This study proposed an advanced control system for air fryers by integrating fuzzy logic and fractal concepts to enhance the efficiency of nonlinear dynamic processes. Traditional air fryers require users to manually set cooking times, which can lead to inefficiencies. To address this issue, a fuzzy based controller with augmented output membership functions (MFs) is designed to automatically determine optimal cooking times based on food type and quantity. The system employs the mamdani model, which involves crisp input determination, fuzzification, rule evaluation, and defuzzification to generate precise output. Additionally, the proposed fuzzy fractal control approach leverages the fractal dimension to measure the complexity of the air fryer's dynamic behavior, while fuzzy logic captures expert knowledge and manages uncertainty in decision-making. The system is simulated using MATLAB Simulink R2016, with experimental validation conducted via a microcontroller utilizing pulse width modulation (PWM). Results demonstrate significant improvements, including a 68.2% reduction in root mean square error (RMSE) and 53.2% reduction in mean absolute percentage error (MAPE). By combining the strengths of fractal theory and fuzzy logic, this approach not only ensures optimal food quality but also saves time, reduce energy consumption, and provides a robust framework for controlling nonlinear systems. The proposed method highlights the potential of fuzzy fractal control in enhancing household appliances and other complex, dynamic processes.
Khokhar et al. (Tue,) studied this question.