The Control and Optimization group aims to perform quality and impactful research in the fundamental areas of Control and Optimization and also in the emerging subdisciplines that lie at the ever-expanding intersection of these areas. Along with theoretical research, the group also actively works on practical applications of the developed methods on hardware platforms. Various theoretical, applied and laboratory-based courses are being offered by the group, which have been carefully designed to build a foundation in control and optimization and lead the students to the state of the art.
Control and Optimization Group
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Research Focus
The group works in developing fundamental methods of systems and control theory, ranging from linear and nonlinear analysis and control, infinite-dimensional systems, identification and estimation, decision sciences, optimisation techniques, and in emerging areas of data driven and learning based methods in control. Our research focuses on a broad range of applications.
Network systems
Network identification and reconstruction from data, network design and optimization for control, Information transfer in networks.
Multi-robot systems
Multi-robot systems with focus on rendezvous amidst constraints and digital design for robotics and other applications are among areas of interest to the group. Trajectory tracking, event-triggered and beam-forming in multi-agent robotic networks.
Optimization
Dynamic system approach to solving convex optimization problems. Online distributed optimization.
Control systems and decision sciences
Control systems and decision sciences (game theory and optimization) with applications in the analysis of engineering and economic systems.