数据驱动预整定自适应子空间模型预测控制

doi:10.16182/j.issn1004731x.joss.201801044

摘要/Abstract

摘要： 针对电厂过热汽温系统的大迟延、大惯性、时变等的特性,研究其预测控制问题,提出一种数据驱动预整定自适应子空间模型预测控制方法PTA-MPC (Pre Tuning Adaptive Subspace Model Predictive Control),结合子空间辨识和状态空间预测控制的优点。通过子空间辨识得到多个工况在输入信号满足持续激励的情况下的状态空间模型。通过状态空间模型递推出预测控制律,利用粒子群算法对各工况下的控制器参数进行整定。采用最小二乘参数估计的方法将多工况下的状态空间模型及预测控制器参数进行平滑处理。给出了PTA-MPC在串级控制中设计的步骤。仿真实验的结果验证了所提方法的有效性。

关键词: 子空间辨识, 状态空间模型, 预测控制, 预整定自适应

Abstract: The problem of predictive control is investigated for power plant superheated steam temperature system with the characteristics of large delay, large inertia and time-varying. The data driven pre tuning adaptive subspace model predictive control (PTA-MPC) method, which combines the advantages of subspace identification and state space predictive control, is proposed. The state space models of multiple conditions are obtained by subspace identification with the input signal in persistent excitation. The predictive control law is derived with the state space models, and the controller parameters are optimized by using particle swarm optimization (PSO) algorithm. Based on the least square parameter estimation, state space model parameters and predictive controller parameters are smoothed. The steps of PTA-MPC algorithm in cascade control system are presented. A simulation example illustrates the effectiveness of this method.

Key words: subspace identification, state space model, predictive control, pre tuning adaptive

中图分类号:

TP391.9

韩璞, 刘淼, 贾昊. 数据驱动预整定自适应子空间模型预测控制[J]. 系统仿真学报, 2018, 30(1): 332-340.

Han Pu, Liu Miao, Jia Hao. Data Driven Pre Tuning Adaptive Subspace Model Predictive Control[J]. Journal of System Simulation, 2018, 30(1): 332-340.

