At the Intelligent Control Systems (ICONS) Laboratory, we develop the learning and control foundation of intelligent cyber-physical systems, with applications in autonomous systems.

An intelligent cyber-physical system (iCPS) integrates deeply machine learning (ML) and artificial intelligence (AI) for decision and real-time control of a CPS to achieve high performance, adaptability, autonomy, and safety. By integrating learning, AI, control, and optimization, we create methods, algorithms, and engineering solutions for autonomous learning and real-time operation of iCPS. Our research and research approaches are multi-disciplinary, combining theories and techniques from

  • Computer science: machine learning, AI, scientific computing;
  • Electrical and computer engineering: control theory, edge computing, internet-of-things (IoT), sensors, embedded systems;
  • Applied mathematics: optimization theory, dynamical systems;
  • and various application domains.

We are particularly interested in the following application areas:

  • AI-enabled smart and sustainable energy systems such as smart buildings, connected communities, smart grids, smart cities.
  • Smart environment sensing and monitoring systems such as mobile robotic sensor networks (MRSN), wireless sensor networks, IoT sensing and monitoring in the built environments.
  • Data-driven control and optimization.

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