多变量预测控制工程应用的控制模型前馈解耦策略
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摘要
针对多变量预测控制计算量大、控制效果对扰动和模型失配敏感等特点,提出一种适用于预测控制工程应用的控制模型前馈解耦策略.基于结构分析,保留重要的被控变量与操作变量配对关系,将不重要的被控变量与操作变量配对作为前馈引入进行补偿,简化了系统结构,降低了系统耦合程度,减弱了预测控制器对扰动和模型失配的敏感程度,极端情况下形成的单入单出或小规模多入多出系统有效减小了在线计算量;基于分布式预测控制思想,给出控制模型前馈解耦策略的分散优化策略,进一步减小了系统规模和在线计算量.最后,通过仿真验证了所提策略的可行性与有效性.
For the characteristics that multi-variable predictive control has a large amount of calculation, and its control effect is sensitive to disturbance and model mismatch, a feedforward decoupling strategy based on a control model for the engineering application of multi-variable predictive control is proposed. Based on structure analysis, the important pairings between controlled variables and manipulated variables are preserved, and others are introduced as feedforward to make up the influence, which simplifies the system structure, reduces the coupling between controlled variables and manipulated variables, weakens the sensitivity of the predictive controller to the disturbance and model mismatch. The single input single output or smaller multiple input multiple output system formed under extreme circumstances reduces the amount of online computing. Then a decentralized optimization strategy based on distributed predictive control is proposed to reduce the system scale and online calculation amount further. Finally, the simulation examples are given to verify the feasibility and effectiveness of the proposed method.
For the characteristics that multi-variable predictive control has a large amount of calculation, and its control effect is sensitive to disturbance and model mismatch, a feedforward decoupling strategy based on a control model for the engineering application of multi-variable predictive control is proposed. Based on structure analysis, the important pairings between controlled variables and manipulated variables are preserved, and others are introduced as feedforward to make up the influence, which simplifies the system structure, reduces the coupling between controlled variables and manipulated variables, weakens the sensitivity of the predictive controller to the disturbance and model mismatch. The single input single output or smaller multiple input multiple output system formed under extreme circumstances reduces the amount of online computing. Then a decentralized optimization strategy based on distributed predictive control is proposed to reduce the system scale and online calculation amount further. Finally, the simulation examples are given to verify the feasibility and effectiveness of the proposed method.
引文
[1]席裕庚.预测控制[M].第2版.北京:国防工业出版社,2013:51-72.(Xi Y G. Predictive control[M]. 2nd ed. Beijing:National Defense Industry Press, 2013:51-72.)
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[6] Camponogara E, Jia D, Krogh B H, et al. Distributed model predictive control[J]. IEEE Control Systems Magazine, 2002, 22(1):44-52.
[7] Lima M L, Limon D, Camponogara E. Distributed satisficing MPC with guarantee of stability[J]. IEEE Trans on Automatic Control, 2016, 61(2):532-537.
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[9] Rubagotti M, Barcelli D, Bemporad A. Robust explicit model predictive control via regular piecewise affine approximation[J]. Int J of Control, 2014, 87(12):2583–2593.
[10] Zheng Y, Li S Y, Qiu H. Networked coordination-based distributed model predictive control for large-scale system[J]. IEEE Trans on Control Systems Technology,2013, 21(3):991-998.
[11] Li S Y, Zheng Y, Lin Z L. Impacted-region optimization for distributed model predictive control systems with constraints[J]. IEEE Trans on Automation Science and Engineering, 2015, 12(4):1447-1460.
[12] Kouvaritakis B, Cannon M, Rossiter J A. Who needs QP for linear MPC anyway?[J]. Automatica, 2002, 38(5):879-884.
[13] Li D W, Xi Y G. Quality guaranteed aggregation based model predictive control and stability analysis[J]. Science in China Series F:Information Sciences, 2009, 52(7):1145-1156.
[14]柴天佑.多变量自适应解耦控制及应用[M].北京:科学出版社, 2001:113-120.(Chai T Y. Multi-variables adaptive decoupling control and application[M]. Beijing:Science Press, 2001:113-120.)
[15]薛美盛,樊弟,魏衡华.多变量系统的广义预测控制解耦设计[J].控制工程, 2011, 18(1):39-42.(Xue M S, Fan D, Wei H H. Decoupling design of generalized predictive control for multi-variable systems[J]. Control Engineering of China, 2011, 18(1):39-42.)
[16]罗雄麟,张佳,许锋,等.操作变量与前馈变量转换对模型预测控制可行域影响机制[J].大连理工大学学报, 2017, 57(3):308-315.(Luo X L, Zhang J, Xu F, et al. Influential mechanism of switch between manipulated variables and feed-forward variables on feasible region of model predictive control[J]. J of Dalian University of Technology, 2017,57(3):308-315.)
[17]丁宝苍.工业预测控制[M].北京:机械工业出版社,2016:95-101.(Ding B C. Industrial predictive control[M]. Beijing:China Machine Press, 2016:95-101.)
[18]《运筹学》教材编写组编.运筹学[M].第3版.北京:清华大学出版社, 2005:8-15, 176-178.(Compilation of teaching materials for operations research. Operations research[M]. 3rd ed. Beijing:Tsinghua University Press, 2005:8-15, 176-178.)
