Nonlinear Adaptive Neural Control of Power Converter‐Driven DC Motor System: Design and Experimental Validation
ABSTRACT This article presents an intelligent adaptive neural control scheme to track the output speed trajectory in power converter‐driven DC motor system. The proposed technique integrates an adaptive polynomial‐neural network with a backstepping strategy to yield a robust control system for outpu...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Engineering Reports |
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| Online Access: | https://doi.org/10.1002/eng2.13025 |
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| author | Tousif Khan Nizami Sasank Das Gangula Ramanjaneya Reddy Udumula Arghya Chakravarty Fareed Ahmad Alireza Hosseinpour |
| author_facet | Tousif Khan Nizami Sasank Das Gangula Ramanjaneya Reddy Udumula Arghya Chakravarty Fareed Ahmad Alireza Hosseinpour |
| author_sort | Tousif Khan Nizami |
| collection | DOAJ |
| description | ABSTRACT This article presents an intelligent adaptive neural control scheme to track the output speed trajectory in power converter‐driven DC motor system. The proposed technique integrates an adaptive polynomial‐neural network with a backstepping strategy to yield a robust control system for output tracking in DC motor. Such a unification of online neural network‐based estimation and adaptive control, results in effective regulation of the output across a wide load torque uncertainties, besides yielding a promising transient and steady‐state performance. The stability of the entire closed‐loop system is ensured through Lyapunov stability criterion. The efficacy of the proposed strategy is revealed through an extensive experimental investigation under various operating points during start‐up, step‐reference tracking, and external step‐load torque disturbances. The real‐time experimentation is conducted on a laboratory prototype of power converter‐driven DC motor of 200 W, using dspace DS1104 control board with MPC8240 processor. The results obtained confirm an improvement in the transient response of the output speed by significantly reducing the settling time to 50% and yielding a steady state behavior with no peak over/undershoots during load disturbances, in contrast to other similar works presented in the literature intended for same the application. |
| format | Article |
| id | doaj-art-b401e8062a644671a28cff1620f231fe |
| institution | Kabale University |
| issn | 2577-8196 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Engineering Reports |
| spelling | doaj-art-b401e8062a644671a28cff1620f231fe2025-01-31T00:22:48ZengWileyEngineering Reports2577-81962025-01-0171n/an/a10.1002/eng2.13025Nonlinear Adaptive Neural Control of Power Converter‐Driven DC Motor System: Design and Experimental ValidationTousif Khan Nizami0Sasank Das Gangula1Ramanjaneya Reddy Udumula2Arghya Chakravarty3Fareed Ahmad4Alireza Hosseinpour5Department of Electrical & Electronics Engineering School of Engineering and Sciences, SRM University AP Amaravati IndiaDepartment of Electrical & Electronics Engineering School of Engineering and Sciences, SRM University AP Amaravati IndiaDepartment of Electrical & Electronics Engineering School of Engineering and Sciences, SRM University AP Amaravati IndiaDepartment of Electrical & Electronics Engineering School of Engineering and Sciences, SRM University AP Amaravati IndiaDepartment of Electrical Engineering SND College of Engineering and Research Center Nashik IndiaDepartment of Electrical Engineering University of Zabol Zabol IranABSTRACT This article presents an intelligent adaptive neural control scheme to track the output speed trajectory in power converter‐driven DC motor system. The proposed technique integrates an adaptive polynomial‐neural network with a backstepping strategy to yield a robust control system for output tracking in DC motor. Such a unification of online neural network‐based estimation and adaptive control, results in effective regulation of the output across a wide load torque uncertainties, besides yielding a promising transient and steady‐state performance. The stability of the entire closed‐loop system is ensured through Lyapunov stability criterion. The efficacy of the proposed strategy is revealed through an extensive experimental investigation under various operating points during start‐up, step‐reference tracking, and external step‐load torque disturbances. The real‐time experimentation is conducted on a laboratory prototype of power converter‐driven DC motor of 200 W, using dspace DS1104 control board with MPC8240 processor. The results obtained confirm an improvement in the transient response of the output speed by significantly reducing the settling time to 50% and yielding a steady state behavior with no peak over/undershoots during load disturbances, in contrast to other similar works presented in the literature intended for same the application.https://doi.org/10.1002/eng2.13025adaptive neural controlDC motor systemdisturbance rejectionexperimental validationtrajectory tracking |
| spellingShingle | Tousif Khan Nizami Sasank Das Gangula Ramanjaneya Reddy Udumula Arghya Chakravarty Fareed Ahmad Alireza Hosseinpour Nonlinear Adaptive Neural Control of Power Converter‐Driven DC Motor System: Design and Experimental Validation Engineering Reports adaptive neural control DC motor system disturbance rejection experimental validation trajectory tracking |
| title | Nonlinear Adaptive Neural Control of Power Converter‐Driven DC Motor System: Design and Experimental Validation |
| title_full | Nonlinear Adaptive Neural Control of Power Converter‐Driven DC Motor System: Design and Experimental Validation |
| title_fullStr | Nonlinear Adaptive Neural Control of Power Converter‐Driven DC Motor System: Design and Experimental Validation |
| title_full_unstemmed | Nonlinear Adaptive Neural Control of Power Converter‐Driven DC Motor System: Design and Experimental Validation |
| title_short | Nonlinear Adaptive Neural Control of Power Converter‐Driven DC Motor System: Design and Experimental Validation |
| title_sort | nonlinear adaptive neural control of power converter driven dc motor system design and experimental validation |
| topic | adaptive neural control DC motor system disturbance rejection experimental validation trajectory tracking |
| url | https://doi.org/10.1002/eng2.13025 |
| work_keys_str_mv | AT tousifkhannizami nonlinearadaptiveneuralcontrolofpowerconverterdrivendcmotorsystemdesignandexperimentalvalidation AT sasankdasgangula nonlinearadaptiveneuralcontrolofpowerconverterdrivendcmotorsystemdesignandexperimentalvalidation AT ramanjaneyareddyudumula nonlinearadaptiveneuralcontrolofpowerconverterdrivendcmotorsystemdesignandexperimentalvalidation AT arghyachakravarty nonlinearadaptiveneuralcontrolofpowerconverterdrivendcmotorsystemdesignandexperimentalvalidation AT fareedahmad nonlinearadaptiveneuralcontrolofpowerconverterdrivendcmotorsystemdesignandexperimentalvalidation AT alirezahosseinpour nonlinearadaptiveneuralcontrolofpowerconverterdrivendcmotorsystemdesignandexperimentalvalidation |