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

Öz

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.

Anahtar Kelimeler

• 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

Öz

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.

Anahtar Kelimeler

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

Kaynakça

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