CDS Seminar

Thursday February 18, 2016 11:00 AM

Adaptive Horizon Model Predictive Control

Speaker: Arthur Krener, Department of Applied Mathematics, Navel Postgraduate School
Location: Gates-Thomas 115

Speaker:  Arthur J. Krener
Distinguished Visiting Professor
Department of Applied Mathematics
Naval Postgraduate School
Date & Time:  Thursday, February 18, 2016 @ 11:00am
Location:  115 Gates-Thomas
Host: Richard Murray

Abstract
Abstract: Adaptive Horizon Model Predictive Control  (AHMPC) is a scheme for varying  the horizon length of Model Predictive Control (MPC) as
needed. Its goal is to achieve stabilization with horizons as small as possible so that MPC can be used on faster or more complicated dynamic
processes.  Beside the standard requirements of MPC including a terminal cost that is a control Lyapunov function, AHMPC requires a terminal
feedback that turns the control Lyapunov function into a standard Lyapunov function in some domain around the operating point.   But this
domain need not be known explicitly.  MPC does not compute off-line the optimal cost and  the optimal feedback over a large domain instead it
computes these quantities on-line when and where they are needed.  AHMPC does not compute off-line the  domain on which the terminal cost is a
control Lyapunov function instead it computes on-line when a state is in this domain.

Contact: Nikki Fauntleroy at 626-395-4140 nikkif@caltech.edu