参考文献

[1] 王富强. 基于逆向传递策略的直流锅炉主汽温网络化预测控制[J]. 中国电机工程学报, 2015, 35(19): 4981-4990. Wang Fuqiang. Networked Predictive Control of Main Steam Temperature for Once-through Boiler Based on [J]. Proceedings of the CSEE, 2015, 35(19): 4981-4990.
[2] Wu C, Lu C, Han. Closed-Loop Identification of Power System Based on Ambient Data[J]. Mathematical Problems in Engineering(S1024-123X), 2012(2): 1-16.
[3] Kung S.Y, Arun K, Rao D. A New Identification and Model Reduction Algorithm via Singular Value Decomposition[C]// Proceedings of the 12th Asilomar Conference on Circuits Systems and Computers, Pacific Grove, CA, 1978.
[4] 王新, 吕剑虹, 向文国. 一种改进的子空间辨识方法在热工过程中的应用[J]. 中国电机工程学报, 2010, 30(32): 96-102. Wang Xin, Lü Jianhong, Xiang Wenguo. A Modified Subspace Identification Method and Its Application in Thermal Process [J]. Proceedings of the CSEE, 2010, 30(32): 96-102.
[5] Wang J, Qin S.Closed-loop Subspace Identification Using the Parity Space[J]. Automatic(S0005-1098), 2006, 42(2): 315-320
[6] 叶凌箭, 钟伟红, 宋执环. 基于分段线性化法的改进自主优化控制[J]. 自动化学报, 2013, 39(8): 1231-1237. Ye Ling-Jian, Zhong Wei-Hong, Song Zhi-Huan. Improved Self-optimizing Control Method Based on Piece-wise Linearization [J]. ACTA Automatica Sinca, 2013, 39(8): 1231-1237.
[7] 王志甄, 邹志云, 赵丹丹. 分段线性化多模型动态矩阵控制算法的设计[J]. 计算机与应用化学, 2010, 27(8): 1103-1106. Wang Zhizhen, Zou Zhiyun, Zhao Dandan. Design of a piecewise linear multi model dynamic matrix control algorithm [J] Computers and Applied Chemistry, 2010, 27(8): 1103-1106.
[8] 席裕庚. 预测控制[M]. 北京: 国防工业出版社, 2013. Xi Yugeng. Predictive Control [M]. Beijing: National Defense Industry Press, 2013.
[9] 吴啸, 沈炯. 机炉协调系统的子空间辨识及预测控制[J]. 东南大学学报(自然科学版), 2012, 42(2): 281-286. Wu Xiao Shen Jiong. Subspace identification and predictive control of boiler-turbine coordinated system [J]. Journal of Southeast University (Natural Science Edition), 2012, 42(2): 281-286.
[10] 张立, 高宪文. 递推子空间自适应预测控制综合方法[J]. 自动化学报, 2012, 41(4): 439-453. Zhang Li, Gao Xianwen. A Synthesis Method of Adaptive Predictive Control Based on Recursive Subspace [J]. Information and Control, 2012, 41(4): 439-453.
[11] 赵慧荣, 沈炯, 沈德明, 等. 主汽温多模型扰动抑制预测控制方法[J]. 中国电机工程学报, 2014, 34(32): 5763-5770. Zhao Huirong, Shen Jiong, Shen Deming, et al. Multiple Model Disturbance Rejection Predictive Control of Main Steam Temperature [J]. Proceedings of the CSEE, 2014, 34(32): 5763-5770.
[12] 侯忠生, 许建新. 数据驱动控制理论及方法的回顾和展望[J]. 自动化学报, 2009, 35(6): 650-667. Hou Zhong-Sheng, Xu Jian-Xin. On Data-driven Control Theory: the State of the Art and Perspective [J]. ACTA Automatica Sinca, 2009, 35(6): 650-667.
[13] 李晗, 萧德云. 基于数据驱动的故障诊断方法综述[J]. 控制与决策, 2011, 26(1): 1-9. Li Han, Xiao De-yun. Survey on data driven fault diagnosis methods [J]. Control and Decision, 2011, 26(1): 1-9.
[14] 赵书强, 张一, 马燕峰. 基于数据驱动随机子空间方法在低频振荡辨识中的应用[J]. 电力系统保护与控制, 2013, 41(8): 81-86. Zhao Shu-qiang, Zhang Yi, Ma Yan-feng. Data-driven based stochastic subspace identification for low frequency oscillation analysis [J]. Power System Protection and Control, 2013, 41(8): 81-86.
[15] 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, 39(4): 293-302. Wang Fei-Yue. Parallel Control: A Method for Data-Driven and Computational Control [J]. ACTA Automatica Sinca, 2013, 39(4): 293-302.
[16] Van Overschee P, D E Moor B. Subspace Identification for Linear Systems: Theory-Implementation- Applications[M]. Kluwer Academic Publishers, 1996.
[17] 孙明, 韩璞, 程海燕, 等. 600MW燃煤机组燃烧系统的建模方法研究[J]. 计算机仿真, 2016, 33(1): 416-419. Sun Ming, Han Pu, Chen Haiyan, et al. Research on Modeling Method of 600MW Coal-Fired Units’ Combustion System [J]. Computer Simulation, 2016, 33(1): 416-419.
[18] 刘长良, 马增辉, 开平安. 电站过热汽温系统的内模PID控制及其仿真[J]. 系统仿真学报, 2014, 26(11): 2722-2726. Liu Changliang, Ma Zenghui, Kai Ping’an. Internal Model PID Control and Its Simulation in Main Steam Temperature System of Power Plant[J]. Journal of System Simulation, 2014, 26(11): 2722-2726.
[19] 韩璞. 智能控制理论及应用[M]. 北京: 中国电力出版社, 2013. Han Pu. The theory and application of intelligent control [M]. BeiJing: China Electric Power Publishing House, 2013.