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X-WR-CALNAME:Computational Optimisation Group
X-ORIGINAL-URL:http://optimisation.doc.ic.ac.uk
X-WR-CALDESC:Events for Computational Optimisation Group
BEGIN:VEVENT
DTSTART;TZID=UTC+0:20171027T160000
DTEND;TZID=UTC+0:20171027T170000
DTSTAMP:20171018T200057
CREATED:20171011T095354Z
LAST-MODIFIED:20171011T095354Z
UID:1001-1509120000-1509123600@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Robust Model Predictive Control
DESCRIPTION:Title: Robust Model Predictive Control\nSpeaker: Dr. Saša V. Raković\nLocation: Room 218 Huxley Building\nTime: 4:00pm \nAbstract. \nModel 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. \nThe 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). \nAbout the speaker. \nDr. 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.) \nDr. 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. \nDr. 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. \nDr. 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. \nDr. 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. \n
URL:http://optimisation.doc.ic.ac.uk/event/seminar-robust-model-predictive-control/
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BEGIN:VEVENT
DTSTART;TZID=UTC+0:20171114T160000
DTEND;TZID=UTC+0:20171114T170000
DTSTAMP:20171018T200057
CREATED:20170921T091809Z
LAST-MODIFIED:20170921T091809Z
UID:989-1510675200-1510678800@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Multi-level Optimization by multi-parametric programming & its use for the solution of Mixed Integer Adjustable Robust Optimization Problems
DESCRIPTION:Title: Multi-level Optimization by multi-parametric programming & its use for the solution of Mixed Integer Adjustable Robust Optimization Problems\nSpeaker: Prof. Stratos Pistikopoulos\, FREng\nAffiliation: Artie McFerrin Department of Chemical Engineering\, Texas A&M University\nLocation: Room 217 Huxley Building\nTime: 4:00pm \nAbstract. Optimization problems involving multiple decision makers at different decision levels are referred to as multi-level programming problems. We are considering bi-level (two decision levels) and tri-level (three decision levels) programming problems. Multi-level programming problems are very challenging to solve even when considering just two linear decision levels. For classes of problems where the lower level problems also involve discrete variables\, this complexity is further increased\, typically requiring global optimization methods for its solution. Solution approaches for mixed integer bi-level problems with discrete variables in both levels mainly include reformulation approaches\, branch and bound techniques or genetic algorithms\, all of which result in approximate solutions. \nIn this work\, we present novel algorithms for the exact\, global and parametric solution of two classes of multi-level programming problems\, namely (i) bi-level mixed-integer linear or quadratic programming problems (B-MILP or B-MIQP) and (ii) tri-level mixed-integer linear or quadratic programming problems (T-MILP or T-MIQP) containing both integer and continuous variables at all optimization levels. Based on multi-parametric theory and our earlier results for bi-level programing problems [5\, 6]\, the main idea is to recast the lower levels of the multi-level programming problem as multi-parametric programming problems\, in which the optimization variables of all the upper level problems\, both continuous and integer\, are considered as parameters for the lower level problems. \nThis novel algorithm can be then used for the exact and global solution of adjustable robust optimization problems. Classical robust optimization (RO) is an approach for incorporating uncertainty in optimization problems\, and traditionally assumes that all decisions must be made before the realization of uncertainty (referred to as “here-and-now” decisions)\, a strategy which may be overly conservative. A more realistic approach is adjustable robust optimization (ARO) which involves recourse decisions (i.e. reactive actions after the realization of the uncertainty\, “wait-and-see”) as functions of the uncertainty\, typically posed in a two-stage stochastic setting. We propose a novel method for the derivation of generalized affine decision rules for linear/quadratic/nonlinear and mixed-integer ARO problems through multi-parametric programming. The problem is treated as a multi-level programming problem that can be then solved using the presented algorithm. A set of illustrative numerical examples are provided to demonstrate the potential of the proposed novel approach. \nAbout the speaker. Professor Pistikopoulos is TEES Distinguished Research Professor in the Artie McFerrin Department of Chemical Engineering at Texas A&M University. He was a Professor of Chemical Engineering at Imperial College London\, UK (1991-2015) and the Director of its Centre for Process Systems Engineering (2002-2009). At Texas A&M\, he is the Interim Co-Director & Deputy Director of the Texas A&M Energy Institute\, the Course Director of the Master of Science in Energy\, the Director of the Gulf Coast Regional Manufacturing Centre\, and the Texas A&M Principal Investigator of the RAPID Institute on process intensification\, co-leading the Modeling & Simulation Focus Area. \nHe holds a Ph.D. degree from Carnegie Mellon University and he worked with Shell Chemicals in Amsterdam before joining Imperial. He has authored or co-authored over 400 major research publications in the areas of modelling\, control and optimization of process\, energy and systems engineering applications\, 12 books and 2 patents. He is a Fellow of IChemE and AIChE\, and the Editor-in-Chief of Computers & Chemical Engineering. He is the current Chair of the Computing and Systems Technology (CAST) Division of AIChE and he serves as a trustee of the Computer Aids for Chemical Engineering (CACHE) Organization. In 2007\, Prof. Pistikopoulos was a co-recipient of the prestigious MacRobert Award from the Royal Academy of Engineering. In 2012\, he was the recipient of the Computing in Chemical Engineering Award of CAST/AIChE. He received the title of Doctor Honoris Causa in 2014 from the University Politehnica of Bucharest\, and from the University of Pannonia in 2015. In 2013\, he was elected Fellow of the Royal Academy of Engineering in the UK. \n
URL:http://optimisation.doc.ic.ac.uk/event/seminar-multi-level-optimization-by-multi-parametric-programming-its-use-for-the-solution-of-mixed-integer-adjustable-robust-optimization-problems/
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