This core discipline deals with all aspects of system identification, inference, estimation, control, and learning for feedback systems.
Theoretical research includes quantification of fundamental capabilities and limitations of feedback systems, inference and control over networks, and development of practical methods and algorithms for decision making under uncertainty.
Historically, the intellectual roots of LIDS lie in the field of Systems and Control Theory. The original focus of this field was on modeling, analysis, and feedback controller design for systems described by linear or nonlinear differential or difference equations, with special emphasis on issues of robustness, a subject in which LIDS played a pioneering role. More recently, the focus has been shifting towards complex, often distributed and networked, systems. Typical concerns that are driving the field stem from the high-dimensionality of such systems, the simultaneous presence of discrete and continuous dynamics (hybrid systems), the interaction of physical systems with humans or software, and the quantification of appropriate notions of information for the purpose of decision-making.
- Autonomous vehicles: terrestrial and aerial
- Decentralized control under communication constraints
- Dynamics and control of networked dynamical systems
- Hybrid systems: control and verification methodologies
- Model reduction
- Reinforcement learning