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Interval state estimation-based robust model predictive control for linear parameter varying systems
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DOI：10.1002/rnc.5676.png)
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Abstract
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For linear parameter varying (LPV) systems with unknown system states, this article investigates an interval state estimation-based robust model predictive control algorithm. Two interval state estimation approaches, including interval observer systems and zonotope-based box computations, are considered to estimate the upper and lower bounds of system states. The on-line interval estimation error boxes are contained within the scaled and time-varying ellipsoidal robust positively invariant sets. Then, the centers of the state constraint boxes are steered to a region near the origin. When the interval estimation error boxes and the centers of state constraint boxes simultaneously converge to the neighborhood of the origin, the controlled LPV systems are robust stable.
Keywords:
interval observer
LPV systems
model predictive control
output feedback
set-membership estimation
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