Performance enhancement of multivariable model reference optimal adaptive motor speed controller using error-dependent hyperbolic gain functions

The main contribution of this paper is to formulate a robust-adaptive and stable state-space speed control strategy for DC motors. The linear-quadratic-integral (LQI) controller is utilized as the baseline controller for optimal speed-regulation, accurate reference-tracking and elimination of steady...

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Main Authors: Omer Saleem, Mohsin Rizwan, Khalid Mahmood-ul-Hasan, Muaaz Ahmad
Format: Article
Language:English
Published: Taylor & Francis Group 2020-01-01
Series:Automatika
Subjects:
Online Access:http://dx.doi.org/10.1080/00051144.2019.1688508
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spelling doaj-3050d439cbbb462e94d07a794cac68632020-11-25T02:00:06ZengTaylor & Francis GroupAutomatika0005-11441848-33802020-01-0161111713110.1080/00051144.2019.16885081688508Performance enhancement of multivariable model reference optimal adaptive motor speed controller using error-dependent hyperbolic gain functionsOmer Saleem0Mohsin Rizwan1Khalid Mahmood-ul-Hasan2Muaaz Ahmad3Department of Electrical Engineering, National University of Computer and Emerging SciencesUniversity of Engineering & TechnologyUniversity of Engineering and TechnologyDepartment of Electrical Engineering, National University of Computer and Emerging SciencesThe main contribution of this paper is to formulate a robust-adaptive and stable state-space speed control strategy for DC motors. The linear-quadratic-integral (LQI) controller is utilized as the baseline controller for optimal speed-regulation, accurate reference-tracking and elimination of steady-state fluctuations in the motor’s response. To reject the influence of modelling errors, the LQI controller is augmented with a Lyapunov-based model reference adaptation system (MRAS) that adaptively modulates the controller gains while maintaining the asymptotic stability of the controller. To further enhance the system’s robustness against parametric uncertainties, the adaptation gains of MRAS online gain-adjustment law are dynamically adjusted, after every sampling interval, using smooth hyperbolic functions of motor’s speed-error. This modification significantly improves the system’s response-speed and damping against oscillations, while ensuring its stability under all operating conditions. It dynamically re-configures the control-input trajectory to enhance the system’s immunity against the detrimental effects of random faults occurring in practical motorized systems such as bounded impulsive-disturbances, modelling errors, and abrupt load–torque variations. The efficacy of the proposed control strategy is validated by conducting credible hardware-in-the-loop experiments on QNET 2.0 DC Motor Board. The experimental results successfully validate the superior tracking accuracy and disturbance-rejection capability of the proposed control strategy as compared to other controller variants benchmarked in this article.http://dx.doi.org/10.1080/00051144.2019.1688508model reference adaptive controllqi controllyapunov theoryhyperbolic functionqnet 2.0 dc motor board
collection DOAJ
language English
format Article
sources DOAJ
author Omer Saleem
Mohsin Rizwan
Khalid Mahmood-ul-Hasan
Muaaz Ahmad
spellingShingle Omer Saleem
Mohsin Rizwan
Khalid Mahmood-ul-Hasan
Muaaz Ahmad
Performance enhancement of multivariable model reference optimal adaptive motor speed controller using error-dependent hyperbolic gain functions
Automatika
model reference adaptive control
lqi control
lyapunov theory
hyperbolic function
qnet 2.0 dc motor board
author_facet Omer Saleem
Mohsin Rizwan
Khalid Mahmood-ul-Hasan
Muaaz Ahmad
author_sort Omer Saleem
title Performance enhancement of multivariable model reference optimal adaptive motor speed controller using error-dependent hyperbolic gain functions
title_short Performance enhancement of multivariable model reference optimal adaptive motor speed controller using error-dependent hyperbolic gain functions
title_full Performance enhancement of multivariable model reference optimal adaptive motor speed controller using error-dependent hyperbolic gain functions
title_fullStr Performance enhancement of multivariable model reference optimal adaptive motor speed controller using error-dependent hyperbolic gain functions
title_full_unstemmed Performance enhancement of multivariable model reference optimal adaptive motor speed controller using error-dependent hyperbolic gain functions
title_sort performance enhancement of multivariable model reference optimal adaptive motor speed controller using error-dependent hyperbolic gain functions
publisher Taylor & Francis Group
series Automatika
issn 0005-1144
1848-3380
publishDate 2020-01-01
description The main contribution of this paper is to formulate a robust-adaptive and stable state-space speed control strategy for DC motors. The linear-quadratic-integral (LQI) controller is utilized as the baseline controller for optimal speed-regulation, accurate reference-tracking and elimination of steady-state fluctuations in the motor’s response. To reject the influence of modelling errors, the LQI controller is augmented with a Lyapunov-based model reference adaptation system (MRAS) that adaptively modulates the controller gains while maintaining the asymptotic stability of the controller. To further enhance the system’s robustness against parametric uncertainties, the adaptation gains of MRAS online gain-adjustment law are dynamically adjusted, after every sampling interval, using smooth hyperbolic functions of motor’s speed-error. This modification significantly improves the system’s response-speed and damping against oscillations, while ensuring its stability under all operating conditions. It dynamically re-configures the control-input trajectory to enhance the system’s immunity against the detrimental effects of random faults occurring in practical motorized systems such as bounded impulsive-disturbances, modelling errors, and abrupt load–torque variations. The efficacy of the proposed control strategy is validated by conducting credible hardware-in-the-loop experiments on QNET 2.0 DC Motor Board. The experimental results successfully validate the superior tracking accuracy and disturbance-rejection capability of the proposed control strategy as compared to other controller variants benchmarked in this article.
topic model reference adaptive control
lqi control
lyapunov theory
hyperbolic function
qnet 2.0 dc motor board
url http://dx.doi.org/10.1080/00051144.2019.1688508
work_keys_str_mv AT omersaleem performanceenhancementofmultivariablemodelreferenceoptimaladaptivemotorspeedcontrollerusingerrordependenthyperbolicgainfunctions
AT mohsinrizwan performanceenhancementofmultivariablemodelreferenceoptimaladaptivemotorspeedcontrollerusingerrordependenthyperbolicgainfunctions
AT khalidmahmoodulhasan performanceenhancementofmultivariablemodelreferenceoptimaladaptivemotorspeedcontrollerusingerrordependenthyperbolicgainfunctions
AT muaazahmad performanceenhancementofmultivariablemodelreferenceoptimaladaptivemotorspeedcontrollerusingerrordependenthyperbolicgainfunctions
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