Stability proof for computationally efficient predictive control in the uncertain case
Large system dimensions and/or a possible need for long horizons restrict the applicability of predictive control. Earlier work showed that by sacrificing a certain degree of optimality it is possible to define efficient algorithms which reduce considerably computational complexity. This note consid...
| Main Authors: | , , , |
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| Format: | Conference item |
| Published: |
2003
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| Summary: | Large system dimensions and/or a possible need for long horizons restrict the applicability of predictive control. Earlier work showed that by sacrificing a certain degree of optimality it is possible to define efficient algorithms which reduce considerably computational complexity. This note considers a class of such algorithms which deploy just one degree of freedom. It is shown that it is possible to: (i) derive a priori stability guarantees over much larger regions of the state space and for a larger class of control trajectories; (ii) account for a particular class of model uncertainty and (iii) show that even though, use is made of ellipsoidal invariant sets, nevertheless the stability results are not limited to the volume of such ellipsoids. |
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