基于鲁棒约束的PI控制器参数多目标优化及应用

doi:10.3969/j.issn.1674-1951.2021.05.001

摘要/Abstract

摘要：

比例-积分（PI）控制器结构简单、易于实施,但其参数不能保证系统取得满意的控制效果,为提升系统运行品质,提出了一种基于鲁棒约束的PI控制器参数多目标优化方法。采取最大灵敏度函数作为鲁棒约束指标,将闭环系统的跟踪性能与抗干扰性能同时作为优化目标,采用多目标遗传算法在PI控制器稳定域内对参数进行优化,得到满足鲁棒约束的Pareto最优解集,借助水平图法得到优化参数。采用该方法优化后的PI控制器参数在山东潍坊某电厂^#3高压加热器中投入应用,运行数据表明,在更大的负荷范围内,高压加热器水位波动范围只有原来的61.89%,具有很好的工业现场应用潜力。

关键词: 比例-积分控制器, 鲁棒约束, 最大灵敏度函数, 多目标遗传算法, 稳定域, 高压加热器

Abstract:

Though a proportional-integral （PI） controller is of simple structure and good operability,its parameters can barely satisfy the control on the controller. To improve its operation quality, a multi-objective optimization method for PI controller parameters under robustness constraint is proposed. Taking maximum sensitivity function as the robustness constraint index and setting the tracking performance and anti-interference as optimization objectives simultaneously, the method can get Pareto optimal solution satisfying the robustness constraint after optimizing PI controller parameters in their stable regions by multi-objective algorithm. Then, the parameters are optimized by level diagrams. PI controller parameters of No.3 high-pressure heater of a power plant in Weifang, Shandong Province were optimized by this method. The test results show that,taking the proposed method,the fluctuation of high-pressure heater water level is about 61.89% of the original one only, which indicates the promising application prospect of this method on industrial site.

Key words: PI controller, robustness constraint, maximum sensitivity function, multi-objective genetic algorithm, stability region, high pressure heater

中图分类号:

TP13：TM621：TK264.9

荆立坤, 唐宜强, 潘凤萍, 吴振龙. 基于鲁棒约束的PI控制器参数多目标优化及应用[J]. 华电技术, 2021, 43(5): 1-8.

JING Likun, TANG Yiqiang, PAN Fengping, WU Zhenlong. Multi-objective optimization of PI controller parameters under robustness constraint and its application[J]. Huadian Technology, 2021, 43(5): 1-8.

图/表 19

图1

图2

图3

图4

图5

图6

图7

图8

图9

图10

图11

表1

标称系统性能指标统计

控制器参数	$J sp$	$J id$
现场参数	154.26	154.26
优化参数	21.25	8.14
SIMC	23.26	8.98
MIGO	20.52	59.12

