Optimal Linear Quadratic Control with Nonconvex Quadratic Constraints

Dr. Ather Gattami is currently an assistant professor at the Royal Institute of Technology, ACCESS Linnaeus Centre, Electrical Engineering, Stockholm, Sweden. He received the M.S. in Engineering Physics and Ph.D. degree in Engineering Sciences in June 2008, both from Lund University, Sweden. He pursued his Master's Thesis in 2003 at California Institute of Technology, Pasadena, CA, USA. During 2008, he did his post doc studies at the Laboratory for Information and Decision Systems (LIDS), MIT, Boston, USA. Dr. Gattami is supervising a number of doctorate and graduate students at the Royal Institute of Technology, Sweden. His main interests are Decision Theory, Game Theory, Optimization, and Information Theory, with applications in the industry.

We consider the problem of stochastic finite- and infinite-horizon linear quadratic control under nonconvex quadratic constraints (in the state and control signal). The calculations of the optimal control law can be done off-line as in the classical linear quadratic Gaussian control theory using dynamic programming, which turns out to be a special case of the new theory developed.