Tomoaki Hashimoto, Aug 2014

• Visitor: Prof. Tomoaki Hashimoto (Osaka University, Assistant Professor)
• Date: August 1st (Fri)

Schedule

• 9 am - Yutaka (218 ANB; ex 3552)
• 10 am -
• 11 am -
• 11:50 am - seminar set up
• 12-1:15 pm - seminar (213 ANB)
• 1:15 pm - Richard (until 2 pm)
• 2 pm -
• 3 pm - Yilin Mo (310 ANN; ex 3503)
• 4 pm - Ye Yuan (334 ANNENBERG)
• 5 pm - Done

Seminar

Abstract: Model predictive control (MPC), also known as receding horizon control, is a type of optimal feedback control where control performance over a finite future is optimized with a performance index that has a moving initial time and a terminal time. This talk provides two topics about MPC. One is a design method of MPC for thermal fluid systems governed by nonlinear partial differential equations [1]. The other one is probabilistic constrained MPC for linear discrete-time stochastic systems. Both topics are related to the recent development of MPC. In particular, we would like to review the background and key idea of these studies [1, 2] rather than the technical details.

References:

[1] Tomoaki HASHIMOTO, Yusuke YOSHIOKA and Toshiyuki OHTSUKA: Receding Horizon Control with Numerical Solution for Nonlinear Parabolic Partial Differential Equations, IEEE Transactions on Automatic Control, Vol. 58, pp.725-730, 2013.

[2] Tomoaki HASHIMOTO: Probabilistic Constrained Model Predictive Control for Linear Discrete-time Systems with Additive Stochastic Disturbances, Proceedings of the 52nd IEEE Conference on Decision and Control, pp.6434-6439, 2013.

Bio

Dr. Hashimoto received the B.Eng., M.Eng., and D.Eng. degrees from the Tokyo Metropolitan Institute of Technology, Hino, Japan, in 2003, 2004, and 2007, respectively, all in aerospace engineering. He was a Research Assistant at the RIKEN Brain Science Institute, Wako, Japan, from 2007 to 2008, and an Assistant Professor at the Shinshu University, Nagano, Japan, from 2008 to 2009. Since 2009, he has been an Assistant Professor at Osaka University, Toyonaka, Japan. His recent research interests are in the area of model predictive control and its application.