表1

图12

图13

图14

图15

图16

图17

表2

参考文献 26

[1]	ASTROM K J, KUMAR P R. Control: A perspective[J]. Automatica, 2014,50(1):3-43. doi: 10.1016/j.automatica.2013.10.012
[2]	SUN L, LI D H, LEE K Y. Optimal disturbance rejection for PI controller with constraints on relative delay margin[J]. ISA Transactions, 2016,63:103-111. doi: 10.1016/j.isatra.2016.03.014
[3]	WU Z L, YUAN J, LI D H, et al. The PI controller design based on a Smith-like predictor for a class of high order systems[J]. Transactions of the Institute of Measurement and Control, 2020,43(4):875-890. doi: 10.1177/0142331220944627
[4]	HUSSAIN M N, MELATH G, AGARWAL V. An active damping technique for PI and predictive controllers of an interlinking converter in an islanded hybrid microgrid[J]. IEEE Transactions on Power Electronics, 2020,36(5):5521-5529. doi: 10.1109/TPEL.63
[5]	GHOSH S, PAN S. Centralized PI controller design method for MIMO processes based on frequency response approximation[J]. ISA Transactions, 2021,110:117-128. doi: 10.1016/j.isatra.2020.10.041
[6]	BARREAU M, GOUAISBAUT F, SEURET A. Practical stability analysis of a drilling pipe under friction with a PI controller[J]. IEEE Transactions on Control Systems Technology, 2021,29(2):620-634. doi: 10.1109/TCST.87
[7]	WANG W S, LIU C H, LIU S Y, et al. Current harmonic suppression for permanent-magnet synchronous motor based on Chebyshev filter and PI controller[J]. IEEE Transactions on Magnetics, 2021,57(2):1-6. doi: 10.1109/TMAG.20
[8]	胡瑞玲. 协调控制系统的增益调度优化控制[J]. 控制工程, 2020,27(7):1216-1222.
	HU Ruiling. Optimal control based on gain scheduling for coordinated control system[J]. Control Engineering of China, 2020,27(7):1216-1222.
[9]	焦健. 基于史密斯预估补偿及自整定PID的过热汽温控制方案[J]. 山西电力, 2020(2):41-44.
	JIAO Jian. The control scheme of overheating steam temperature based on Smith predictive compensation and self-adjusting PID[J]. Shanxi Electric Power, 2020(2):41-44.
[10]	孙金龙. 330 MW 燃煤火电机组脱硝系统的优化研究[J]. 发电技术, 2019,40(6):570-579.
	SUN Jinlong. Optimization study of denitrification system for 330 MW coal-fired thermal power plant[J]. Power Generation Technology, 2019,40(6):570-579.
[11]	林青. SCR脱硝系统烟气流场分布研究及优化控制[J]. 华电技术, 2019,41(12):58-62.
	LIN Qing. Study on the distribution and optimization control of SCR denitration system flue gas flow field[J]. Huadian Technology, 2019,41(12):58-62.
[12]	李峰, 姜文堂, 史忠军, 等. 300 MW燃煤机组SCR烟气脱硝喷氨自动控制策略优化[J]. 华电技术, 2018,40(4):67-69.
	LI Feng, JIANG Wentang, SHI Zhongjun, et al. The optimization of automatic control strategy of 300 MW coal fired units SCR flue gas denitrification and ammonia injection[J]. Huadian Technology, 2018,40(4):67-69.
[13]	SKOGESTAD S. Simple analytic rules for model reduction and PID controller tuning[J]. Journal of Process Control, 2003,13:291-309. doi: 10.1016/S0959-1524(02)00062-8
[14]	ZIEGLER J G, NICHOLS N B. Optimum settings for automatic controllers[J]. Journal of Dynamic Systems Measurement and Control, 1942,64:759-765.
[15]	HAGGLUND T, ASTROM K J. Revisiting the Ziegler-Nichols tuning rules for PI control[J]. Asian Journal of Control, 2002,4:364-380. doi: 10.1111/asjc.2002.4.issue-4
[16]	WANG C F, LI D H. Decentralized PID controllers based on probabilistic robustness[J]. Journal of Dynamic Systems, Measurement, and Control, 2011,133(6):1-8.
[17]	SRIVASTAVA S, PANDIT V S. A 2-D of LQR based PID controller for integrating processes considering robustness/performance tradeoff[J]. ISA Transactions, 2017,71:426-439. doi: 10.1016/j.isatra.2017.09.010
[18]	SUNDARAMOORTHY S, RAMASAMY M. Optimal proportional-integral-derivative controllers for desired closed-loop response using the method of moments[J]. Industrial & Engineering Chemistry Research, 2014,53:17403-17418. doi: 10.1021/ie501824k
[19]	喻锋, 王西田, 杨煜, 等. 一种混合遗传算法在HVDC定电流控制器参数优化中的应用[J]. 电力系统保护与控制, 2014,42(9):126-131.
	YU Feng, WANG Xitian, YANG Yu, et al. An application of hybrid genetic algorithm in the parameters optimization of HVDC constant current controller[J]. Power System Protection and Control, 2014,42(9):126-131.
[20]	袁雪峰, 马进, 强硕, 等. 遗传算法优化锅炉汽包水位不完全微分PID参数[J]. 华电技术, 2018,40(10):7-11.
	YUAN Xuefeng, MA Jin, QIANG Shuo, et al. Incomplete differential PID parameters of boiler drum water level optimized by genetic algorithm[J]. Huadian Technology, 2018,40(10):7-11.
[21]	刘东, 冯全源, 蒋启龙. 基于改进PSO算法的磁浮列车PID控制器参数优化[J]. 西南交通大学学报, 2010,45(3):405-410.
	LIU Dong, FENG Quanyuan, JIANG Qilong. Parameter optimization of maglev PID controller based on improved PSO algorithm[J]. Journal of Southwest Jiaotong University, 2010,45(3):405-410.
[22]	高明明, 杨磊, 于浩洋, 等. 量子计算在火电机组优化控制中的应用综述[J]. 华电技术, 2020,42(8):90-96.
	GAO Mingming, YANG Lei, YU Haoyang, et al. Review on the application of quantum computing in optimization control on thermal power units[J]. Huadian Technology, 2020,42(8):90-96.
[23]	SHAFIEI Z, SHENTON A T. Tuning of PID-type controllers for stable and unstable systems with time delay[J]. Automatica, 1994,30(10):1609-1615. doi: 10.1016/0005-1098(94)90100-7
[24]	ASTROM K J, HAGGLUND T. Advanced PID control [M]. Princeton: Princeton University Press, 2006.
[25]	HIMANSHU J, KALYANMOY D. An evolutionary many-objective optimization algorithm using reference-point based non-dominated sorting approach, part Ⅱ: Handling constraints and extending to an adaptive approach[J]. IEEE Transactions on Evolutionary Computation, 2014,18(4):602-622. doi: 10.1109/TEVC.2013.2281534
[26]	吴振龙, 何婷, 王灵梅, 等. 基于多目标遗传算法的过热汽温建模与仿真[J]. 系统仿真学报, 2017,29(9):2081-2086.
	WU Zhenlong, HE Ting, WANG Lingmei, et al. Modeling and simulation of superheated steam temperature based on multi-objective genetic algorithm[J]. Journal of System Simulation, 2017,29(9):2081-2086.

项目	现场参数	优化参数
最大正偏差/mm	4.19	2.26
最大负偏差/mm	3.63	2.58
绝对偏差均值	0.92	0.44
方差	1.41	0.31