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About

Abolfazl Jalilvand is an emerging researcher whose work sits at the intersection of intelligent control systems and cyber-physical security. His primary research areas include adaptive fuzzy-neural inference systems, robotic manipulator control, and resilient control architectures for networked systems. Jalilvand’s most cited paper, “Design of an adaptive fuzzy-neural inference system-based control approach for robotic manipulators” (2023, 20 citations), introduces a novel hybrid control framework that combines fuzzy logic with neural network learning to enhance the precision and adaptability of robotic manipulators in uncertain environments. This contribution is particularly valuable for advanced manufacturing and autonomous systems. His more recent work, “Cyber-physical systems under hybrid cyber-attacks: Resilient event-triggered H∞ control approach” (2025, 2 citations), addresses the critical challenge of securing cyber-physical systems against sophisticated, multi-vector attacks. By proposing an event-triggered control strategy that maintains system stability and performance under hybrid threats, Jalilvand is helping to fortify the next generation of critical infrastructure. Though early in his career, his focused contributions to both intelligent control and cybersecurity signal a promising trajectory in these increasingly interconnected fields.