Abstract
We study a stabilizing multi-model predictive control strategy for controlling nonlinear process at different operating conditions. The control algorithm is a receding horizon scheme with a quasi-infinite horizon objective function that has finite and infinite horizon cost components. The finite horizon cost consists of free input variables that direct the system towards a terminal region which contains the desired operating point. The infinite horizon cost has an upper bound and steers the system to the desired operating point. The system is represented by a sequence of piecewise linear models. Based on the condition of the system states, the sequence of piecewise linear models is updated and the controller's objective function switches form quasi-infinite to infinite horizon objective function. This results in a hybrid control structure. A recent approach in the analysis of hybrid systems that uses multiple Lyapunov functions is employed in the stability analysis of the closed-loop system. The stabilizing hybrid control strategy is illustrated on two examples and their closed-loop stability properties are studied.
Original language | English (US) |
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Pages (from-to) | 81-90 |
Number of pages | 10 |
Journal | Journal of Process Control |
Volume | 16 |
Issue number | 2 |
DOIs | |
State | Published - Feb 2006 |
Externally published | Yes |
Keywords
- Hybrid systems
- Linear matrix inequalities
- Model predictive control
- Multiple models
- Stability
ASJC Scopus subject areas
- Process Chemistry and Technology
- Control and Systems Engineering
- Industrial and Manufacturing Engineering