[19]戴连奎,于玲,田学民,等.过程控制工程[M].第3版.北京:化学工业出版社, 2012:114-133.(Dai L K, Yu L, Tian X M, et al. Process control engineering[M]. 3rd ed. Beijing:Chemical Industrial Press, 2012:114-133.)
[20] Zhao M, Ding B C. Distributed model predictive control for constrained nonlinear systems with decoupled local dynamics[J]. ISA Trans, 2015, 55:1-12.
[21] Ding B C, Ge L, Pan H G, et al. Distributed MPC for tracking and formation of homogeneous multi-agent system with time-varying communication topology[J].Asian J of Control, 2016, 18(3):1030-1041.
[2]邹涛,丁宝苍,张端.模型预测控制工程应用导论[M].北京:化学工业出版社, 2010:1-10.(Zou T, Ding B C, Zhang D. MPC:An introduction to industrial application[M]. Beijing:Chemical Industrial Press, 2010:1-10.)
[3]席裕庚,李德伟,林姝.模型预测控制—–现状与挑战[J].自动化学报, 2013, 39(3):222-236.(Xi Y G, Li D W, Lin S. Model predictive control—status and challenges[J]. Acta Automatica Sinica, 2013,39(3):222-236.)
[4] Salsbury T, Mhaskar P, Qin S J. Predictive control methods to improve energy efficiency and reduce demand in buildings[J]. Computers and Chemical Engineering,2013, 51:77-85.
[5] Mayne D Q. Model predictive control:Recent developments and future promise[J]. Automatica, 2014,50:2967-2986.
[6] Camponogara E, Jia D, Krogh B H, et al. Distributed model predictive control[J]. IEEE Control Systems Magazine, 2002, 22(1):44-52.
[7] Lima M L, Limon D, Camponogara E. Distributed satisficing MPC with guarantee of stability[J]. IEEE Trans on Automatic Control, 2016, 61(2):532-537.
[8] Bemporad A, Morari M, Dua V, et al. The explicit linear quadratic regulator for constrained systems[J].Automatica, 2002, 38(1):3-20.
[9] Rubagotti M, Barcelli D, Bemporad A. Robust explicit model predictive control via regular piecewise affine approximation[J]. Int J of Control, 2014, 87(12):2583–2593.
[10] Zheng Y, Li S Y, Qiu H. Networked coordination-based distributed model predictive control for large-scale system[J]. IEEE Trans on Control Systems Technology,2013, 21(3):991-998.
[11] Li S Y, Zheng Y, Lin Z L. Impacted-region optimization for distributed model predictive control systems with constraints[J]. IEEE Trans on Automation Science and Engineering, 2015, 12(4):1447-1460.
[12] Kouvaritakis B, Cannon M, Rossiter J A. Who needs QP for linear MPC anyway?[J]. Automatica, 2002, 38(5):879-884.
[13] Li D W, Xi Y G. Quality guaranteed aggregation based model predictive control and stability analysis[J]. Science in China Series F:Information Sciences, 2009, 52(7):1145-1156.
[14]柴天佑.多变量自适应解耦控制及应用[M].北京:科学出版社, 2001:113-120.(Chai T Y. Multi-variables adaptive decoupling control and application[M]. Beijing:Science Press, 2001:113-120.)
[15]薛美盛,樊弟,魏衡华.多变量系统的广义预测控制解耦设计[J].控制工程, 2011, 18(1):39-42.(Xue M S, Fan D, Wei H H. Decoupling design of generalized predictive control for multi-variable systems[J]. Control Engineering of China, 2011, 18(1):39-42.)
[16]罗雄麟,张佳,许锋,等.操作变量与前馈变量转换对模型预测控制可行域影响机制[J].大连理工大学学报, 2017, 57(3):308-315.(Luo X L, Zhang J, Xu F, et al. Influential mechanism of switch between manipulated variables and feed-forward variables on feasible region of model predictive control[J]. J of Dalian University of Technology, 2017,57(3):308-315.)
[17]丁宝苍.工业预测控制[M].北京:机械工业出版社,2016:95-101.(Ding B C. Industrial predictive control[M]. Beijing:China Machine Press, 2016:95-101.)
[18]《运筹学》教材编写组编.运筹学[M].第3版.北京:清华大学出版社, 2005:8-15, 176-178.(Compilation of teaching materials for operations research. Operations research[M]. 3rd ed. Beijing:Tsinghua University Press, 2005:8-15, 176-178.)
[19]戴连奎,于玲,田学民,等.过程控制工程[M].第3版.北京:化学工业出版社, 2012:114-133.(Dai L K, Yu L, Tian X M, et al. Process control engineering[M]. 3rd ed. Beijing:Chemical Industrial Press, 2012:114-133.)
[20] Zhao M, Ding B C. Distributed model predictive control for constrained nonlinear systems with decoupled local dynamics[J]. ISA Trans, 2015, 55:1-12.
[21] Ding B C, Ge L, Pan H G, et al. Distributed MPC for tracking and formation of homogeneous multi-agent system with time-varying communication topology[J].Asian J of Control, 2016, 18(3):1030-1041.
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