Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000. Digital Object Identifier 10.1109/ACCESS.2023.0322000 Efficient Adaptive Regulation Strategy for Control Position of Induction Motors RUBEN TAPIA-OLVERA 1 , ANTONIO VALDERRABANO-GONZALEZ 2 (Member, IEEE), and FRANCISCO BELTRAN-CARBAJAL 3 1 Department of Electrical Energy, Universidad Nacional Autónoma de México, Mexico City, 04510, Mexico; (e-mail:rtapia@fi-b.unam.mx) 2 Universidad Panamericana. Facultad de Ingeniería. Álvaro del Portillo 49, Zapopan, Jalisco, 45010, México.(e-mail: avalder@up.edu.mx) 3 Department of Energy, Universidad Autónoma Metropolitana, Unidad Azcapotzalco, Mexico City, 02200, Mexico; (e-mail: fbeltran@azc.uam.mx) Corresponding author: Antonio Valderrabano-Gonzalez (e-mail: avalder@up.edu.mx). This work was supported by Universidad Panamericana through the Fondo Fomento a la Investigación UP 2023, project UP-CI-2023-GDL-09-ING. ABSTRACT In this paper, we introduce an efficient adaptive algorithm based on B-spline neural networks for trajectory tracking of angular position for industrial induction motors. This strategy is developed in a two- axis reference frame and the regulation algorithm is based on four main stages: a) flux observer; b) internal control loop; c) determination of required electrical currents and; d) calculation of the three-phase input voltages. The strategy considers an algebraic regulation scheme based on the model. The control parameters are tuning online to attain the best dynamic behavior, besides, the proposed adaptive controller is subject to non-modeled components as an 84-pulse voltage source converter included in this study. These two aspects make the main contribution of this article. The proposed high-performance strategy for trajectory tracking of angular position is demonstrated by simulation results using the parameters of two induction motors of 500 hp and 50 hp, respectively. INDEX TERMS Adaptive regulation, angular position trajectory tracking, efficient driver, high performance, induction motors. I. INTRODUCTION N OWADAYS, the industry demands more efficient strate- gies for motor operation, particularly in highly demand- ing applications. In this context, the induction machine con- tinues to gain more significance in industrial processes where it was not widely utilized until a few years ago, thanks to the rapid development of power electronics. Consequently, any study must consider the impact of using power electronic devices. The control of angular rotor position is a crucial requirement in the industry, typically addressed by direct current (DC) motors or permanent magnet synchronous mo- tors [1]. However, induction motors could also participate in these rigorous tasks; nevertheless, their operation must be guaranteed in terms of reliability, security, and efficiency. Moreover, new growing applications such as electric vehicles see the induction motors as a very important alternative, but the advantages of their use must be guaranteed by the control strategy [2]. Important control strategies for induction motors are avail- able in scientific literature such as field orientation control, direct torque control, model predictive control, sliding mode control, and intelligent control techniques. However, the de- pendency on model parameters, complexity, and highly de- manding computational effort of intelligent techniques causes a gap that needs to be covered with new efficient strategies including the effect of power electronic drives [3]. A strat- egy for rotor position control of induction motors based on field-oriented control, incorporating a scheme that utilizes neural networks to address time-varying dynamics and a non- linear structure is presented in [4]. Additionally, it involves fractional-order proportional-integral controllers, which have been tuned using an artificial bee colony optimization al- gorithm. In [5] an adaptive scheme based on fuzzy neural networks is presented for rotor position control of induction motors which emulates the conventional proportional-integral (PI) controller. That scheme requires two groups of parame- ters to be trained in hidden layers to reach the desired control performance, which was adapted for the study case. In the same line of adaptive control techniques, [6] presents a con- trol of induction motors using an adaptive high gain observer VOLUME 11, 2023 1 This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3375346 This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4