Resilient Control Systems
Our research addresses the fundamental security challenges in cyber-physical critical infrastructure systems, with particular emphasis on the vulnerabilities introduced by increasing integration of Internet of Things (IoT) technologies. While these advanced networking capabilities enhance system efficiency and reliability, they simultaneously expand the attack surface for sophisticated cyber threats. Our lab develops theoretical frameworks and practical implementations for resilient control systems that maintain operational integrity under adversarial conditions. This work advances beyond conventional security paradigms to establish new methodologies for adaptive, real-time response mechanisms that are both computationally efficient and practically deployable in critical infrastructure settings.
Research Focus Areas
- Attack Detection & Prevention: Developing sophisticated algorithms for early threat detection and mitigation
- Adaptive Control Systems: Creating resilient control mechanisms that maintain functionality during attacks
- Real-time Response: Implementing fast-acting, efficient response systems to neutralize threats
- System Hardening: Engineering robust architectures resistant to cyber and physical threats
Autonomous Systems Control
Our lab investigates fundamental challenges in autonomous systems through a multi-scale experimental approach, encompassing platforms from scaled mobile robots to full-size autonomous vehicles. The research program addresses core theoretical and implementation challenges in autonomous navigation, focusing on the integration of vision-based perception, learning-enabled object detection, and robust motion control architectures. By developing novel algorithms for perception, planning, and control, our work advances the theoretical foundations and practical deployment of reliable autonomous systems across diverse operational domains.
Research Focus Areas
- Path Planning & Navigation: Developing sophisticated algorithms for optimal route calculation and dynamic obstacle avoidance
- Localization & Traffic Control: Creating precise positioning systems and smart traffic management solutions
- Intersection Safety Systems: Implementing efficient, low-cost sensor fusion for real-time detection, tracking, and trajectory prediction of vehicles and vulnerable road users, with modular designs that integrate into existing infrastructure
- Resilient Motion Control: Engineering robust control systems that maintain stability and performance under various conditions
- Advanced Teleoperation: Implementing remote operation capabilities with enhanced operator feedback
- Large-scale Shared Control: Developing frameworks for multiple vehicle coordination and fleet management
Power & Energy Systems Research
Our research explores advanced control strategies, optimization and data-driven frameworks for energy delivery systems—generation, transmission and distribution—to ensure robust, efficient, and sustainable energy solutions for tomorrow’s grid. We develop and implement cutting-edge control strategies and learning methods for distributed energy resources. Our algorithms are validated using real-time simulation, hardware-in-the-loop (HIL), controller hardware-in-the-loop (CHIL) and power hardware-in-the-loop (PHIL) experiments.
