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MPC: Current practice and challenges
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DOI：10.1016/j.conengprac.2011.12.004.png)
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Linear Model Predictive Control (MPC) continues to be the technology of choice for constrained, multivariable control applications in the process industry. Successful deployment of MPC requires getting right multiple aspects of the control problem. This includes the design of the underlying regulatory controls, design of the MPC(s), test design for model identification, model development, and dealing with nonlinearities. Approaches and techniques that are successfully applied in practice are described, including the challenges involved in ensuring a successful MPC application. Academic contributions are highlighted and suggestions provided for improving MPC. (c) 2011 Elsevier Ltd. All rights reserved.
Keyword:
Model predictive control
Model-based control
Constraints
Control system design
Modeling
Process identification
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