Seminar Details
2018-01-16 (14:00) : Robust model predictive control
At Euler building (room A.002)
Organized by Mathematical Engineering
Speaker :
Sasa V. Rakovic
Abstract :
Model predictive control (MPC) is an advanced control technique that employs an open-loop online optimization in order to take
account of system dynamics, constraints and control objectives and to obtain the best current control action. Robust MPC (RMPC)
is an improved MPC form that is robust against the bounded uncertainty. RMPC employs a generalized prediction framework that
allows for a meaningful optimization of, and over, the set of possible system behaviours effected by the uncertainty. A real intricacy
in RMPC arises due to the facts that the exact RMPC provides strong structural properties but it is computationally unwieldy,
while the conventional MPC is not necessarily robust even though it is computationally convenient.
The seminar focuses on novel RMPC methods, developed through my research investigations and collaborations, that address
effectively the fundamental challenge of reaching a meaningful compromise between the quality of guaranteed structural properties
and the associated computational complexity. In particular, the talk discusses flexible and efficiently optimizable parameterizations
as well as tube MPC, which lead to synthesis methods that are theoretically sound (i.e., they guarantee a-priori strong structural
properties) and computationally efficient (i.e., they have a manageable computational complexity that is close enough to that of
the conventional MPC synthesis).