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Laguerre function-based quasi-infinite horizon nonlinear model predictive control

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Abstract

This paper proposes Laguerre function-based quasi-infinite horizon nonlinear model predictive control (QIH-NMPC) in order to solve the trio control problems of strong nonlinearities, very noisy control signals and highly compute-intensive control laws. In the proposed method, a nonlinear dynamic model is used for process state prediction, while Laguerre function is used for modeling future control signal sequence. The proposed method therefore combines the closed-loop stability guaranteed attribute of QIH-NMPC with the noise-filtering ability of Laguerre function. An unstable nonlinear two-state, single input system and a non-minimum phase four tank system are used to illustrate the effectiveness of the proposed method. The simulation results obtained show that the proposed Laguerre function-based QIH-NMPC compared favorably with Kautz function-based QIH-NMPC. Both the Laguerre and Kautz function-based QIH-NMPC result in smoother and more cautious control signals in the face of very noisy process measurements compared with the traditional QIH-NMPC. The proposed method also requires fewer parameters to achieve the same performance as the traditional QIH-NMPC and is less computationally intensive.

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No specific data or unique material was used for this work.

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The codes used for the simulations in this work may be made available on request.

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Acknowledgements

The author grateful acknowledges anonymous reviewers whose comments have helped to improve the manuscript significantly.

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  1. PSE Laboratory, Department of Chemical Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria

    Ayorinde Bamimore

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  1. Ayorinde Bamimore
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Bamimore, A. Laguerre function-based quasi-infinite horizon nonlinear model predictive control. Int. J. Dynam. Control 11, 2380–2397 (2023). https://doi.org/10.1007/s40435-023-01118-4

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