一种面向控制系统解耦的序列决策PID参数整定方法

doi:10.11949/j.issn.0438-1157.20171478

摘要/Abstract

摘要：

传统的解耦控制方法由于其过于依赖模型辨识等问题难以被实际应用，且存在成熟系统不宜进行控制器再设计等问题，此外，回路解耦控制的专家经验难以提取与传承。因此以等效传函理论为研究基础，提出了一种面向解耦问题的基于序列决策的PID参数整定方法，提取历史数据中经验丰富的操作人员的控制序列。在解决回路之间存在耦合而使控制效果不佳的问题的同时，使工程人员的PID参数整定经验数字化得以传承，辅助初级人员进行操作。最后将所提方法应用于Matlab和Aspen两个仿真平台的耦合回路系统，获得满意结果，并验证所提方法的有效性和正确性。

关键词: 过程控制, 参数估值, 神经网络

Abstract:

Traditional decoupling control methods have difficulties in industrial applications as a result of heavily relying on model identification and in controller re-designing as a result of mature system. Besides, it is difficult to extract and inherit expert experience of loops' decoupling controls. Based on equivalent transfer function theory, sequence-decision PID parameter tuning method was developed, which control sequences of experienced operators were effectively extracted from historical data. When facing poor control performance due to loops' coupling, junior staff could operate from inheritance of digitalized PID parameter tuning experience of experienced engineers. Method effectiveness and accuracy were verified in two coupling circuit systems in Matlab and Aspen simulation platforms.

Key words: process control, parameter estimation, neural networks

中图分类号:

TP29

高月, 宿翀, 李宏光. 一种面向控制系统解耦的序列决策PID参数整定方法[J]. 化工学报, 2018, 69(3): 1071-1080.

GAO Yue, SU Chong, LI Hongguang. Sequence-decision PID parameter tuning approach towards control system decoupling[J]. CIESC Journal, 2018, 69(3): 1071-1080.

