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About

Mohammad Bajelani is an emerging researcher whose work spans intelligent control systems, soft robotics, and safety-critical cyber-physical systems. His research is particularly focused on developing advanced control strategies for mechanically complex robotic platforms, including cable-driven parallel robots and tendon-driven continuum manipulators — systems notorious for their nonlinear dynamics and inherent uncertainties. Bajelani's most recognized contribution applies Brain Emotional Learning algorithms to cable-driven parallel robots, proposing a bio-inspired intelligent controller that circumvents the need for precise dynamic models — a paper that has garnered 6 citations since 2021. His broader body of work consistently addresses the fundamental tension between model-based and model-free control: his time-delay learning-based controller and data-driven safety filter research push toward practical, model-free solutions without sacrificing performance or safety guarantees. Notably, his 2024 work on modular safety filters extends this philosophy to networked systems vulnerable to cyber attacks, demonstrating meaningful interdisciplinary reach. More recently, Bajelani has embraced deep reinforcement learning for continuum robot shape-constrained control, reflecting his evolution toward data-driven intelligent frameworks. Though early in his career, his consistent output across robotics, learning-based control, and system safety marks him as a researcher with broad and growing influence in intelligent autonomous systems.