Combined Moving Horizon Estimation and Model Predictive Control for Main Steam Temperature System
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- 英文论文题名：Combined Moving Horizon Estimation and Model Predictive Control for Main Steam Temperature System
- 论文作者：Huaizhong Hu ; Yu Li ; Qingyu Yang ; Yuanli Cai
- 英文论文作者：Huaizhong Hu ; Yu Li ; Qingyu Yang ; Yuanli Cai ; Institute of Control Engineering ; Xi'an Jiaotong University
- 年：2017
- 作者机构：Institute of Control Engineering,Xi'an Jiaotong University;
- 英文论文关键词：Main steam temperature ; moving horizon estimation ; model predictive control ; min-max optimization ; robust control
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（B）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（B）
- 语种：英文
- 分类号：TM621
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：6
- 文件大小：219k
- 原文格式：D
- 会议级别：全国
摘要
This paper considers a robust control strategy for main steam temperature system of thermal power plant that is affected by immeasurable bounded disturbances. An output-feedback min-max optimization approach is proposed to obtain a controller which could effectively reject disturbances and stabilize the system. This approach combines moving horizon estimation with model predictive control and is formulated into a min-max optimization. At each sampling time, the optimization criterion is maximized over worst-case estimate of disturbance sequence and minimized over optimal control sequence via a primal-dual interior-point method. In this way, the controller can improve robustness under constrained disturbances. Simulation results verify that the presented approach could provide significant anti-disturbance capability compared with a conventional DMC-PID strategy.
This paper considers a robust control strategy for main steam temperature system of thermal power plant that is affected by immeasurable bounded disturbances. An output-feedback min-max optimization approach is proposed to obtain a controller which could effectively reject disturbances and stabilize the system. This approach combines moving horizon estimation with model predictive control and is formulated into a min-max optimization. At each sampling time, the optimization criterion is maximized over worst-case estimate of disturbance sequence and minimized over optimal control sequence via a primal-dual interior-point method. In this way, the controller can improve robustness under constrained disturbances. Simulation results verify that the presented approach could provide significant anti-disturbance capability compared with a conventional DMC-PID strategy.
This paper considers a robust control strategy for main steam temperature system of thermal power plant that is affected by immeasurable bounded disturbances. An output-feedback min-max optimization approach is proposed to obtain a controller which could effectively reject disturbances and stabilize the system. This approach combines moving horizon estimation with model predictive control and is formulated into a min-max optimization. At each sampling time, the optimization criterion is maximized over worst-case estimate of disturbance sequence and minimized over optimal control sequence via a primal-dual interior-point method. In this way, the controller can improve robustness under constrained disturbances. Simulation results verify that the presented approach could provide significant anti-disturbance capability compared with a conventional DMC-PID strategy.
引文
[1]LI Jun,WU Gang,D Sun,W Zhang,and D Chen,Using modular cascade DMC in the main steam temperature control of large power-plant boiler,Journal of University of Science and Technology of China,2003,33(4):460-465.
[2]L Hubka O Modrlak,The practical possibilities of steam temperature dynamic models application,In International Carpathian Control Conference,2012:237–242.
[3]L.Magni and R.Scattolini,Robustness and robust design of MPC for nonlinear discrete-time systems,In Assessment and future directions of nonlinear model predictive control,pages239-254,Springer,2007.
[4]D.M.Raimondo,D.Limon,M.Lazar,L.Magni,and E.Camacho,Min-max model predictive control of nonlinear systems:A unifying overview on stability,European Journal of Control,15(1):5-21,2009.
[5]M.Lazar,D.Mu?oz de la Pe?a,W.Heemels,and T.Alamo,On input-to-state stability of min–max nonlinear model predictive control,Systems&Control Letters,vol.57,no.1,pp.39-48,2008.
[6]J.H.Lee and Z.Yu,Worst-case formulations of model predictive control for systems with bounded parameters,Automatica,33(5):763-781,1997.
[7]C.V.Rao,J.B.Rawlings,and D.Q.Mayne,Constrained state estimation for nonlinear discrete-time systems:Stability and moving horizon approximations,Automatic Control,IEEE Transactions on,48(2):246-258,2003.
