The induction motor (IM) drives are prone to various uncertainties, disturbances, and non-linear dynamics. A high-performance control system is essential in the outer loop to guarantee the accurate convergence of speed and torque to the required value. Super-twisting sliding mode control (ST-SMC) and fractional-order calculus have been widely used to enhance the sliding mode control (SMC) performance for IM drives. This paper combines the ST-SMC and fractional-order calculus attributes to propose a novel super-twisting fractional-order sliding mode control (ST-FOSMC) for the outer loop speed control of the model predictive torque control (MPTC)-based IM drive system. The MPTC of the IM drive requires some additional sensors for speed control. This paper also presents a novel machine learning-based Gaussian Process Regression (GPR) framework to estimate the speed of IM. The GPR model is trained using the voltage and current dataset obtained from the simulation of a three-phase MPTC based IM drive system. The performance of the GPR-based ST-FOSMC MPTC drive system is evaluated using various test cases, namely (a) electric fault incorporation, (b) parameter perturbation, and (c) load torque variations in Matlab/Simulink environment. The stability of ST-FOSMC is validated using a fractional-order Lyapunov function. The proposed control and estimation strategy provides effective and improved performance with minimal error compared to the conventional proportional integral (PI) and SMC strategies.

Unexploded ordnance (UXO) poses a persistent threat to safety in post-conflict areas, where manual clearance is slow, dangerous, and financially expensive. UAV-based real-time detection provides a more secure and scalable approach. However, UXO typically appear as small, low-contrast targets embedded in complex backgrounds, which significantly degrade the performance of conventional YOLO-based detectors. To address these challenges, this paper proposes UXO-YOLO, a lightweight, attention-enhanced YOLOv8s framework specifically designed for UXO detection in UAV imagery. A Spatial-Efficient Channel Attention (SECA) module is introduced in the neck to enhance fine-grained feature extraction and improve UXO-to-background discrimination while maintaining real-time efficiency. In addition, a C2f Coordinate Attention (C2f CA) module is integrated into the backbone to enable direction-aware feature encoding and improve localization robustness for small UXO targets. Furthermore, the large-scale P5 detection head is removed to eliminate unnecessary computation and focus the network on small-scale target detection. Owing to the sensitive and specific nature of UXO in military contexts, publicly available UXO-specific datasets are extremely limited. To address this limitation, a custom Unexploded Aerial Bomb (UAB) dataset was constructed using UAV-captured RGB images under controlled testing conditions. The proposed model is evaluated on the UAB dataset and further assessed on the public UAVDT benchmark to examine cross-domain robustness. Experimental results show that UXO-YOLO achieves 95.8% mAP₅₀, 68.2% mAP_50:95, and 91.9% recall on the UAB dataset, and 86.7% mAP₅₀, 47.9% mAP_50:95, and 81.8% recall on the UAVDT dataset, while preserving real-time inference efficiency. These results indicate the suitability of UXO-YOLO for UAV-based humanitarian UXO detection applications. The reported real-time performance is evaluated on a GPU-equipped laptop platform, and on-board validation on embedded UAV hardware remains part of future work.