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Volume 18
Issue 17
10.3390/en18174570
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Model-Free Predictive Control of Inverter Based on Ultra-Local Model and Adaptive Super-Twisting Sliding Mode Observer

by
Wensheng Luo
1,
Zejian Shu
1,
Ruifang Zhang
2,
Jose I. Leon
3,
Abraham M. Alcaide
3 and
Leopoldo G. Franquelo
3,*
1
School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
2
School of Astronautics, Harbin Institute of Technology, Harbin 150001, China
3
Electronic Engineering Department, University of Seville, 41092 Seville, Spain
*
Author to whom correspondence should be addressed.
Energies 2025, 18(17), 4570; https://doi.org/10.3390/en18174570
Submission received: 21 July 2025 / Revised: 26 August 2025 / Accepted: 27 August 2025 / Published: 28 August 2025
(This article belongs to the Special Issue Modeling, Scheduling, Optimising, and Control of Power Grids and Integrated Energy Systems)
Versions Notes

Abstract

Model predictive control (MPC) is significantly affected by parameter mismatch in inverter applications, whereas model-free predictive control (MFPC) avoids parameter dependence through the ultra-local model (ULM). However, the traditional MFPC based on the algebraic method needs to store historical data for multiple cycles, which results in a sluggish dynamic response. To address the above problems, this paper proposes a model-free predictive control method based on the ultra-local model and an adaptive super-twisting sliding mode observer (ASTSMO). Firstly, the effect of parameter mismatch on the current prediction error of conventional MPC is analyzed through theoretical analysis, and a first-order ultra-local model of the inverter is established to enhance robustness against parameter variations. Secondly, a super-twisting sliding mode observer with adaptive gain is designed to estimate the unknown dynamic terms in the ultra-local model in real time. Finally, the superiority of the proposed method is verified through comparative validation against conventional MPC and the algebraic-based MFPC. Simulation results demonstrate that the proposed method can significantly enhance robustness against parameter variations and shorten the settling time during dynamic transients.
Keywords: model-free predictive control; ultra-local model; adaptive super-twisting sliding mode observer; parameter robustness model-free predictive control; ultra-local model; adaptive super-twisting sliding mode observer; parameter robustness

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MDPI and ACS Style

Luo, W.; Shu, Z.; Zhang, R.; Leon, J.I.; Alcaide, A.M.; Franquelo, L.G. Model-Free Predictive Control of Inverter Based on Ultra-Local Model and Adaptive Super-Twisting Sliding Mode Observer. Energies 2025, 18, 4570. https://doi.org/10.3390/en18174570

AMA Style

Luo W, Shu Z, Zhang R, Leon JI, Alcaide AM, Franquelo LG. Model-Free Predictive Control of Inverter Based on Ultra-Local Model and Adaptive Super-Twisting Sliding Mode Observer. Energies. 2025; 18(17):4570. https://doi.org/10.3390/en18174570

Chicago/Turabian Style

Luo, Wensheng, Zejian Shu, Ruifang Zhang, Jose I. Leon, Abraham M. Alcaide, and Leopoldo G. Franquelo. 2025. "Model-Free Predictive Control of Inverter Based on Ultra-Local Model and Adaptive Super-Twisting Sliding Mode Observer" Energies 18, no. 17: 4570. https://doi.org/10.3390/en18174570

APA Style

Luo, W., Shu, Z., Zhang, R., Leon, J. I., Alcaide, A. M., & Franquelo, L. G. (2025). Model-Free Predictive Control of Inverter Based on Ultra-Local Model and Adaptive Super-Twisting Sliding Mode Observer. Energies, 18(17), 4570. https://doi.org/10.3390/en18174570

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