自耦PID控制器

曾喆昭; 刘文珏

doi:10.16383/j.aas.c180290

自耦PID控制器

doi: 10.16383/j.aas.c180290

曾喆昭^1, ,,
刘文珏^1,

1.
长沙理工大学电气与信息工程学院长沙 410114

基金项目:

国家自然科学基金 51877011

湖南省教育厅重点项目 17A006

详细信息

作者简介:
刘文珏长沙理工大学电子科学与技术硕士研究生.主要研究方向为智能计算与智能控制.E-mail: 269766331@qq.com

通讯作者:
曾喆昭长沙理工大学教授. 1987、1989和2008年先后获湘潭大学物理学、清华大学固体物理学和湖南大学电路与系统等理学学士、理学硕士和工学博士学位.主要研究方向为智能计算与智能控制.本文通信作者. E-mail: 508984293@qq.com

计量
- 文章访问数: 2440
- HTML全文浏览量: 424
- PDF下载量: 553
- 被引次数: 0
出版历程
- 收稿日期: 2018-05-08
- 录用日期: 2019-01-18
- 刊出日期: 2021-02-26

Self-coupling PID Controllers

ZENG Zhe-Zhao^{1
, ,},
LIU Wen-Jue^1
,

1.
College of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114

Funds:

National Natural Science Foundation of China 51877011

Key project of Education Department of Hunan Province 17A006

More Information

Author Bio:
LIU Wen-Jue Master student in electronic science and technology at Changsha University of Science and Technology. His research interest covers intelligent computing and intelligent control

Corresponding author: ZENG Zhe-Zhao Professor at Changsha University of Science and Technology. He received his bachelor of science, master of science and Ph. D. of engineering degrees in physics from Xiangtan University, solid state physics from Tsinghua University and Circuits and Systems from Hunan University in 1987, 1989 and 2008. His research interest covers intelligent computing and intelligent control. Corresponding author of this paper

摘要

摘要: 针对比例—积分—微分(Proportional-integral-differential, PID)控制器的整定问题, 提出了自耦PID (Self-coupling PID, SC-PID)控制方法.该方法将系统动态和内外不确定性定义为总和扰动, 从而将非线性不确定系统变换为线性不确定系统, 进而构建了总和扰动反相激励下的误差动态系统, 据此设计了SC-PID控制律模型和整定规则, 进而设计了自适应速度因子(Adaptive speed factor, ASF)模型.数值仿真结果表明, SC-PID具有快的响应速度、高的控制精度、良好的抗总和扰动鲁棒性等诸多优点. SC-PID整定规则为现有PID整定结果的技术评估与技术升级提供了科学的理论依据, 在国防和工业控制领域具有广泛的应用价值.
- 非线性不确定系统 /
- 自适应速度因子 /
- 自耦PID控制 /
- 整定规则 /
- 全局稳定性
Abstract: Aiming at tuning problem for proportional-integral-differential (PID) controller, the self-coupling PID (SC-PID) control method was proposed. Dynamic and uncertainty both inside and outside system was defined as a total disturbance in the proposed method, so as to transform nonlinear uncertain system to linear uncertain system, and then error dynamic system (EDS) inversely driven by the total disturbance was built. Control law and its tuning rules for SC-PID were designed on the basis of EDS, and then adaptive speed factor (ASF) model was designed. Numerical simulation results showed that SC-PID has fast response, high control accuracy, good global stability robustness and good total disturbance rejection robustness etc. many advantages. The SC-PID tuning rules will provide a scientific theoretical basis for the technical evaluation and technical upgrading of the existing PID tuning results, and has extensive application value in national defense and industrial control.
- Nonlinear uncertain system /
- adaptive speed factor /
- SC-PID control /
- tuning rule /
- global stability
Recommended by Associate Editor XU Bin
注释:

