Nonlinear MPC for Tracking for a Class of Nonconvex Admissible Output Sets
| Autor: | Emanuele Garone, Daniel R. Ramirez, Andres Cotorruelo, Daniel Limon |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Mathematical optimization Optimization problem Linear programming Computer science MathematicsofComputing_NUMERICALANALYSIS Convex set 02 engineering and technology Extension (predicate logic) Homeomorphism Computer Science Applications Nonlinear system Model predictive control 020901 industrial engineering & automation Control and Systems Engineering TheoryofComputation_ANALYSISOFALGORITHMSANDPROBLEMCOMPLEXITY Convergence (routing) Electrical and Electronic Engineering |
| Zdroj: | IEEE Transactions on Automatic Control. 66:3726-3732 |
| ISSN: | 2334-3303 0018-9286 |
| DOI: | 10.1109/tac.2020.3025297 |
| Popis: | This article presents an extension to the nonlinear model predictive control (MPC) for tracking scheme able to guarantee convergence even in cases of nonconvex output admissible sets. This is achieved by incorporating a convexifying homeomorphism in the optimization problem, allowing it to be solved in the convex space. A novel class of nonconvex sets is also defined for which a systematic procedure to construct a convexifying homeomorphism is provided. This homeomorphism is then embedded in the MPC optimization problem in such a way that the homeomorphism is no longer required in closed form. Finally, the effectiveness of the proposed method is showcased through an illustrative example. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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