This study addresses the trajectory tracking control problem for an underactuated hovercraft subject to additive bias and multiplicative loss-of-effectiveness thruster faults under environmental disturbances. In these systems, actuator degradation structurally breaks the differential flatness mapping, driving nominal controllers to generate control actions that induce severe actuator saturation and cause instability. To resolve this challenge, a hierarchical physics-informed neural adaptive control (PINAC) framework is proposed. First, a gated-recurrent-unit physics-informed neural observer (PINO) is designed to isolate thruster faults from exogenous hydrodynamic disturbances. Second, a constrained Safe-TD3 reinforcement learning agent functions as a supervisor, computing an online dilation factor to slow down the mission timeline, thereby reconfiguring the reference trajectory to accommodate degraded actuator boundaries. Third, a low-level non-singular terminal sliding mode (NTSM) controller is implemented as a tracking-guarantee layer. Unlike classical asymptotic schemes where convergence is only achieved as time approaches infinity, or finite-time controllers where the settling time depends on the initial state, the proposed PINAC framework guarantees practical fixed-time stability, ensuring that the settling-time bound is independent of initial conditions. Simulation results demonstrate that the designed controller prevents actuator saturation, provides smooth trajectory adjustment, and reduces tracking errors under severe composite faults.
Shafqat Ali, Aamir Mehmood, Faiza Iftikhar et al.· Journal of Marine Science an...· 0 citations
Computed Tomography (CT) scans are widely used to diagnose lung infections; however, manual interpretation is labor-intensive. Artificial intelligence has accelerated the development of computer-aided diagnostic (CAD) systems, allowing faster and more accurate diagnosis. Nevertheless, many existing CAD systems lack robust cross-dataset generalization and interpretability, limiting their reliability and resulting in suboptimal diagnostic performance. To address these limitations, we propose a semantic attention-driven retrieval framework based on a lightweight Meta-Domain Adaptive Segmentation Network (MDA-SN) with an adaptive data normalization strategy to enhance infection detection in cross-dataset analysis. This framework quantifies infection ratios and retrieves relevant CT slices from the database, closely matching the input test sample to further support medical experts in making more accurate diagnostic decisions. The MDA-SN design leverages multi-scale dilated grouped convolution with residual attention to ensure real-time performance while maintaining accuracy. Our framework achieved an average cross-dataset performance of 75.93% Dice index and 67.42% Intersection over Union, surpassing state-of-the-art methods by 3.32% and 3.28%, respectively. Additionally, it achieves real-time execution, processing an average of 29 slices per second, due to its significantly reduced number of training parameters, approximately 70% fewer than its closest competitor. The implementation and materials are available at our GitHub repository:
https://github.com/Owais-CodeHub/MDA-SN
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Muhammad Owais, Taimur Hassan, Naqash Afzal et al.· Scientific Reports· 1 citation
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