title: Adaptive MPC based on MIMO ARX-Laguerre model.
abstract: This paper proposes a method for synthesizing an adaptive predictive controller using a reduced complexity model. This latter is given by the projection of the ARX model on Laguerre bases. The resulting model is entitled MIMO ARX-Laguerre and it is characterized by an easy recursive representation. The adaptive predictive control law is computed based on multi-step-ahead finite-element predictors, identified directly from experimental input/output data. The model is tuned in each iteration by an online identification algorithms of both model parameters and Laguerre poles. The proposed approach avoids time consuming numerical optimization algorithms associated with most common linear predictive control strategies, which makes it suitable for real-time implementation. The method is used to synthesize and test in numerical simulations adaptive predictive controllers for the CSTR process benchmark.
authors.affiliation: Research Laboratory of Automatic Signal and Image Processing, National School of Engineers of Monastir, University of Monastir, 5019, Tunisia. Electronic address: benabdelwahed.imen@gmail.com.
authors.forename: Imen
authors.initials: I
authors.lastname: Ben Abdelwahed
authors.affiliation: Research Laboratory of Automatic Signal and Image Processing, National School of Engineers of Monastir, University of Monastir, 5019, Tunisia. Electronic address: abdelkader.mbarek@enim.rnu.tn.
authors.forename: Abdelkader
authors.initials: A
authors.lastname: Mbarek
authors.affiliation: Research Laboratory of Automatic Signal and Image Processing, National School of Engineers of Monastir, University of Monastir, 5019, Tunisia. Electronic address: kais.bouzrara@enim.rnu.tn.
authors.forename: Kais
authors.initials: K
authors.lastname: Bouzrara
keywords: ARX-Laguerre model; Adaptive predictive control; MIMO system modeling; Online identification; Pole optimization