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Predefined-time Stabilization of 4D Permanent-magnet Synchronous Motor System with Deterministic and Stochastic Disturbances Using Linear Time-varying Control Input

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  • Control Theory and Applications
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Abstract

This paper presents a novel continuous predefined-time convergent control algorithm for higher-order systems with incompletely and completely measured states subjected to deterministic disturbances and stochastic noises. The algorithm enables a control designer to assign the convergence time as a control law parameter of in advance, regardless of initial conditions. The designed control law employs time-varying linear control input terms substituting for previously used exponential ones. The control algorithm efficiency is demonstrated by numerical simulations for a four-dimensional (4D) permanent-magnet synchronous motor (PMSM) system with incompletely and completely measured states affected by both deterministic disturbances satisfying Lipschitz conditions and stochastic white Gaussian noises. The numerical simulations confirm the algorithm efficiency in each considered case. To the best of the authors’ knowledge, this is the first attempt to design a predefined-time convergent continuous control law for higher-order systems with incompletely measurable states subjected to both deterministic disturbances satisfying Lipschitz conditions and stochastic white Gaussian noises, using a linear time-varying control input.

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Authors and Affiliations

  1. Facultad de Ciencias Fisico-Matematicas, Universidad Autonoma de Nuevo Leon, San Nicolas de los Garza, Nuevo Leon, Mexico

    Nain de la Cruz & Michael Basin

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  1. Nain de la Cruz
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  2. Michael Basin
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Correspondence to Michael Basin.

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Conflicts of Interests

To the best of our knowledge, there are no conflicts of interests/competing interests related to this work.

Nain de la Cruz received his bachelor’s degree in mechatronic engineering from the Technological Institute of Nuevo Leon and a master’s degree in industrial physics engineering from the Autonomous University of Nuevo Leon. His research interests include mathematical modelling, sliding mode control, and mechatronic systems.

Michael Basin received his Ph.D. degree in physical and mathematical sciences with major in automatic control and system analysis from the Moscow Aviation University (MAI) in 1992. He is currently a Full Professor with the Autonomous University of Nuevo Leon, Mexico, and the Ningbo Institute of Intelligent Equipment Technology, China. Starting from 1992, Dr. Basin published more than 400 research papers in international referred journals and conference proceedings. He is the author of the monograph “New Trends in Optimal Filtering and Control for Polynomial and Time-Delay Systems,” published by Springer. His works are cited more than 7000 times (h index = 47). Dr. Basin has supervised 17 doctoral and 10 master’s theses. He has served as the Editor-in-Chief and serves as the Co-Editor-in-Chief of Journal of The Franklin Institute, the Senior Editor of IEEE/ASME Transactions on Mechatronics, an Associate Editor of Automatica, IEEE Transactions on Systems, Man and Cybernetics: Systems, IET-Control Theory and Applications, International Journal of Systems Science, Neural Networks. Dr. Basin was awarded a title of Highly Cited Researcher by Thomson Reuters, the publisher of Science Citation Index, in 2009 and listed in “100 000 Leading Scientists in the World”; he is a regular member of the Mexican Academy of Sciences. Prof. Basin has been honored as a Fellow of Prominent Talent (Qian Ren) Program of Zhejiang Province, China. His research interests include optimal filtering and control problems, stochastic systems, time-delay systems, identification, sliding mode control and variable structure systems, and applications to mechatronic and transportation systems.

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de la Cruz, N., Basin, M. Predefined-time Stabilization of 4D Permanent-magnet Synchronous Motor System with Deterministic and Stochastic Disturbances Using Linear Time-varying Control Input. Int. J. Control Autom. Syst. 21, 1852–1865 (2023). https://doi.org/10.1007/s12555-022-0401-4

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  • DOI: https://doi.org/10.1007/s12555-022-0401-4

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