This paper presents a type-3 fuzzy logic (T3-FL)-based controller for Load Frequency Control (LFC) in microgrids, focusing on addressing the challenges of renewable energy integration. The integration of renewable sources such as wind and solar leads to power fluctuations and frequency deviations that compromise system stability. The proposed T3-FL controller incorporates advanced features like online adaptation of membership functions and enhanced computational capacity to manage uncertainties in renewable power generation and load variations. The design principles prioritize robustness, adaptability to stochastic variations, and effective frequency stabilization. Simulation results demonstrate that the T3-FL controller significantly improves the microgrid’s stability by efficiently mitigating frequency fluctuations across multiple dynamic scenarios.

Accurate short-term wind power forecasting plays a critical role in maintaining grid stability due to the inherently irregular and fluctuating nature of wind resources. Deep learning models such as LSTM, GRU, and CNN are widely used to learn temporal dynamics; however, their ability to capture or adapt to the underlying symmetries and asymmetries inherent in real-world wind energy data remains insufficiently explored. In this study, we evaluate and compare these models using authentic production and meteorological data from the Tokat Wind Farm in Türkiye. The forecasting scenarios were designed to reflect the temporal structure of the dataset, including seasonal patterns, recurrent behaviors, and the symmetry-breaking effects caused by abrupt changes in wind speed and operational variability. The results demonstrate that the LSTM model most effectively captures the temporal relationships and partial symmetries within the data, yielding the lowest error metrics (RMSE = 0.2355, MAE = 0.1249, MAPE = 25.16%, R² = 0.8199). GRU and CNN offer moderate performance but show reduced sensitivity to asymmetric fluctuations, particularly during periods of high variability. The comparative findings highlight how symmetry-informed model behavior—specifically the ability to learn repeating temporal structures and respond to symmetry-breaking events—can significantly influence forecasting accuracy. This study provides practical insights into the interplay between data symmetries and model performance, supporting the development of more robust deep learning approaches for real-world wind energy forecasting.

Direct current (DC) motors—which have widespread usage areas such as industrial devices, automobiles, and household appliances—are also widely used in robotic systems that have developed rapidly in recent years and need precise control. Since the DC motors with encoder on its shaft used in robots have low resolution, accurate speed measurement cannot be made and this makes robot control difficult. In this study, a novel period–based measurement approach is presented to more accuracy and precision determine DC motor velocity at high speed with signals obtained from a low-resolver incremental encoder. First, the signals of the dual channel encoder are applied to the exclusive or gate to produce a single output signal with twice the frequency in order to reduce the number of interrupt pins used in the microcontroller and optimize the program cycle time. Based on the sample count calculated with the entered by user reference speed and microcontroller loop time, the mean filter is then applied to the half-wave period of the signal received through the Arduino board interrupt pin. In order to demonstrate the effectiveness of the method, measurement made with three different methods as follows: traditional period–based method, half-wave period method with only one channel, and exclusive or output signal half-wave period method. The results show the high-speed measurements through the presented method outperform other methods at high DC motor speeds. Especially in robots using more than one motor, the processing load of microcontroller has reduced and enabled the use of other peripheral interfaces by reducing the number of microcontroller pins used for the encoder. In addition, utilizing the results obtained with this method, the proportional–integral–derivative (PID) control of the motors in a four-wheel drive mobile robot is carried out for different speeds.

In this study, a Bluetooth-based Android application interface is developed to perform a manual and automatic control of a four-wheel-driven mobile robot designed for education, research, health, military, and many other fields. The proposed application with MIT App Inventor consists of three components: the main screen, the manual control screen, and the automatic control screen. The main screen is where the actions of the control preference selection such as manual control and automatic control and the Bluetooth connection between the mobile robot and Android phone occur. When the robot is operated manually for calibration or manual positioning purposes, the manual control screen is employed to adjust the desired robot movement and speed by hand. In the case of the need for automatic motion control, the desired robot position and speed data are inserted into the mobile robot processor through the automatic control screen. At the first stage of the work, the proposed Android application is developed with the design and block editors of the MIT App Inventor. The compiled application is then installed on the Android phone. Next, the communication between the Arduino microcontroller used for the robot control with the Bluetooth protocol and the Android application is established. The accuracy of the data dispatched to the Arduino is tested on the serial connection screen. It is validated that the data from the Android application is transferred to Arduino smoothly. At the end of this study, the manual and automatic controls of the proposed mobile robot are performed experimentally and success of the coordination between the Android application and the mobile robot are demonstrated.