Automation

In the era of renewable dominated grids, integration of dynamic loads such as EV charging stations have increased the operational challenges in multifolds, particularly in DC microgrids (DC MGs). Traditional battery-dominated grid energy management strategies (EMSs) are often not capable of handling fast transients due to the limitations of battery electrochemistry. To overcome this limitation, a hierarchical hybrid energy management strategy is proposed that uses the combination of data-driven and metaheuristic algorithms. The designed optimization framework consists of particle swarm optimization (PSO) and a neural network (NN) implemented in the central controller of a 4-bus ringmain DC MG. An efficient decoupling of fast and slow storage dynamics is performed, where the supercapacitor (SC) is optimized using the NN and the battery is optimized using PSO. This selective optimization reduces the computational overhead on the PSO making it more feasible for real-time implementation. The designed hybrid PSO-Neural EMS framework is initially designed on MATLAB and further validated on a real-time hardware setup. Robustness of the control scheme is verified with various case studies, such as renewable intermittency, dynamic loading and partial shading scenarios. An effective optimization of the SC in both transient and heavy load scenarios are observed. LabVIEW interfacing is used for MODBUS-based interaction with PV emulators and DC-DC converters.

Two-wheeled self-balancing robots (TWSBRs) are difficult to control because they are nonlinear, unstable, and underactuated, particularly when balance, velocity regulation, and line tracking must be achieved simultaneously. This paper proposes a hybrid parallel control architecture for a line-following TWSBR operating on straight segments, ${90}^{\circ }$ curves, and a ${15}^{\circ }$ slope. Balance stabilization is handled by a classical PD loop, while traslational velocity is regulated by an adaptive fuzzy PI controller, and line following is performed with an adaptive fuzzy PD controller. The fuzzy modules adjust the effective gains based on tracking errors, thereby improving robustness to disturbances, sensor noise, and changes in operating conditions. The complete strategy is implemented on a low-cost PIC18F4550 microcontroller. Experiments show that the fuzzy line-following controller reduces the orientation tracking error compared with a conventional controller. At $0.10{\displaystyle \frac{\mathrm{m}}{\mathrm{s}}}$, RMSE decreases from $0.042\mathrm{rad}$ to $0.038\mathrm{rad}$, and at $0.175{\displaystyle \frac{\mathrm{m}}{\mathrm{s}}}$, it decreases from $0.083\mathrm{rad}$ to $0.066\mathrm{rad}$. The fuzzy approach also improves IAE ($1.317{\displaystyle \frac{\mathrm{rad}}{\mathrm{s}}}$ to $1.185{\displaystyle \frac{\mathrm{rad}}{\mathrm{s}}}$) and ISE ($0.242{\displaystyle \frac{{\mathrm{rad}}^2}{\mathrm{s}}}$ to $0.153{\displaystyle \frac{{\mathrm{rad}}^2}{\mathrm{s}}}$) at $0.175{\displaystyle \frac{\mathrm{m}}{\mathrm{s}}}$, while maintaining similar maximum error ($0.299\mathrm{rad}$ to $0.261\mathrm{rad}$). Overall, the proposed hybrid scheme achieves better adaptability without retuning. These results support real-time deployment on resource-limited platforms.

Reliable ship detection in complex maritime optical imagery is a fundamental requirement for intelligent maritime monitoring and maritime automation systems. However, severe image degradation, large-scale variations, and background clutter often lead to feature ambiguity and unstable detection performance in real-world maritime environments. To address these challenges, this paper proposes a lightweight one-stage ship detection framework designed for robust real-time perception under degraded maritime sensing conditions. The proposed method incorporates an Adaptive Expert Selection Attention (AESA) mechanism to perform adaptive feature selection and background suppression under visually degraded conditions, together with a Geometry-Aware MultiScale Fusion (GAMF) module that enables orientation-aware aggregation of contextual information for elongated ship targets near complex sea–sky boundaries. In addition, a geometry-aware bounding box regression refinement is introduced to improve localization consistency in image space. Extensive experiments conducted on a unified real-world maritime benchmark demonstrate that the proposed framework consistently outperforms the baseline YOLO11n model by approximately 2–5 percentage points in terms of mAP@0.5 and mAP@0.5:0.95, while maintaining moderate computational complexity and real-time inference capability. These results indicate that the proposed method provides a practical and deployment-oriented perception solution for maritime automation applications, including onboard electro-optical sensing and coastal surveillance.