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All Journal International Journal of Electrical and Computer Engineering Scientific Journal of Computer Science
Donatus, Rexcharles Enyinna
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Computer Science & IT Electrical & Electronics Engineering

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Residual reinforcement learning for disturbance-resilient control under modeling uncertainties Adetifa, Abolanle; Donatus, Rexcharles Enyinna; Udekwe, Daniel
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1175-1187

Abstract

Modern control systems must operate reliably in the presence of modeling uncertainties and external disturbances, conditions under which conventional fixed-gain controllers often exhibit performance degradation. This paper proposes a residual reinforcement learning framework for disturbance-resilient pitch-rate control of an aircraft longitudinal model. A classical proportional-integral-derivative (PID) controller is employed as a stabilizing baseline, while a deep deterministic policy gradient (DDPG) agent learns a bounded residual control signal to compensate for unmodeled dynamics and external perturbations. To promote favorable transient behavior, the learning process incorporates transient-aware and reference-model-based reward shaping, while actuator constraints are enforced within the environment dynamics. Simulation results demonstrate that the proposed residual controller achieves a superior balance between response speed, overshoot, and tracking accuracy compared with both the standalone PID controller and a pure DDPG-based controller. In particular, the residual architecture significantly reduces overshoot and tracking error while preserving fast transient response and providing robust disturbance rejection under large pitching moment disturbances. These results indicate that residual reinforcement learning offers a practical and effective approach for enhancing robustness and performance in safety-critical flight control applications.
A Structured Survey of Attention Mechanisms in Audio-Visual Fusion: Architectures, Challenges, and Evaluation Frameworks Donatus, Rexcharles Enyinna; Awodele, Oludele; Oguike, Osondu Everestus; Sambo-Magaji, Amina
Scientific Journal of Computer Science Vol. 2 No. 2 (2026): December Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v2i2.2026.438

Abstract

Audio-visual fusion plays an important role in multimodal artificial intelligence, particularly in applications such as speech processing, emotion recognition, and video understanding, where information from sound and vision improves performance and contextual understanding. Recent developments are driven by attention mechanisms and transformer-based models, which enable more flexible and context-aware interaction within and across modalities compared to conventional fusion approaches. Despite these advances, challenges remain, including sensitivity to noisy or missing modalities, modality imbalance, limited interpretability, and high computational cost. This paper presents a structured survey of attention mechanisms in audio-visual fusion, with emphasis on architectural design and evaluation practices across multiple application domains. A structured survey methodology inspired by PRISMA principles is used to identify and select relevant studies, followed by comparative analysis of model architectures, training strategies, and evaluation methods. The findings show that transformer-based and attention-centered architectures have become increasingly prominent and achieve strong performance across tasks. However, these approaches involve trade-offs between robustness, interpretability, and computational efficiency, and remain sensitive to noise and modality imbalance. Evaluation practices are also inconsistent, with limited use of standardized and robustness-focused metrics. The survey provides an attention-centered taxonomy of audio-visual fusion methods and synthesizes current approaches and evaluation strategies. It identifies key challenges and outlines directions for improving robustness, interpretability, and efficiency in practical deployment.
Co-Authors Adetifa, Abolanle Awodele, Oludele Oguike, Osondu Everestus Sambo-Magaji, Amina Udekwe, Daniel