Journal article
Shortest-prediction-horizon non-linear model-predictive control with guaranteed asymptotic stability
International journal of control, v 80(10), pp 1533-1543
01 Oct 2007
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
This paper presents a continuous-time shortest-prediction-horizon model-predictive control method that provides optimal output regulation with guaranteed closed-loop asymptotic stability within an assessable domain of attraction. The closed-loop stability is ensured by requiring plant state variables to satisfy a hard, Lyapunov, inequality constraint. Whenever the output regulation alone cannot ensure asymptotic closed-loop stability, the closed-loop system evolves while being at the hard constraint. Once the closed-loop system enters a state-space region in which the output regulation can ensure asymptotic stability, the hard constraint becomes inactive. Consequently, the non-linear control method is applicable to stable and unstable plants, whether non-minimum- or minimum-phase. A major shortcoming of unconstrained, shortest-prediction-horizon model-predictive control, which is equivalent to input-output linearization, is its inapplicability to plants operating at a non-minimum-phase steady state. This work addresses the major shortcoming. The control method is implemented on a chemical reactor with multiple steady states, to show its application and performance. The simulation results demonstrate that the closed-loop system is asymptotically stable for all physically-meaningful initial conditions.
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Details
- Title
- Shortest-prediction-horizon non-linear model-predictive control with guaranteed asymptotic stability
- Creators
- C. Panjapornpon - Drexel UniversityM. Soroush - Drexel University
- Publication Details
- International journal of control, v 80(10), pp 1533-1543
- Publisher
- Taylor & Francis Group
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Chemical and Biological Engineering
- Web of Science ID
- WOS:000249589400001
- Scopus ID
- 2-s2.0-34648814065
- Other Identifier
- 991019168814804721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Automation & Control Systems