[8]A.Alessandri,M.Baglietto,and G.Battistelli,Moving horizon state estimation for nonlinear discrete-time systems:New stability results and approximation schemes,Automatica,44(7):1753-1765,2008.
[9]D.Sui,L.Feng.And M.Hovd,Robust output feedback model predictive control for linear systems via moving horizon estimation,In American control conference,2008,pages 190-206,2003.
[10]XC Luan,SY Li,and LL Li,Adaptive control for the superheated steam temperature based on multi-model state observer,IEEE,2004,2:978–982.
[11]D.A.Copp and J.P.Hespanha,Addressing adaptation and learning in the context of MPC with MHE,Control of Complex Systems:Theory and Applications,In press.
[12]D.A.Copp and J.P.Hespanha,Nonlinear output-feedback model predictive control with moving horizon estimation,Technical report,Univ.California,Santa Barbara,2014.http://www.ece.ucsb.edu/hespanha/techrep.html.
[13]Stephen Boyd and Lieven Vandenberghe,Convex Optimization,New York,United States of America by Cambridge University Press,2004,chapter 11.
[14]Aljunaid,On DMC-PID Cascade Control and Multivariable Generalized Predictive Control Algorithm(in Chinese),East China University of Science and Technology,2013.
1 Given an integer N,0N denotes the N-vectors with all entries equal to 0.
2 *means the entry-wise product.
[2]L Hubka O Modrlak,The practical possibilities of steam temperature dynamic models application,In International Carpathian Control Conference,2012:237–242.
[3]L.Magni and R.Scattolini,Robustness and robust design of MPC for nonlinear discrete-time systems,In Assessment and future directions of nonlinear model predictive control,pages239-254,Springer,2007.
[4]D.M.Raimondo,D.Limon,M.Lazar,L.Magni,and E.Camacho,Min-max model predictive control of nonlinear systems:A unifying overview on stability,European Journal of Control,15(1):5-21,2009.
[5]M.Lazar,D.Mu?oz de la Pe?a,W.Heemels,and T.Alamo,On input-to-state stability of min–max nonlinear model predictive control,Systems&Control Letters,vol.57,no.1,pp.39-48,2008.
[6]J.H.Lee and Z.Yu,Worst-case formulations of model predictive control for systems with bounded parameters,Automatica,33(5):763-781,1997.
[7]C.V.Rao,J.B.Rawlings,and D.Q.Mayne,Constrained state estimation for nonlinear discrete-time systems:Stability and moving horizon approximations,Automatic Control,IEEE Transactions on,48(2):246-258,2003.
[8]A.Alessandri,M.Baglietto,and G.Battistelli,Moving horizon state estimation for nonlinear discrete-time systems:New stability results and approximation schemes,Automatica,44(7):1753-1765,2008.
[9]D.Sui,L.Feng.And M.Hovd,Robust output feedback model predictive control for linear systems via moving horizon estimation,In American control conference,2008,pages 190-206,2003.
[10]XC Luan,SY Li,and LL Li,Adaptive control for the superheated steam temperature based on multi-model state observer,IEEE,2004,2:978–982.
[11]D.A.Copp and J.P.Hespanha,Addressing adaptation and learning in the context of MPC with MHE,Control of Complex Systems:Theory and Applications,In press.
[12]D.A.Copp and J.P.Hespanha,Nonlinear output-feedback model predictive control with moving horizon estimation,Technical report,Univ.California,Santa Barbara,2014.http://www.ece.ucsb.edu/hespanha/techrep.html.
[13]Stephen Boyd and Lieven Vandenberghe,Convex Optimization,New York,United States of America by Cambridge University Press,2004,chapter 11.
[14]Aljunaid,On DMC-PID Cascade Control and Multivariable Generalized Predictive Control Algorithm(in Chinese),East China University of Science and Technology,2013.
1 Given an integer N,0N denotes the N-vectors with all entries equal to 0.
2 *means the entry-wise product.