology, 2020, 33(6): 18-23.
[14]	CHEN Mingfang,ZANG Jiaxiu,ZHANG Yongxia. Research on A Multi-area Air-cooled Precise Temperature Control System with High and Low Temperature [J]. Electronic Science and Technology, 2020, 33(6): 46-51.
[15]	SUN Ruifang,ZHANG Xiaolong,LIANG Wenkai,XIE Xiaoquan. Speed Feedback and Incomplete Differential PID Control of Pneumatic Proportional Position System [J]. Electronic Science and Technology, 2020, 33(4): 65-70.