Dr. Ayyappadas Rajagopal 

I am a Ph.D. graduate in Electrical Engineering from IIT Palakkad, with a primary research focus on artificial intelligence, deep learning, and reinforcement learning (RL) for autonomous and networked control systems. My work centers on developing intelligent, adaptive, and resilient decision-making algorithms for robots and cyber-physical systems operating under uncertain and communication-constrained environments. I integrate deep RL, learning-based estimation, and classical control theory to design robust controllers and state estimators that can operate reliably in real-world scenarios. I have developed a comprehensive hardware-in-the-loop (HIL) testbed and deployed learning-based and control algorithms on UAVs and ground robots, bridging theoretical models with embedded, real-time autonomous platforms. I have authored multiple journal and conference papers, including publications in IEEE Transactions on Circuits and Systems II, the International Journal of Systems Science (Taylor & Francis), ICC, and ICARCV and hold a patent in intelligent control. My broader goal is to advance AI-driven autonomy and create scalable, dependable robotic and networked control solutions that address complex real-world challenges.

Networked Control Systems (NCS) involve the integration of communication networks with control systems, allowing the exchange of data between sensors, actuators, and controllers over shared or distributed networks. NCS enables efficient remote control and monitoring of various systems, from industrial automation to smart cities, by leveraging network connectivity to manage dynamic and uncertain environments.

My primary research area focuses on advancing NCS, specifically on enhancing state estimation and control mechanisms in uncertain network environments. My work integrates theoretical frameworks with practical hardware-in-the-loop (HIL) implementations, aiming to improve the performance, security, and adaptability of NCS in real-world applications.