For automatic guided vehicles (AGVs), maximizing the operating time with maximum energy efficiency is the most important factor that increases work efficiency. In this study, the fuel-cell-powered AGV (FCAGV) system was modeled and optimized control and design were carried out to obtain high tracking performance with minimum power consumption. Firstly, a full mathematical model of FCAGV, which involves the AGV, the fuel cell, DC/DC converters and motors, was obtained. Then, particle swarm optimization (PSO)-based intelligent PID and I controllers were developed for maximizing the route-tracking performance of AGV and voltage-tracking performance of the DC/DC converter with reduced power consumption. PSO was used to determine the optimal parameters of controllers and the values of DC/DC converters’ components. The performance of the full AGV system was analyzed for different paths. The results show that the sufficient path-tracking and voltage-tracking performance was obtained for AGV and DC/DC converters, respectively. The average tracking errors according to global coordinate system are 0.0061 m at the x axis, 0.0572 m at the y axis and 0.0228 rad at rotational axis. The obtained average voltage-tracking errors for each DC/DC converters were approximately 0.8033 V. These results indicate that the developed controllers with optimal coefficients work successfully with small voltage and path-tracking errors. During this motion, the average consumed power from the fuel cell was observed as 58.2675 W. These results show that the designed optimized intelligent controllers have sufficient performance with high energy efficiency and maximum route tracking.

This study presents an experimental study combining multiple sensor data and employing different deep learning algorithms to detect drone propeller damage. Under controlled conditions, vibration, acoustic and temperature data were simultaneously collected from quadcopter equipped with accelerometer, microphone and temperature sensors for a healthy propeller and 8 different faulty propellers. The data were collected for the quadcopter operating both indoors and outdoors, and external factors such as noise and wind were also included in the analysis. Short Time Fourier Transform, spectrograms and standard deviation, Kurtosis and skewness analyses were performed on the collected data in both frequency and time domains to improve the performance. After that, data was analyzed using deep learning architectures such as Convolutional Neural Networks, ResNet50, EfficientNet, MobileNetV2 and VGG16, to classify cases with damaged and intact propellers. The performance of the models was evaluated comparatively using accuracy, precision, sensitivity, and F1-score metrics. The results show that EfficientNet emerged as the most accurate and reliable, achieving an accuracy of 97.2% and the lowest log loss (0.0868), indicating both strong predictive capability and high confidence in its predictions. ResNet (87.5%) and VGG16 (89.2%) also performed well, with balanced precision and recall, making them suitable candidates for scenarios where accuracy is important, but resources are moderately constrained. Ablation and hypermeters analyses were applied to the EfficientNet architecture, which is the most successful model, and the results showed that the proposed approach should be carefully configured against modality deficiencies and that the evaluated architecture exhibits stable and reliable performance. These results demonstrate that multi-sensor fusion significantly increases detection accuracy, and deep learning models perform successfully in complex damage scenarios.