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Combined model approximation techniques and multiparametric programming for explicit nonlinear model predictive control
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DOI：10.1016/j.compchemeng.2012.01.009.png)
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This work presents a methodology to derive explicit multiparametric controllers for nonlinear systems, combining model approximation techniques and multiparametric model predictive control (mp-MPC) algorithms. Particular emphasis is given to an approach that applies a nonlinear model reduction technique, based on balancing of empirical gramians, which generates a reduced order model suitable for nonlinear mp-MPC algorithms. This approach is compared with a recently proposed method that uses a meta-modelling based model approximation technique which can be directly combined with standard multiparametric programming algorithms. The methodology is illustrated for two nonlinear models, of a distillation column and a train of CSTRs, respectively. Crown Copyright (c) 2012 Published by Elsevier Ltd. All rights reserved.
Keyword:
Multiparametric programming
Nonlinear MPC
Explicit MPC
Nonlinear model reduction
Model approximation
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Perspectives in Multiparametric Programming and Explicit Model Predictive Control多参数编程和显式模型预测控制的观点
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