1) 本文责任编委许斌

HTML全文

图 1 基于SC-PI的闭环控制系统模型

Fig. 1 Closed loop control system based on SC-PI

下载: 全尺寸图片幻灯片

图 2 基于SC-PID的闭环控制系统模型

Fig. 2 Closed loop control system based on SC-PID

下载: 全尺寸图片幻灯片

图 3 基于SC-PD的闭环控制系统模型

Fig. 3 Closed loop control system based on SC-PD

下载: 全尺寸图片幻灯片

图 4 SC-PI的正弦跟踪控制结果

Fig. 4 Sinusoidal tracking control results of SC-PI

下载: 全尺寸图片幻灯片

图 5 SC-PI的阶跃跟踪控制结果

Fig. 5 Step tracking control results of SC-PI

下载: 全尺寸图片幻灯片

图 6 时变系统的SC-PI控制结果

Fig. 6 SC-PI control results for time-varying systems

下载: 全尺寸图片幻灯片

图 7 SC-PID的正弦跟踪控制结果

Fig. 7 Sinusoidal tracking control results of SC-PID

下载: 全尺寸图片幻灯片

图 8 SC-PID的阶跃跟踪控制结果

Fig. 8 Step tracking control results of SC-PID

下载: 全尺寸图片幻灯片

图 9 时变系统的阶跃跟踪控制结果

Fig. 9 Step tracking control results for TVS

下载: 全尺寸图片幻灯片

图 10 SC-PD的正弦跟踪控制结果

Fig. 10 Sinusoidal tracking control results of the SC-PD

下载: 全尺寸图片幻灯片

图 11 SC-PD的阶跃跟踪控制结果

Fig. 11 Step tracking control results of the SC-PD

下载: 全尺寸图片幻灯片

图 12 时变系统的阶跃跟踪控制结果

Fig. 12 Step tracking control results for TVS

下载: 全尺寸图片幻灯片

图 13 四种控制器的控制结果

Fig. 13 Control results of the four controllers

下载: 全尺寸图片幻灯片

图 14 四种控制器的控制结果

Fig. 14 Control results of the four controllers

下载: 全尺寸图片幻灯片

参考文献(27)

