Linear robust adaptive model predictive control: Computational complexity and conservatism

In this paper, we present a robust adaptive model predictive control (MPC) scheme for linear systems subject to parametric uncertainty and additive disturbances. The proposed approach provides a computationally efficient formulation with theoretical guarantees (constraint satisfaction and stability)...

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Vydáno v:Proceedings of the IEEE Conference on Decision & Control s. 1383 - 1388
Hlavní autoři: Kohler, Johannes, Andina, Elisa, Soloperto, Raffaele, Muller, Matthias A., Allgower, Frank
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.12.2019
Témata:
Additives
Computational complexity
Electron tubes
Hypercubes
Robustness
Uncertainty
ISSN:2576-2370
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Shrnutí:In this paper, we present a robust adaptive model predictive control (MPC) scheme for linear systems subject to parametric uncertainty and additive disturbances. The proposed approach provides a computationally efficient formulation with theoretical guarantees (constraint satisfaction and stability), while allowing for reduced conservatism and improved performance due to online parameter adaptation. A moving window parameter set identification is used to compute a fixed complexity parameter set based on past data. Robust constraint satisfaction is achieved by using a computationally efficient tube based robust MPC method. The predicted cost function is based on a least mean squares point estimate, which ensures finite-gain ℒ 2 stability of the closed loop. The overall algorithm has a fixed (user specified) computational complexity. We illustrate the applicability of the approach and the trade-off between conservatism and computational complexity using a numerical example.
ISSN:2576-2370
DOI:10.1109/CDC40024.2019.9028970