Title: Fractional-order multi-model predictive control for nonlinear processes

Authors: Imen Deghboudj; Samir Ladaci

Addresses: SP-Lab Laboratory, Department of Electronics, University of Constantine 1, Constantine 25000, Algeria ' Department of EEA, National Polytechnic School of Constantine, Ali Mendjli, Constantine 25000, Algeria; SP-Lab Laboratory, Department of Electronics, University of Constantine 1, Constantine 25000, Algeria

Abstract: This paper proposes a novel fractional-order multi-model predictive control (FO-MMPC) design to deal with a class of nonlinear fractional order systems. Based on the assumption that the plant is governed by a fractional order nonlinear dynamical model, we are able for each operating region, to determine the linear portion of the nonlinear process as a single fractional order pole transfer function. The predictive model is then approximated by rational transfer functions using the singularity function approach in the frequency domain. The main contribution in this control approach is the use of a switching algorithm between fractional order prediction models that are approximating the nonlinear system dynamics for different operating points and input range intervals. By using the FO-MMPC for controlling the level of a conical tank system with nonlinear dynamics using a multiple fractional-order predictive models, it is shown by means of numerical simulations the effectiveness of the proposed control strategy.

Keywords: model predictive control; MPC; fractional-order system; multi-model system; nonlinear system; singularity function approximation; conical tank system.

DOI: 10.1504/IJAAC.2021.116426

International Journal of Automation and Control, 2021 Vol.15 No.4/5, pp.611 - 630

Received: 20 Jul 2019
Accepted: 13 Jan 2020
Published online: 23 Jul 2021 *

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