Research

A primary goal of automatic control is to improve a dynamical system's performance while ensuring its stability and robustness against disturbances, measurement noise, and modeling errors. This is accomplished by establishing a feedback loop between the system and the controller. Special emphasis is placed on hybrid dynamical systems and distributed network systems.

Statistical signal processing treats signals as stochastic processes, and exploits their statistical properties to perform tasks such as target detection, parameter estimation, stochastic control, and machine learning. In particular, stochastic control over distributed sensor networks is an emerging research area that is highly relevant in our increasingly distributed world.

S. Ghosh and J.-W. Lee, "Optimal distributed finite-time consensus on unknown undirected graphs," IEEE Transactions on Control of Network Systems, vol. 2, no. 4, pp. 323–334, 2015.

S. Mirzazad-Barijough and J.-W. Lee, "Stability and transient performance of discrete-time piecewise affine systems," IEEE Transactions on Automatic Control, vol. 57, no. 4, pp. 936–949, 2012.

J.-W. Lee and P. P. Khargonekar, "Distribution-free consistency of empirical risk minimization and support vector regression," Mathematics of Control, Signals, and Systems, vol. 21, no. 2, pp. 111–125, 2009.

J.-W. Lee and P. P. Khargonekar, "Detectability and stabilizability of discrete-time switched linear systems," IEEE Transactions on Automatic Control, vol. 54, no. 3, pp. 424–437, 2009.

J.-W. Lee and G. E. Dullerud, "Uniform stabilization of discrete-time switched and Markovian jump linear systems," Automatica, vol. 42, no. 2, pp. 205–218, 2006.