Herrera-Hernández, RamónReusser, CarlosCarvajal, RodrigoZamora, Ramon2026-03-162026-03-162026-03-09Scientific Reports, ISSN: 2045-2322 (Print); 2045-2322 (Online), Nature Portfolio. doi: 10.1038/s41598-026-41944-y2045-23222045-2322http://hdl.handle.net/10292/20778In this paper, we present a reformulation of both the predictive torque and flux control (PTC) scheme and the full-order adaptive observer (FAO) for induction machine drives. The proposed approach is based on a state-space representation expressed exclusively in terms of stator current and stator flux linkage, simplifying the observer structure and removing the explicit dependence on rotor flux variables found in conventional sensorless formulations. This representation is consistently applied within both the FAO and PTC frameworks, and second- and higher-order discrete-time models are derived using Taylor- and Runge-Kutta-based methods to enhance numerical accuracy and dynamic performance. The resulting FAO-PTC scheme is validated through Hardware-in-the-Loop simulations, demonstrating steady-state performance comparable to conventional designs, faster transient response, improved dynamic behaviour, and a reduced state-space order, albeit with slightly higher computational cost. Notably, simply employing a more accurate observer substantially enhances the performance of the sensorless scheme. Among the evaluated discretization strategies, the Taylor-based model provides the highest steady-state accuracy and fastest convergence, with only a modest increase in torque ripple. Overall, the proposed reformulated FAO-PTC framework achieves a balanced trade-off between accuracy, implementation simplicity, and computational efficiency for real-time sensorless induction machine drives.Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/Discrete-time modelsFull-order adaptive observerInduction machineModel predictive controlSensorless40 Engineering4008 Electrical Engineering4009 Electronics, Sensors and Digital Hardware7 Affordable and Clean EnergyJournal ArticleOpenAccess10.1038/s41598-026-41944-y