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).
About the speaker.
Dr. Saša V. Raković received the Ph.D. degree in Control Theory from Imperial College London. His Ph.D. thesis, entitled “Robust Control of Constrained Discrete Time Systems: Characterization and Implementation”, was awarded the Eryl Cadwaladr Davies Prize as the best Ph.D. thesis in the Electrical and Electronic Engineering Department of Imperial College London in 2005. (This award is presented annually to the Ph.D. student who produces the best thesis during the academic year.)
Dr. Saša V. Raković has been affiliated with a number of the leading international universities, including Imperial College London, ETH Zürich, Oxford University, UMD at College Park, UT at Austin and Texas A&M University at College Station.
Dr. Saša V. Raković’s research spans the broad areas of autonomy, controls, dynamics, systems, applied mathematics, optimization and set–valued analysis. His current research activity is focused on problems encountered in, or closely related to, the fields of smart autonomous and cyber-physical systems, and that belong to the intersection of controls, dynamics, systems and optimization. Dr. Saša V. Raković has authored 95 publications, most of which are highly cited (i.e., more than 3600 citations according to Google Scholar) and which are published in leading international journals and proceedings of key conferences in the related fields.
Dr. Saša V. Raković is best known for his research in model predictive control that has made significant contributions to theory, computation and implementation of conventional, robust and stochastic model predictive control. Dr. Saša V. Raković is one of the global leaders in robust model predictive control, and one of the key pioneers of the tube model predictive control framework. Tube model predictive control has been recognized as a milestone contribution to, and a major paradigm shift in, model predictive control under uncertainty.
Dr. Saša V. Raković’s most important work in analysis of dynamics and control synthesis via optimization and set–valued methods has dealt with previously long–standing problems. Inter alia, Zvi Artstein and Saša V. Raković have resolved important problems concerned with minimality of invariant sets and set invariance under output feedback. Dr. Saša V. Raković has also significantly expanded classical results on the linear quadratic Lyapunov equations by developing theory and computations for Minkowski–Lyapunov equations, namely Lyapunov equations within the class of generic vector semi–norms.