Author

Khalil F. Yassin

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Open access Aug 2026

Dynamic Modeling and Real-Time Control of Continuum Soft Robots

Continuum soft robots have attracted significant attention due to their high flexibility, compliance, and ability to perform complex tasks in unstructured environments. However, their inherent nonlinear, distributed-parameter dynamics pose major challenges for accurate modeling and real-time control. Existing approaches typically face a trade-off between computational efficiency and model fidelity, where simplified models lack accuracy, while high-fidelity formulations are computationally prohibitive for real-time implementation. This study addresses these limitations by proposing a hybrid framework for dynamic modeling and real-time control of continuum soft robots.The proposed approach integrates a reduced-order dynamic model derived from continuum mechanics with a data-driven compensation module to enhance modeling accuracy while maintaining computational efficiency. The reduced-order formulation captures the dominant deformation behavior of the robot, while a learning-based component compensates for unmodeled dynamics and uncertainties. Based on this hybrid model, a real-time control strategy is developed to achieve accurate trajectory tracking under nonlinear and uncertain operating conditions.Comprehensive evaluations are conducted through simulation-based studies and comparative analysis with state-of-the-art modeling and control methods. The results demonstrate that the proposed framework achieves improved accuracy, reduced tracking error, and significantly enhanced computational efficiency compared to conventional approaches such as constant curvature, Cosserat rod, and FEM-based models. This improvement is achieved because the reduced-order dynamic model preserves the dominant deformation characteristics while lowering computational complexity, and the data-driven compensation module corrects unmodeled dynamics and nonlinear effects that are not captured by conventional formulations. Consequently, the framework enables faster computation, more accurate state prediction, and more reliable real-time control performance. Furthermore, the proposed method maintains real-time feasibility while ensuring robustness against disturbances and noise.

R. Hasan, Munif Ahmed Abdullah, Mehdi Qahraman Fakhruldin et al. · EDRAAK · 0 citations

Open access Sep 2026

Comprehensive Performance Evaluation of Deep Learning Algorithms for Multi-Variable Climate Prediction: RNN, LSTM, and GRU Analysis

The selection of appropriate deep learning architectures for climate prediction remains a critical challenge in atmospheric sciences, with different algorithms showing varying performance across climate variables and geographical regions. This study presents a comprehensive comparative analysis of three prominent deep learning architectures Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) for multi-variable climate prediction in the Middle East region. Using 42 years (1981-2022) of high-resolution MERRA-2 reanalysis data across nine Middle Eastern capitals, we evaluated the performance of these algorithms in predicting six key climate variables: maximum and minimum temperature, relative humidity, wind speed, solar radiation, and precipitation. Our analysis reveals significant performance differences across algorithms, with GRU demonstrating superior computational efficiency (14% faster training than LSTM) while maintaining competitive accuracy. LSTM excelled in capturing long-term dependencies for temperature variables (R² > 0.95), while RNN showed adequate performance for simpler patterns but struggled with complex temporal relationships. The study provides detailed performance metrics, computational requirements, and practical guidelines for algorithm selection based on specific climate prediction tasks. Key findings indicate that algorithm choice should be tailored to the prediction target, with temperature variables favoring LSTM, precipitation benefiting from GRU's efficiency, and humidity showing comparable performance across all architectures. These results provide essential guidance for operational weather forecasting systems and climate modeling applications, contributing to the optimization of deep learning approaches in atmospheric sciences.

M. M. Akawee, R. Hasan, Munif Ahmed Abdullah et al. · EDRAAK · 0 citations