| Title |
Data-based predictive control via direct weight optimization |
| Authors |
SALVADOR ORTIZ, JOSÉ RAMÓN, Munoz de la Pena, D. , Alamo, T. , Bemporad, A. |
| External publication |
Si |
| Means |
IFAC-PapersOnLine |
| Scope |
Proceedings Paper |
| Nature |
Científica |
| SJR Quartile |
3 |
| SJR Impact |
0.29800 |
| Publication date |
01/01/2018 |
| ISI |
000451092800054 |
| DOI |
10.1016/j.ifacol.2018.11.059 |
| Abstract |
In this paper we propose a novel data-based predictive control scheme in which the prediction model is obtained from a linear combination of past system trajectories. The proposed controller optimizes the weights of this linear combination taking into account simultaneously performance and the variance of the estimation error. For unconstrained systems, dynamic programming is used to obtain an explicit linear solution of a finite or infinite horizon optimal control problem. When constraints are taken into account, the controller needs to solve online a quadratic optimization problem to obtain the optimal weights, possibly considering also local information to improve the performance and estimation. A simulation example of the application of the proposed controller to a quadruple-tank system is provided. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. |
| Keywords |
Predictive control; Database; Dynamic programming; Direct weight optimization; Model-free control |
| Universidad Loyola members |
|
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