Research

A primary goal of automatic control is to improve the performance of a dynamical system while ensuring stability and robustness in the presence of disturbances, measurement noise, and modeling errors. This is accomplished through a feedback loop between the system and its controller. I focus especially on hybrid dynamical systems and decentralized networked 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, I focus on stochastic control and machine learning over distributed sensor networks.

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.