Abstract
A new robust model predictive control (MPC) algorithm is presented for stable, constrained, linear plants that is a direct generalization of the nominally stabilizing regulator presented by Rawlings and Muske. Model uncertainty is parametrized by a list of possible plants. Robust stability is achieved through the addition of constraints that prevent the sequence of optimal controller costs from increasing for the true plant. Asymptotic stability is demonstrated through a Lyapunov argument. Simulation experiments demonstrate the performance of the algorithm for two example processes.
Keywords
Control theory (sociology)Model predictive controlGeneralizationStability (learning theory)MathematicsExponential stabilityRobust controlLyapunov functionSequence (biology)Controller (irrigation)Linear systemMathematical optimizationControl (management)Computer scienceEngineeringControl systemNonlinear systemArtificial intelligenceMachine learning
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Publication Info
- Year
- 1997
- Type
- article
- Volume
- 68
- Issue
- 4
- Pages
- 797-818
- Citations
- 156
- Access
- Closed
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Cite This
Thomas A. Badgwell
(1997).
Robust model predictive control of stable linear systems.
International Journal of Control
, 68
(4)
, 797-818.
https://doi.org/10.1080/002071797223343
Identifiers
- DOI
- 10.1080/002071797223343