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

Wensheng Luo, Zejian Shu, Ruifang Zhang, Jose I. Leon, Abraham M. Alcaide and Leopoldo G. Franquelo ()
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Wensheng Luo: School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
Zejian Shu: School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
Ruifang Zhang: School of Astronautics, Harbin Institute of Technology, Harbin 150001, China
Jose I. Leon: Electronic Engineering Department, University of Seville, 41092 Seville, Spain
Abraham M. Alcaide: Electronic Engineering Department, University of Seville, 41092 Seville, Spain
Leopoldo G. Franquelo: Electronic Engineering Department, University of Seville, 41092 Seville, Spain

Energies, 2025, vol. 18, issue 17, 1-16

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 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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