Advanced adaptive control strategies application for speed regulation of a 12V small dc geared motor

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.