A learning-based approach for robust predictive control design for multi-input multi-output (MIMO) linear systems is presented. The identification stage allows to obtain multi-step ahead prediction models and to derive tight uncertainty bounds. The identified models are then used by a robust model predictive controller, that is designed for the tracking problem with stabilizing properties. The proposed algorithm is used to control the nonlinear model of a quadruple-tank process using data gathered from it. The resulting controller, suitably modified to account for the nonlinear system gain matrix, results in remarkable tracking performances.

Learning-based predictive control for MIMO systems

E. Terzi;M. Farina;L. Fagiano;R. Scattolini
2019

Abstract

A learning-based approach for robust predictive control design for multi-input multi-output (MIMO) linear systems is presented. The identification stage allows to obtain multi-step ahead prediction models and to derive tight uncertainty bounds. The identified models are then used by a robust model predictive controller, that is designed for the tracking problem with stabilizing properties. The proposed algorithm is used to control the nonlinear model of a quadruple-tank process using data gathered from it. The resulting controller, suitably modified to account for the nonlinear system gain matrix, results in remarkable tracking performances.
2019 IEEE 58th Conference on Decision and Control (CDC)
AUT
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1141457
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