Estimation and control is a general research thrust of our lab that underpins many applications mentioned above. On one hand, we are interested using advanced estimation and control strategies such as differential flatness based control, extended Kalman filter, robust control, decentralized control to solve problems in robotics, transportation, smart grid, and machine learning. On the other hand, we are also interested in the general theory of nonlinear systems, especially in hybrid systems.
Hybrid systems are a general class of dynamical systems for which continuous dynamics are coupled with discrete logic rules. It switches between many operating modes where each mode is governed by its own characteristic dynamical law. Mode transitions are triggered by variables crossing specific thresholds (state events), by the elapse of certain time periods (time events), or by external inputs (input events). Hybrid systems are regarded as the most appropriate framework in modeling modern engineering systems. Over the years, we have made several theoretical contributions in optimal control, estimation, and stabilization of hybrid systems.