Efficient speed control of DC motors: imitation learning with fuzzy logic expert systems - Güray Sonugür (Automatika)
Two learning-based methods were employed in this study to control DC motor speeds. The first method is one of the data-driven control strategies. It identifies the system and learns the motor characteristics by evaluating the input-output data of each motor. In contrast to traditional methods, which require distinct models for each motor, the second method uses a single dataset, offering a generalized solution across many DC motor characteristics. Imitation learning (IL) was used to develop this model. IL is a type of machine learning where an expert system is imitated to learn a given task. The expert system was designed using fuzzy logic control principles, and an observation-action dataset was generated. This dataset presents the optimum response of DC motors to voltage changes and disturbances. An adaptive neuro-fuzzy inference system (ANFIS) was used as a supervised learning method. Unlike simulation-based studies, this study analyzes controllers in real-world scenarios, making it extremely relevant to industrial applications. Real-time experiments with three motors of different characteristics revealed that the IL-based controller significantly outperformed the data-driven method in terms of robustness, stability, and transient response metrics.