[1]	柴天佑.自动化科学与技术发展方向.自动化学报, 2018, 44(11): 1923-1930 doi: 10.16383/j.aas.2018.c180252 Chai Tian-You. Development directions of automation science and technology. Acta Automatica Sinica, 2018, 44(11): 1923-1930 doi: 10.16383/j.aas.2018.c180252
[2]	王维洲, 吴志伟, 柴天佑.电熔镁砂熔炼过程带输出补偿的PID控制.自动化学报, 2018, 44(7): 1282-1292 doi: 10.16383/j.aas.2018.c170620 Wang Wei-Zhou, Wu Zhi-Wei, Chai Tian-You. PID control with output compensation for the fused magnesia smelting process. Acta Automatica Sinica, 2018, 44(7): 1282-1292 doi: 10.16383/j.aas.2018.c170620
[3]	王兰豪, 贾瑶, 柴天佑.再磨过程的泵池液位和给矿压力双速率区间控制.自动化学报, 2017, 43(6): 993-1006 doi: 10.16383/j.aas.2017.c170134 Wang Lan-Hao, Jia Yao, Chai Tian-You. Dual-rate interval control of pump pool level and feeding pressure during regrinding. Acta Automatica Sinica, 2017, 43(6): 993-1006 doi: 10.16383/j.aas.2017.c170134
[4]	杨辉, 郝丽娜, 陈洋, 薛帮灿.针对气动肌肉仿生肘关节抖振现象的Kalman-PID控制.控制理论与应用, 2017, 34(4): 477-482 https://www.cnki.com.cn/Article/CJFDTOTAL-KZLY201704008.htm Yang Hui, Hao Li-Na, Chen Yang, Xue Bang-Can. Kalman-PID control for chattering phenomena of bionic elbow joint actuated by pneumatic artificial muscles. Control Theory & Applications, 2017, 34(4): 477-482 https://www.cnki.com.cn/Article/CJFDTOTAL-KZLY201704008.htm
[5]	Wang Z D, Xu Y, Liu G, Wang J X, Jiang W, Wang Y, et al. Simulation and experiment of PID applied to the automatic voltage control of gyrotron traveling wave tubes. IEEE Transactions on Plasma Science, 2018, 46(7): 2446-2451 doi: 10.1109/TPS.2017.2723245
[6]	Zhang Y, Jia Y, Chai T Y, Wang D H, Dai W, Fu J. Data-driven PID controller and its application to pulp neutralization process. IEEE Transactions on Control Systems Technology, 2018, 26(3): 828-841 doi: 10.1109/TCST.2017.2695981
[7]	韩京清.自抗扰控制器及其应用.控制与决策, 1998, 13(1): 19-23 doi: 10.3321/j.issn:1001-0920.1998.01.005 Han Jing-Qing. Active disturbance rejection controller and applications. Control and Decision, 1998, 13(1): 19-23 doi: 10.3321/j.issn:1001-0920.1998.01.005
[8]	韩京清.控制理论—模型论还是控制论.系统科学与数学, 1989, 9(4): 328-335 https://www.cnki.com.cn/Article/CJFDTOTAL-STYS198904005.htm Han Jing-Qing. Control theory, is it a model analysis approach or a direct control approach. Journal of Systems Science and Mathematical Sciences, 1989, 9(4): 328-335 https://www.cnki.com.cn/Article/CJFDTOTAL-STYS198904005.htm
[9]	韩京清.自抗扰控制技术.北京:国防工业出版社, 2009, XI Han Jing-Qing. Active Disturbance Rejection Control Technology, Beijing: National Defense Industry Press, 2009, XI
[10]	魏伟, 梅生伟, 张雪敏.先进控制理论在电力系统中的应用综述及展望.电力系统保护与控制, 2013, 41(12): 143-153 doi: 10.7667/j.issn.1674-3415.2013.12.023 Wei Wei, Mei Sheng-Wei, Zhang Xue-Min. Review of advanced control theory and application in power system. Power System Protection and Control, 2013, 41(12): 143-153 doi: 10.7667/j.issn.1674-3415.2013.12.023
[11]	周延延, 吴晓燕, 李刚.基于BP神经网络的PID控制器研究.空军工程大学学报, 2007, 8(4): 45-48 doi: 10.3969/j.issn.1009-3516.2007.04.013 Zhou Yan-Yan, Wu Xiao-Yan, Li Gang. Study on PID controller based on BP neural network. Journal of Air Force Engineering University, 2007, 8(4): 45-48 doi: 10.3969/j.issn.1009-3516.2007.04.013
[12]	王明涛, 张百浩.基于专家PID的燃气机转速控制试验.热能动力工程, 2015, 30(6): 848-852 https://www.cnki.com.cn/Article/CJFDTOTAL-RNWS201506011.htm Wang Ming-Tao, Zhang Bai-Hao. Experiment of the rotating speed control over a gas engine based on an expert PID (Proportional, Integral and Differential) control method. Journal of Engineering for Thermal energy and power, 2015, 30(6): 848-852 https://www.cnki.com.cn/Article/CJFDTOTAL-RNWS201506011.htm
[13]	李明, 封航, 张延顺.基于UMAC的RBF神经网络PID控制.北京航空航天大学学报, 2018, 44(10): 2063-2070 https://www.cnki.com.cn/Article/CJFDTOTAL-BJHK201810006.htm Li Ming, Feng Hang, Zhang Yan-Shu. RBF neural network tuning PID control based on UMAC. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(10): 2063-2070 https://www.cnki.com.cn/Article/CJFDTOTAL-BJHK201810006.htm
[14]	王青山, 梁得亮, 杜锦华.交流稳压电源的改进神经网络PID控制.电机与控制学报, 2017, 21(2): 1-9 https://www.cnki.com.cn/Article/CJFDTOTAL-DJKZ201702001.htm Wang Qing-Shan, Liang De-Liang, Du Jin-Hua. Improved neural network PID controller for regulated power supply. Electric Machines and Control, 2017, 21(2): 1-9 https://www.cnki.com.cn/Article/CJFDTOTAL-DJKZ201702001.htm
