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Nonlinear Model Predictive Control: A Self-Adaptive Approach
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DOI：10.1021/ie901693w.png)
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Model predictive control (MPC) is an online application based on dynamic models. Its application faces two major obstacles: (i) computational constraints and (ii) the need to accurately simulate the process by a model that properly predicts how the plant will behave in the future. Implementation of MPC is not always possible in large-scale or industrial applications due to the computational complexity of MPC and to the dimensionality of the models. To facilitate MPC implementations, this paper proposes a self-adaptive approach based on simplified (or reduced-order) nonlinear models. The proposed methodology yields an MPC that adjusts the dimension of the model according to both the current process conditions and the control objectives. The self-adaptive approach is described and validated on an industrial case study, a C4-splitter.
Keyword:
OPTIMIZING CONTROL
PATH OPTIMIZATION
GRADE TRANSITION
MULTIPLE MODEL
STRATEGY
IDENTIFICATION
PERSPECTIVES
REDUCTION
OPERATION
SYSTEMS
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期刊
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IF:
3.9
论文数:
4.0W
被引数:
9.6W
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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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