On the Optimality and Convergence Properties of the Iterative Learning Model Predictive Controller
| Autor: | Ugo Rosolia, Yingzhao Lian, Emilio T. Maddalena, Giancarlo Ferrari-Trecate, Colin N. Jones |
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| Rok vydání: | 2023 |
| Předmět: |
iterative algorithms
iterative learning control Systems and Control (eess.SY) Electrical Engineering and Systems Science - Systems and Control Computer Science Applications optimal control mpc Optimization and Control (math.OC) Control and Systems Engineering FOS: Mathematics FOS: Electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Mathematics - Optimization and Control predictive control |
| Zdroj: | IEEE Transactions on Automatic Control. 68:556-563 |
| ISSN: | 2334-3303 0018-9286 |
| DOI: | 10.1109/tac.2022.3148227 |
| Popis: | In this technical note we analyse the performance improvement and optimality properties of the Learning Model Predictive Control (LMPC) strategy for linear deterministic systems. The LMPC framework is a policy iteration scheme where closed-loop trajectories are used to update the control policy for the next execution of the control task. We show that, when a Linear Independence Constraint Qualification (LICQ) condition holds, the LMPC scheme guarantees strict iterative performance improvement and optimality, meaning that the closed-loop cost evaluated over the entire task converges asymptotically to the optimal cost of the infinite-horizon control problem. Compared to previous works this sufficient LICQ condition can be easily checked, it holds for a larger class of systems and it can be used to adaptively select the prediction horizon of the controller, as demonstrated by a numerical example. Comment: technical note |
| Databáze: | OpenAIRE |
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