参考文献 36

[1]	Tanaskovica m, Fagianob l, Novarac c, et al. Data-driven control of nonlinear systems:an on-line direct approach[J]. Automatica, 2017, 75:1-10.
[2]	Wei x j, Mottershead J E. Block-decoupling vibration control using eigenstructure assignment[J]. Mechanical Systems and Signal Processing, 2016, 74:11-28.
[3]	Ram Y M, Mottershead J E. Multiple-input active vibration control by partial pole placement using the method of receptances[J]. Mechanical Systems and Signal Processing, 2013, 40:727-735.
[4]	白辰, 樊垚, 任章, 等. 基于模糊神经网络的MIMO系统自适应解耦控制[J]. 北京航空航天大学学报, 2015, 41(11):2131-2136. BAI C, FAN Y, REN Z, et al. Adaptive decoupling control of a MIMO system based on fuzzy neural networks[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(11):2131-2136.
[5]	陈苏平, 孙优贤. 多变量过程的鲁棒解耦[J]. 自动化学报, 1995, 21(2):214-220. CHEN S P, SUN Y X. Robust decoupling of multivariable processes[J]. Acta Automatica Sinica, 1995, 21(2):214-220.
[6]	Juan C, He Z F, Qi X. A new control method for MIMO first order time delay non-square systems[J]. Journal of Process Control, 2011, 21(4):538-546.
[7]	Chiranjeeevi T, Vijetha I V V, Chakravarthi B, et al. Tuning and control of multi-variable systems[J]. Int. J. of Electronics and Electrical Engineering, 2014, 2(4):309-320.
[8]	Shen Y L, Sun Y X, Xu W. Centralized PI/PID controller design for multivariable processes[J]. Industrial & Engineering Chemistry Research, 2014, 53(25):10439-10447.
[9]	栾小丽, 王志强, 刘飞. 多变量非方系统集中式PI控制器设计[J]. 控制与决策, 2016, 31(5):811-816. LUAN X L, WANG Z Q, LIU F. Centralized PI control for multivariable non-square systems[J]. Control and Decision, 2016, 31(5):811-816.
[10]	王飞跃. 平行控制:数据驱动的计算控制方法[J]. 自动化学报, 2013, 38(4):293-302. WANG F Y. Parallel control:a method for data-driven and computational control[J]. Acta Automatica Sinica, 2013, 38(4):293-302.
[11]	王元卓, 靳小龙, 程学旗. 网络大数据:现状与展望[J]. 计算机学报, 2013, 36(6):1125-1138. WANG Y Z, JIN X L, CHENG X Q. Network big data present and future[J]. Chinese Journal of Computers, 2013, 36(6):1125-1138.
[12]	柴天佑. 工业过程控制系统研究现状与发展方向[J]. 中国科学, 2016, 8:1003-1015. CHAI T Y. Research status and development trend of industrial process control system[J]. Scientia Sinica Informationis, 2016, 8:1003-1015.
[13]	Galit S, Soumya R, Juan M V E, et al. The elephant in the room:predictive performance of PLS models[J]. Journal of Business Research, 2016, 69:4552-4564.
[14]	汤健, 贾美英, 刘卓, 等. 基于偏最小二乘算法的高维谱数据特征选择[J]. 控制工程, 2015, 22(6):1127-1130. TANG J, JIA M Y, LIU Z, et al. Feature selection approach of high dimension spectral data based-on partial least squares algorithm[J]. Control Engineering of China, 2015, 22(6):1127-1130.
[15]	Schlittgen r, Christian M R, Marko S, et al. Segmentation of PLS path models by iterative reweighted regressions[J]. Journal of Business Research, 2016, 69:4583-4592.
[16]	You w j, Yang z j, Ji g l. PLS-based recursive feature elimination for high-dimensional small sample[J]. Knowledge-Based Systems, 2014, 55:15-28.
[17]	Zhang n, Shetty d. An effective LS-SVM-based approach for surface roughness prediction in machined surface[J]. Neurocomputing, 2016, 198:35-39.
[18]	SHANG C, YANG F, HUANG D X, et al. Data-driven soft sensor development based on deep learning technique[J]. Journal of Process Control, 2014, 24(3):223-233.
[19]	Hossain m s, GOng Z C, Ismail Z, et al. Artificial neural networks for vibration based inverse parametric identifications:a review[J]. Applied Soft Computing, 2017, 52:203-219.
[20]	Zakaryazad A, Duman E. A profit-driven artificial neural network(ANN) with applications to fraud detection and direct marketing[J]. Neurocomputing, 2016, 75:121-131.
[21]	Leema N, Khanna H N, Kannan A. Neural network classifier optimization using differential evolution with global information and back propagation algorithm for clinical datasets[J]. Applied Soft Computing, 2016, 49:834-844.
[22]	欧阳华兵. 基于BP神经网络的面向STEP-NC加工工步序列生成[J]. 计算机集成制造系统, 2015, 21(7):1810-1819. OUYANG H B. STEP-NC oriented generation of workingsteps sequence based on BP neural networks[J]. Computer Integrated Manufacturing Systems, 2015, 21(7):1810-1819.
[23]	Oluwarotimi W S, Grace M A, Sangaiahc A K, et al. Prediction of high spatio-temporal resolution land surface temperature under cloudy conditions using microwave vegetation index and ANN[J]. Expert Systems with Applications, 2017, 68:163-172.
[24]	Chen J Y, Chen H J, Wu Z H, et al. Forecasting smog-related health hazard based on social media and physical sensor[J]. Information System, 2017, 64:281-291.
[25]	Nima H N, Guermazi A, Frank W R, et al. Prediction of medial tibiofemoral compartment joint space loss progression using volumetric cartilage measurements:data from the FNIH OA biomarkers consortium[J]. Eur. Radiol., 2017, 27:464-473.
[26]	Shwetha H R, Kumar D N. Prediction of high spatio-temporal resolution land surface temperature under cloudy conditions using microwave vegetation index and ANN[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 117:40-55.
[27]	Be?ikçi E B, Arslan O, Turan O, et al. An artificial neural network based decision support system for energy efficient ship operations[J]. Computers & Operations Research, 2016, 66:393-401.
[28]	Zou J, Chang Q, Arinez b G, et al. Dynamic production system diagnosis and prognosis using model-based data-driven method[J]. Expert Systems With Applications, 2017, 80:200-209.
[29]	Xu P, Han H J, Shi Z. Development of QSBR models for anoxic biodegradability of polycyclic aromatic hydrocarbons by using SMLR and BP-ANN[J]. Separation and Purification Technology, 2017, 178:1-5.
[30]	栾小丽, 陈强, 刘飞. 高维多变量系统集中式PI控制器设计[J]. 控制与决策, 2014, 29(4):605-610. LUAN X L, CHEN Q, LIU F. Centralized PI control for high dimensional multivariable systems[J]. Control and Decision, 2014, 29(4):605-610.
[31]	Hu J H, Zhang Y, Chen X H, et al. Multi-criteria decision making method based on possibility degree of interval type-2 fuzzy number[J]. Knowledge-Based Systems, 2013, 43:21-29.
[32]	TAN S, Mavrovouniotis M L. Reducing data dimensionality through optimizing neural network inputs[J]. AIChE J., 1995, 41(6):1471-1480.
[33]	郝立军, 杨燕华. 模糊解耦控制在精馏塔的应用[J]. 石油化工自动化, 2005, 6:27-29. HAO L J, YANG Y H. The fuzzy decoupling control used in rectifying tower[J]. Automation In Petro-chemical Industry, 2005, 6:27-29.
[34]	Li M X, Jia Y M, Matsuno F. Attenuating diagonal decoupling with robustness for velocity-varying 4WS vehicles[J]. Control Engineering Practice, 2016, 56:49-59.
[35]	Wang Y G, Pang X F, Piao Z L, et al. Intelligent decoupling PID control for the forced-circulation evaporation system[J]. Chinese Journal of Chemical Engineering, 2015, 23:2075-2086.
[36]	Garrido J, Vazquez F, Morilla F. Multivariable PID control by inverted decoupling:application to the Benchmark PID[J]. IFAC Proceedings, 2012, 45(3):352-357.