Regular Papers

International Journal of Control, Automation and Systems 2022; 20(5): 1582-1592

Published online April 21, 2022

https://doi.org/10.1007/s12555-020-0931-6

© The International Journal of Control, Automation, and Systems

Adaptive Generalized Predictive Control of Fractional Order Thermal Aluminum Rod

Abdelaziz Mouhou*, Abdelmajid Badri, and Abdelhakim Ballouk

Hassan II University of Casablanca

Abstract

This paper presents the Adaptive Generalized Predictive Control (AGPC) of the heat conduction in a given aluminum rod modeled using fractional calculus. The oustaloup approximation is used to perform the integer model of the temperature of the rod, then a balanced truncation (BT) model order reduction technique is used to obtain a low integer order model used as internal prediction model of the predictive controller. In order to increase the performance metrics of the AGPC controller, the Improved Grey Wolf Optimizer (IGWO) is applied to obtain the best synthesis parameters which are the minimum prediction horizon, the maximum prediction horizon, the control horizon and the weighting factor of the control signal. Simulation results of IGWO-AGPC versus the GA based AGPC and the Ant Lion Optimizer based Fractional Order PIλDµ (ALO-FOPID) validates the effectiveness of the proposed approach.

Keywords Adaptive control, fractional calculus, grey wolf optimizer, predictive control.

Article

Regular Papers

International Journal of Control, Automation and Systems 2022; 20(5): 1582-1592

Published online May 1, 2022 https://doi.org/10.1007/s12555-020-0931-6

Copyright © The International Journal of Control, Automation, and Systems.

Adaptive Generalized Predictive Control of Fractional Order Thermal Aluminum Rod

Abdelaziz Mouhou*, Abdelmajid Badri, and Abdelhakim Ballouk

Hassan II University of Casablanca

Abstract

This paper presents the Adaptive Generalized Predictive Control (AGPC) of the heat conduction in a given aluminum rod modeled using fractional calculus. The oustaloup approximation is used to perform the integer model of the temperature of the rod, then a balanced truncation (BT) model order reduction technique is used to obtain a low integer order model used as internal prediction model of the predictive controller. In order to increase the performance metrics of the AGPC controller, the Improved Grey Wolf Optimizer (IGWO) is applied to obtain the best synthesis parameters which are the minimum prediction horizon, the maximum prediction horizon, the control horizon and the weighting factor of the control signal. Simulation results of IGWO-AGPC versus the GA based AGPC and the Ant Lion Optimizer based Fractional Order PIλDµ (ALO-FOPID) validates the effectiveness of the proposed approach.

Keywords: Adaptive control, fractional calculus, grey wolf optimizer, predictive control.

IJCAS
May 2024

Vol. 22, No. 5, pp. 1461~1759

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