一种基于MPC结构评价基准的预测控制器
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摘要
针对线性高斯二次型(LQG)基准在经济性能评估算法中不可达问题,提出基于模型预测控制(MPC)结构评价基准的经济性能评估算法。该算法利用扰动简化的过程模型设计控制器,真实的过程模型用来计算带有输入输出方差约束的MPC性能曲线,并以此建立经济性能评估优化命题,在对优化结果分析讨论的基础上得到经济性能评价结果,有效的避免了因模型差异带来的计算误差,仿真结果表明了该算法更具可达性。
As linear quadratic Gaussian(LQG)-based benchmark used in the economic performance evaluation algorithm is inaccessible, a new method based on model prediction control(MPC) structure evaluation benchmark is proposed for economic performance assessment. The algorithm uses the process model with simplified disturbance to design controller while the real process model is used to calculate the MPC performance curve with input and output variance constraints, based on which the economic performance evaluation optimization problem is built, and economic performance evaluation results are gotten based on the analysis of the optimization results. The proposed algorithm effectively avoids the calculation error caused by model, the simulation results show that the algorithm is more accessible.
As linear quadratic Gaussian(LQG)-based benchmark used in the economic performance evaluation algorithm is inaccessible, a new method based on model prediction control(MPC) structure evaluation benchmark is proposed for economic performance assessment. The algorithm uses the process model with simplified disturbance to design controller while the real process model is used to calculate the MPC performance curve with input and output variance constraints, based on which the economic performance evaluation optimization problem is built, and economic performance evaluation results are gotten based on the analysis of the optimization results. The proposed algorithm effectively avoids the calculation error caused by model, the simulation results show that the algorithm is more accessible.
引文
[1]Bauer M,Craig I K.Economic assessment of advanced process control-A survey and framework[J].Journal of Process Control,2008,18(1):2-18.
[2]Huang B,Qi F.Book review on process control performance assessment:from theory to implementation[J].IEEE Transactions on Automatic Control,2008,53(4):1083-1084.
[3]Martin G D,Turpin L E,Cline R P.Estimating control function benefits[J].Hydrocarbon Process,1991,69(3):68-73.
[4]Edgar T F.Control and operations:when does controllability equal profitability[J].Computer and Chemica Engineering.2004.29(2):41-49.
[5]Huang B.A Pragmatic approach towards assessment of Control loop Performance[J].International Journal of Adaptive Control Signal Process,2003,19(4):589-608.
[6]Muske K B.Estimation the economic benefit from improved Proeess control[J].Industry and Engineering Chemistry Research,2003,42(20):4543-4544.
[7]Huang B,Shah S L.Performance Assessment of Control Loops:Theory and Applications[M],London:Springer-Verlag.1999.
[8]Zhao C,Zhao Y,Su H Y,Huang B.Economic performance assessment of advanced process control with LQG bench marking[J].Journal of Process Control,2009,19(4):557-569.
[9]Xu Q L,Zhao C,Zhang D F,Aimin A,Zhang C.Data driven LQG bench making for economic performance assessment of advanced process control systems[C].Proceedings of the 2011 American Control Conference.San Francisco,USA:IEEE,2011.5085-5090.
[10]Liu Z,Gu Y,Xie L.MPC economic performance assessment based on equal-grid LQG benchmark[A].Proceedings of the 2011International Symposium on Advanced Control of Industrial Processes[C].Hangzhou,China:IEEE,2011.632-637.
[11]田学民.罗芝芬.王平.基于LQG基准的预测控制器经济敏感度分析及调节准则[J],自动化学报,2012,38(1):1513-11519.Tian X M,Luo Z F,Wang Ping.LQG-based sensitivity analysis and tuning guidelines in economic performance assessment of predictive controller[J].Acta Automatica Sinica,2012,38(1):1513-11519.
[12]Julien R H,Foley M W,Cluett W R.Performance assessment using a model predictive control benchmark[J].Journal of Process Control,2004,14(4):441-56.
[13]Ruan,X.,Bien,Z.and K.H.Park.Iterative learning controllers for discrete-time large-scale systems to track trajectories with distinct magnitudes[J],International Journal of Systems Science,2005,36(4):221–233.
[2]Huang B,Qi F.Book review on process control performance assessment:from theory to implementation[J].IEEE Transactions on Automatic Control,2008,53(4):1083-1084.
[3]Martin G D,Turpin L E,Cline R P.Estimating control function benefits[J].Hydrocarbon Process,1991,69(3):68-73.
[4]Edgar T F.Control and operations:when does controllability equal profitability[J].Computer and Chemica Engineering.2004.29(2):41-49.
[5]Huang B.A Pragmatic approach towards assessment of Control loop Performance[J].International Journal of Adaptive Control Signal Process,2003,19(4):589-608.
[6]Muske K B.Estimation the economic benefit from improved Proeess control[J].Industry and Engineering Chemistry Research,2003,42(20):4543-4544.
[7]Huang B,Shah S L.Performance Assessment of Control Loops:Theory and Applications[M],London:Springer-Verlag.1999.
[8]Zhao C,Zhao Y,Su H Y,Huang B.Economic performance assessment of advanced process control with LQG bench marking[J].Journal of Process Control,2009,19(4):557-569.
[9]Xu Q L,Zhao C,Zhang D F,Aimin A,Zhang C.Data driven LQG bench making for economic performance assessment of advanced process control systems[C].Proceedings of the 2011 American Control Conference.San Francisco,USA:IEEE,2011.5085-5090.
[10]Liu Z,Gu Y,Xie L.MPC economic performance assessment based on equal-grid LQG benchmark[A].Proceedings of the 2011International Symposium on Advanced Control of Industrial Processes[C].Hangzhou,China:IEEE,2011.632-637.
[11]田学民.罗芝芬.王平.基于LQG基准的预测控制器经济敏感度分析及调节准则[J],自动化学报,2012,38(1):1513-11519.Tian X M,Luo Z F,Wang Ping.LQG-based sensitivity analysis and tuning guidelines in economic performance assessment of predictive controller[J].Acta Automatica Sinica,2012,38(1):1513-11519.
[12]Julien R H,Foley M W,Cluett W R.Performance assessment using a model predictive control benchmark[J].Journal of Process Control,2004,14(4):441-56.
[13]Ruan,X.,Bien,Z.and K.H.Park.Iterative learning controllers for discrete-time large-scale systems to track trajectories with distinct magnitudes[J],International Journal of Systems Science,2005,36(4):221–233.