ADVANCED ADAPTIVE CONTROL STRATEGIES APPLICATION FOR SPEED REGULATION OF A 12V SMALL DC GEARED MOTOR

Abstract

This paper investigates the implementation and performance of adaptive control techniques for a 12V small geared DC motor characterized by modeling errors and input disturbances. This paper discusses the following two primary approaches: Adaptive Radial Basis Function Neural Network (ARBFNN) Controllers and Model Reference Adaptive Control (MRAC). In model uncertainty, MRAC and ARBFNN outperformed the simple Proportional-Integral (PI) controller. The study is further expanded to involve Robust MRAC and Adaptive Sliding Mode Radial Basis Function Neural Network (ASRBFNN) Controllers to counter the compounded effects of model uncertainty and input disturbances. The versions of the robust controllers performed better than the conventional PI controller in cases involving both uncertainties and disturbances. Implementations were done on a 12V geared DC motor testbed with an Arduino microcontroller and MATLAB's System Identification Toolbox. The results from simulations and experimental applications highlight the greater flexibility and disturbance rejection capability of the developed advanced adaptive control schemes, making them perform better than standard PI controllers under challenging conditions.

Keywords

• Model-based adaptive control
• Adaptive sliding mode control
• DC motor
• Speed control
• Unknown system parameters

12V KÜÇÜK DC DİŞLİ MOTORUNUN HIZ DÜZENLEMESİ İÇİN GELİŞMİŞ UYARLANABİLİR KONTROL STRATEJİLERİ UYGULAMASI

Abstract

Bu makale, modelleme hataları ve giriş bozuklukları ile karakterize edilen 12 V küçük dişlili DC motor için uyarlamalı kontrol tekniklerinin uygulanmasını ve performansını araştırmaktadır. Bu makale aşağıdaki iki temel yaklaşımı tartışmaktadır: Uyarlamalı Radyal Baz Fonksiyonlu Sinir Ağı (ARBFNN) Denetleyicileri ve Model Referanslı Uyarlamalı Kontrol (MRAC). Model belirsizliğinde, MRAC ve ARBFNN basit Oransal-İntegral (PI) denetleyiciden daha iyi performans göstermiştir. Çalışma, model belirsizliğinin ve giriş bozukluklarının bileşik etkilerini dengelemek için Gürbüz MRAC ve Uyarlamalı Kayan Modlu Radyal Baz Fonksiyonlu Sinir Ağı (ASRBFNN) Denetleyicilerini içerecek şekilde daha da genişletilmiştir. Gürbüz denetleyicilerin versiyonları, hem belirsizlik hem de bozukluk içeren durumlarda geleneksel PI denetleyicisinden daha iyi performans göstermiştir. Uygulamalar, bir Arduino mikrodenetleyici ve MATLAB'ın Sistem Tanımlama Aracı ile bir 12 V dişlili DC motor test ortamında gerçekleştirilmiştir. Simülasyon ve deneysel uygulamalardan elde edilen sonuçlar, geliştirilen ileri adaptif kontrol şemalarının daha fazla esneklik ve bozulmayı reddetme kabiliyetine sahip olduğunu ve bu sayede zorlu koşullar altında standart PI kontrolörlerinden daha iyi performans gösterdiğini ortaya koymaktadır.

Keywords

• Model tabanlı adaptive control
• Adaptif kayan kipli kontrol
• DC motor
• Hız kontrolü
• Bilinmeyen sistem parametreleri

References

1. Khan, H., Khatoon, S., and Gaur, P., “Comparison of Various Controller Design for the Speed Control of DC Motors Used in Two-Wheeled Mobile Robots”, International Journal of Information Technology, 13(2), 713-720, 2021.
2. Liang, X. et al., “Output Feedback Asymptotic Tracking Control for Uncertain DC Motors”, International Journal of Control, Automation and Systems, 21(8), 2748-2759, 2023.
3. Mahajan, N. P., and Deshpande, S. B., “Study of Nonlinear Behavior of DC Motor Using Modeling and Simulation”, International Journal of Scientific and Research Publications, 3(3), 1-6, 2013.
4. Shekhar, A., and Sharma, A., “Review of Model Reference Adaptive Control”, International Conference on Information, Communication, Engineering and Technology (ICICET), 2018, 1-5.
5. Ioannou, P., and Fidan, B., Adaptive Control Tutorial, Society for Industrial and Applied Mathematics, Philadelphia, 2006.
6. Benosman, M., “Model-Based vs Data-Driven Adaptive Control: An Overview”, International Journal of Adaptive Control and Signal Processing, 32(5), 753-776, 2018.
7. Chao, K.H., Hsieh, C.T., and Chen, X.J., “A Robust Controller Based on Extension Sliding Mode Theory for Brushless DC Motor Drives”, Electronics, 13(20), 4028, 2024.
8. Alejandro-Sanjines, U. et al., “Adaptive PI Controller Based on a Reinforcement Learning Algorithm for Speed Control of a DC Motor”, Biomimetics, 8(5), 434, 2023.

9. Na, J., Ren, X., and Zheng, D., “Adaptive Control for Nonlinear Pure-Feedback Systems With High-Order Sliding Mode Observer”, IEEE Transactions on Neural Networks and Learning Systems, 24(3), 370-382, 2013.
10. Liu, L., Liu, Y., and Chen, C. L. P., “Adaptive Neural Network Control for a DC Motor System with Dead-Zone”, Nonlinear Dynamics, 72, 141-147, 2012.
11. MathWorks, MATLAB and Statistics Toolbox Release 2012b, MathWorks Inc., Natick, Massachusetts, 2012.
12. Wu, W., “DC Motor Parameter Identification Using Speed Step Responses”, Modelling and Simulation in Engineering, 2012, 1937-1941.
13. Narendra, K. S., and Annaswamy, A. M., Stable Adaptive Systems, Prentice Hall, Englewood Cliffs, New Jersey, 1989.
14. Nguyen, N. T., Model-Reference Adaptive Control, Springer, 2018.
15. Volyanskyy, K. Y., Haddad, W. M., and Calise, A. J., “A New Neuroadaptive Controller Architecture for Nonlinear Uncertain Dynamical Systems: Beyond σ- and e-Modifications”, 47th IEEE Conference on Decision and Control, 2008, 80-85.
16. Rao, M. P. R. V., and Leckie, T. J., “Robust Adaptive Control: Improved e-Modification”, IFAC Proceedings, 31(22), 127-132, 1998.
17. Jiang, Y. et al., “A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots”, Complexity, 2017(1), 1-14, 2017
18. Liu, J., Intelligent Controller Design and MATLAB Simulation, Springer, 2018.
19. Liu, J., Radial Basis Function Neural Network Control for Mechanical Systems, Springer, 2013.
20. Chapra, S. and Canale, R., Numerical Methods for Engineers, McGraw-Hill Education, New York, 2014.