Journal article
Shortest-prediction-horizon non-linear model-predictive control
Chemical engineering science, v 53(2), pp 273-292
1998
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
This article concerns non-linear control of single-input-single-output processes with input constraints and deadtimes. The problem of input-output linearization in continuous time is formulated as a model-predictive control problem, for processes with full-state measurements and for processes with incomplete state measurements and deadtimes. This model-predictive control formulation allows one (i) to establish the connections between model-predictive and input-output linearizing control methods; and (ii) to solve
directly the problems of constraint handling and windup in input-output linearizing control. The derived model-predictive control laws have the shortest possible prediction horizon and explicit analytical form, and thus their implementation does not require on-line optimization. Necessary conditions for stability of the closed-loop system under the constrained dynamic control laws are given. The connections between (a) the developed control laws and (b) the model state feedback control and the modified internal model control are established. The application and performance of the derived controllers are demonstrated by numerical simulations of chemical and biochemical reactor examples.
Metrics
Details
- Title
- Shortest-prediction-horizon non-linear model-predictive control
- Creators
- Sairam Valluri - Drexel UniversityMasoud Soroush - Drexel UniversityMasoud Nikravesh - Lawrence Berkeley National Laboratory
- Publication Details
- Chemical engineering science, v 53(2), pp 273-292
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Chemical and Biological Engineering
- Web of Science ID
- WOS:000071386300006
- Scopus ID
- 2-s2.0-0031963581
- Other Identifier
- 991019168485504721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Engineering, Chemical