[15]	邱占芝, 李世峰.基于神经网络的PID网络化控制系统建模与仿真.系统仿真学报, 2018, 30(4): 1423-1432 https://www.cnki.com.cn/Article/CJFDTOTAL-XTFZ201804027.htm Qiu Zhan-Zhi, Li Shi-Feng. Modeling and simulation of PID networked control systems based on neural network. Journal of System Simulation, 2018, 30(4): 1423-1432 https://www.cnki.com.cn/Article/CJFDTOTAL-XTFZ201804027.htm
[16]	曾喆昭, 肖亚芬, 郝逸轩.非线性类PID神经元网络控制器.北京科技大学学报, 2012, 34(1): 12-15 https://www.cnki.com.cn/Article/CJFDTOTAL-BJKD201201002.htm Zeng Zhe-Zhao, Xiao Ya-Fen, Hao Yi-Xuan. Nonlinear PID-like neuron network controller. Journal of Beijing University of Science and Technology, 2012, 34(1): 12-15 https://www.cnki.com.cn/Article/CJFDTOTAL-BJKD201201002.htm
[17]	李桂梅, 曾喆昭.一种基于神经网络算法的非线性PID控制器.中南大学学报(自然科学版), 2010, 41(5): 1865-1870 https://www.cnki.com.cn/Article/CJFDTOTAL-ZNGD201005038.htm Li Gui-Mei, Zeng Zhe-Zhao. A nonlinear PID controller based on neural network algorithm. Journal of Central South University (Science and Technology), 2010, 41(5): 1865-1870 https://www.cnki.com.cn/Article/CJFDTOTAL-ZNGD201005038.htm
[18]	Shen J, Xin B. Control of single-axis rotation INS by tracking differentiator based fuzzy PID. IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(6): 2976-2986 doi: 10.1109/TAES.2017.2722558
[19]	潘泽跃, 程健, 吴嘉珉, 程景行.基于FPGA广义预测比例—积分—微分控制在特种电源中的应用.电工技术学报, 2018, 33(10): 2376-2382 https://www.cnki.com.cn/Article/CJFDTOTAL-DGJS201810023.htm Pan Ze-Yue, Cheng Jian, Wu Jia-Min, Cheng Jing-Xing. The application of generalized predictive proportion-integration-differentiation control in special power supply based on FPGA. Transactions of China Electrotechnical Society, 2018, 33(10): 2376-2382 https://www.cnki.com.cn/Article/CJFDTOTAL-DGJS201810023.htm
[20]	谢宏, 杨鹏, 陈海滨, 张小刚, 陈俊辉, 谭阳红.遗传优化模糊PID融合算法的5自由度机械手控制.电子测量与仪器学报, 2015, 29(1): 21-30 https://www.cnki.com.cn/Article/CJFDTOTAL-DZIY201501003.htm Xie Hong, Yang Peng, Chen Hai-Bin, Zhang Xiao-Gang, Chen Jun-Hui, Tan Yang-Hong. Fuzzy PID control system optimized by genetic algorithm for 5-freedom robot arm. Journal of Electronic Measurement and Instrumentation, 2015, 29(1): 21-30 https://www.cnki.com.cn/Article/CJFDTOTAL-DZIY201501003.htm
[21]	王镇道, 张乐, 彭子舜.基于PSO优化算法的模糊PID励磁控制器设计.湖南大学学报(自然科学版), 2017, 44(8): 106-111 https://www.cnki.com.cn/Article/CJFDTOTAL-HNDX201708016.htm Wang Zhen-Dao, Zhang Le, Peng Zi-Shun. Design of fuzzy PID excitation control based on PSO optimization algorithm. Journal of Hunan University (Natural Science), 2017, 44(8): 106-111 https://www.cnki.com.cn/Article/CJFDTOTAL-HNDX201708016.htm
[22]	Rabiatuladawiah A H, Siti F T, Salmiah A, Mohd K H. Swarm-intelligence tuned current reduction for power-assisted steering control in electric vehicles. IEEE Transactions on Industrial Electronics, 2018, 65(9): 7202-7210 doi: 10.1109/TIE.2017.2784344
[23]	Marín A, Hernández R J A, Jiménez J A. Tuning multivariable optimal PID controller for a continuous stirred tank reactor using an evolutionary algorithm. IEEE Latin America Transactions, 2018, 16(2): 422-427 doi: 10.1109/TLA.2018.8327395
[24]	曾喆昭, 贺莹, 张畅, 李霖.非线性PID自学习控制方法研究.计算机工程, 2014, 40(10): 224-227 doi: 10.3969/j.issn.1000-3428.2014.10.042 Zeng Zhe-Zhao, He Ying, Zhang Chang, Li Lin. Research on nonlinear PID control method for self-learning. Computer Engineering, 2014, 40(10): 224-227 doi: 10.3969/j.issn.1000-3428.2014.10.042
[25]	周勇, 曾喆昭.自学习非线性PID抗扰控制原理研究.控制工程, 2017, 24(6): 1180-1185 https://www.cnki.com.cn/Article/CJFDTOTAL-JZDF201706014.htm Zhou Yong, Zeng Zhe-Zhao. Research on self-learning nonlinear PID disturbance rejection control principle. Control Engineering of China, 2017, 24(6): 1180-1185 https://www.cnki.com.cn/Article/CJFDTOTAL-JZDF201706014.htm
[26]	曾喆昭, 吴亮东, 杨振源, 唐欢.非仿射系统的自学习滑模抗扰控制.控制理论与应用, 2016, 33(7): 980-987 https://www.cnki.com.cn/Article/CJFDTOTAL-KZLY201607017.htm Zeng Zhe-Zhao, Wu Liang-Dong, Yang Zhen-Yuan, Tang Huan. Self-learning sliding-mode disturbance rejection control for non-affine systems. Control Theory & Applications, 2016, 33(7): 980-987 https://www.cnki.com.cn/Article/CJFDTOTAL-KZLY201607017.htm
[27]	Cheng Chun-Hua, Hu Yun-An, Wu Jin-Hua, Li Jing. Auto disturbance rejection controller for non-affine nonlinear systems with adaptive observers. Control Theory & Applications, 2014, 31(2): 148-158