Control theory is the mathematics behind feedback systems that ensure your room is at the exact temperature you require or your car cruises at the exact speed you desire. Master control theory and robots will do whatever you tell them.
At Mason, we develop and utilize advanced control-theoretic tools to design, build, and deploy large teams of heterogeneous robots working together to complete various tasks. Our areas of expertise include:
Autonomous Underwater Vehicles
Autonomous underwater vehicles (AUVs) are receiving more and more scientific attention due to their independent locomotion and long-range operation. The applications of AUVs include but are not limited to oil and gas exploration, search and rescue, aquatic environmental monitoring, and coastal disaster response. Our group aims to develop advanced AUVs using novel sensing, actuation, and control strategies. In particular, our research focuses on the development of energy-efficient underwater gliders and bioinspired robotic fish. Principal investigator: Feitian Zhang.
Model-based and Data-driven Control of Dynamical Systems
Classical model-based control theory deals well with dynamical systems described by mathematically tractable models typically in the form of ordinary differential equations while data-driven control theory applies to complex high-dimensional systems where no suitable control-oriented models exist. Our research investigates the integration of model-based control and data-driven control aiming to achieve both rigorous analysis and practical implementation for complex dynamical systems. In particular, we focus on adaptive control, data-driven model reduction, and deep reinforcement learning with applications in soft robotics and bioinspired autonomous underwater/aerial vehicles. Principal investigator: Feitian Zhang.
Network Science and Distributed Control Theory
Rapid development of technology is quickly leading us to an increasingly networked and wireless world. With massive wireless networks on the horizon, the efficient coordination of such large networks becomes an important consideration that existing control theory is not equipped to handle alone. Given these new-age considerations, new fundamental tools and methods combining ideas from network science and decision/control theory are required to enable the reliable operation of large-scale networks of the future. General applications include networked Cyber-Physical Systems (CPS), the Internet of Things (IoT), multi-agent/swarm robotics, security and privacy of networks. Specific commercial applications include automated dispatch for ride-sharing vehicles or police cars, smart warehouses, smart grid, autonomous drone delivery systems, malware security in IoT networks, etc. Principal investigator: Cameron Nowzari.