基不确定规划的预测控制系统经济性能评估算法
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摘要
针对过程工业普遍存在的扰动和不确定性动态对控制系统经济性能的影响,依据控制要求对过程变量设置相应的机会约束条件来处理这一问题,并将经济性能评估问题转化为一系列不确定规划问题。对系统关键变量方差的合理估计是对预测控制系统进行经济性能评估的一个关键步骤,为了提高评估结果的合理性,引入LQG性能基准估计过程方差的变化率。在求解优化问题获得经济性能评估结果的基础上对控制系统经济效益潜力进行分析,并确定了提高控制系统经济性能的最佳途径和相应的控制策略。通过预测控制系统仿真算例说明了该评估算法的有效性和可操作性。
A stochastic optimization approach for economic performance assessment of the model predictive control(MPC) under uncertainty is presented.Performance evaluation problems are formulated as the stochastic problems which incorporate the uncertainties in both process operation and economic objective.Such problem formulation helps to identify the opportunity of improving the profitability of the process by taking appropriate risks.Both the steady state economic benefit and the optimal operation conditions can be obtained by solving the defined economic optimization problems.Further,the proposed method uses the linear quadratic Guassian(LQG) benchmark other than conventional minimum variance control(MVC) benchmark to estimate potential of variance reduction,which results in a more reasonable performance assessment.To exploit feasible economic performance of the MPC systems,the proposed approach considers the uncertainties induced by process variability and evaluates the economic performance through stochastic solving optimization problem.Results of the performance evaluation provide a guideline for the control system tuning to realize the potential improvement in profitability of process.The proposed algorithm is also illustrated by a simulated example of the model predictive control system.
A stochastic optimization approach for economic performance assessment of the model predictive control(MPC) under uncertainty is presented.Performance evaluation problems are formulated as the stochastic problems which incorporate the uncertainties in both process operation and economic objective.Such problem formulation helps to identify the opportunity of improving the profitability of the process by taking appropriate risks.Both the steady state economic benefit and the optimal operation conditions can be obtained by solving the defined economic optimization problems.Further,the proposed method uses the linear quadratic Guassian(LQG) benchmark other than conventional minimum variance control(MVC) benchmark to estimate potential of variance reduction,which results in a more reasonable performance assessment.To exploit feasible economic performance of the MPC systems,the proposed approach considers the uncertainties induced by process variability and evaluates the economic performance through stochastic solving optimization problem.Results of the performance evaluation provide a guideline for the control system tuning to realize the potential improvement in profitability of process.The proposed algorithm is also illustrated by a simulated example of the model predictive control system.
引文
1 Xu F,Huang B and Akande S.Performance assessment of model predictive control for variability and constraint tuning.Ind Eng Chem Res,2007,46:1208-1219.
2 Muske K B.Estimation the economic benefit from improved process control.Ind Eng Chem Res,2003,42:4535-4544.
3 Zhou Y and Forbes J F.Determining controller benefits via probabilistic optimization.Int J Adapt Control Signal Process, 2003,17:553-568.
4 Huang B.A pragmatic approach towards assessment of control loop performance.Int J Adapt Control Signal Process,2003, 17:589-608.
5 Huang B and Shah S L.Performance Assessment of Control Loops.New York:Theory and Applications,1999.
6 Huang B and Edgar C T.Model validation for industrial model predictive control systems.Chemical Engineering Science,2000, 55:2315-2327.
7 Zhao C,Su H Y and Huang B.Economic control performance assessment with LQG benchmarking.American Control Conference,2008,19:362-3367.
8 Pu Li,Harvey A G and Wozny G Chance constrained programming approach to process optimization under uncertainty. Computers and Chemical Engineering,2008,34:699-708.
9 Zhang Y,Monder D and Forbes J F.Real-time optimization under parametric uncertainty:a probability constrained approach. Journal of Process Control,2002,12:373-389.
10 Young J,Swartz C L and Ross R.On the effects of constraints, economics and uncertain disturbances on dynamic operability assessment.Computers and Chemical Engineering,1996,20: 667-682.
11 Zhao C,Su H Y and Chu J.A pragmatic approach for assessing the economic performance of MPC systems and its industrial application.Chinese Journal of Chemical Engineering,2009,17: 456-466.
12 Pour N D and Shah S L.Perormance assessment of advanced supervisory Cregulatory control system with subspace LQG benchmark.Automatica,2010,46:1363-1368.
13 Pour N D and Shah S L.Consistency of noise covariance estimation in joint input/output closed-loop subspace identification with application in LQG benchmarking.Journal of Process Control,2010,19:1649-1656.
2 Muske K B.Estimation the economic benefit from improved process control.Ind Eng Chem Res,2003,42:4535-4544.
3 Zhou Y and Forbes J F.Determining controller benefits via probabilistic optimization.Int J Adapt Control Signal Process, 2003,17:553-568.
4 Huang B.A pragmatic approach towards assessment of control loop performance.Int J Adapt Control Signal Process,2003, 17:589-608.
5 Huang B and Shah S L.Performance Assessment of Control Loops.New York:Theory and Applications,1999.
6 Huang B and Edgar C T.Model validation for industrial model predictive control systems.Chemical Engineering Science,2000, 55:2315-2327.
7 Zhao C,Su H Y and Huang B.Economic control performance assessment with LQG benchmarking.American Control Conference,2008,19:362-3367.
8 Pu Li,Harvey A G and Wozny G Chance constrained programming approach to process optimization under uncertainty. Computers and Chemical Engineering,2008,34:699-708.
9 Zhang Y,Monder D and Forbes J F.Real-time optimization under parametric uncertainty:a probability constrained approach. Journal of Process Control,2002,12:373-389.
10 Young J,Swartz C L and Ross R.On the effects of constraints, economics and uncertain disturbances on dynamic operability assessment.Computers and Chemical Engineering,1996,20: 667-682.
11 Zhao C,Su H Y and Chu J.A pragmatic approach for assessing the economic performance of MPC systems and its industrial application.Chinese Journal of Chemical Engineering,2009,17: 456-466.
12 Pour N D and Shah S L.Perormance assessment of advanced supervisory Cregulatory control system with subspace LQG benchmark.Automatica,2010,46:1363-1368.
13 Pour N D and Shah S L.Consistency of noise covariance estimation in joint input/output closed-loop subspace identification with application in LQG benchmarking.Journal of Process Control,2010,19:1649-1656.