Introduction Set invariance theory i-steps sets Robust invariant sets. Set Invariance. D. Limon A. Ferramosca E.F. Camacho

Size: px
Start display at page:

Download "Introduction Set invariance theory i-steps sets Robust invariant sets. Set Invariance. D. Limon A. Ferramosca E.F. Camacho"

Transcription

1 Set Invariance D. Limon A. Ferramosca E.F. Camacho Department of Automatic Control & Systems Engineering University of Seville HYCON-EECI Graduate School on Control Limon, Ferramosca, Camacho Set Invariance Outline Some definitions One step set The reach set 4 Limon, Ferramosca, Camacho Set Invariance

2 Some definitions Set invariance Set invariance is a fundamental concept in design of controller for constrained systems. The reason: constraint satisfaction can be guaranteed for all time (and for all disturbances) if and only if the initial state is contained inside a (robust) control invariant set. The evolution of a constrained system is admissible if there exists an invariant set X, where X is the set where constraints on the state are fullfilled. Hence, if x X, then x k X, for all k. Limon, Ferramosca, Camacho Set Invariance Some definitions Set invariance Set invariance is strictly connected with stability. Lyapunov theory states that, if there exists a Lyapunov function V (x) such that: ΔV (x) then, for all x X, any set defined as: ={x R n : V (x) α} X is an invariant set for the system, and hence for any initial state x, the system fulfills the constraints and remains inside. Limon, Ferramosca, Camacho Set Invariance 4

3 Some definitions Positive invariant set Consider an autonomous system: x k+ = f (x k ), x k X Definition (Positive invariant set) R n is a positive invariant set if x X, x k, for all k. If the system reaches a positive invariant set, its future evolution remains inside this set. The maximum invariant set, max X, is the smallest positive invariant set that contains all the positive invariant sets contained in X. Limon, Ferramosca, Camacho Set Invariance 5 Some definitions Positive control invariant set Consider the system: x k R n and u k R m. x k+ = f (x k, uk), x k X, u k U Definition (Positive control invariant set) R n is a positive control invariant set if x X, there exists a control law u k = h(x k ) such that x k, for all k, and u k = h(x k ) U. Limon, Ferramosca, Camacho Set Invariance 6

4 Onestepset The reach set Onestepset Consider the system: x k+ = f (x k, uk), x k X, u k U x k R n and u k R m. Let f (, ) = be en equilibrium point. Definition (One step set) The set Q() is the set of states in R n for which an admissible control inputs exists which will guarantee that the system will be driven to in one step: Q() = {x k R n u k U : f (x k, u k ) } Limon, Ferramosca, Camacho Set Invariance 7 Onestepset The reach set Onestepset The previous definition is the same for a system controlled by a control law u = h(x): Q h () = {x k R n : f (x k, h(x k )) } Property: Monotonicity: consider sets, then: Q( ) Q( ) Limon, Ferramosca, Camacho Set Invariance 8

5 Onestepset The reach set Geometric invariance condition The one step set definition allow us to define a condition for guaranteing the invariance of a set. Geometric invariance condition: set is a control invariant set if and only if Q() Q().5.5 x.5 Q().5 x is not invariant is invariant Limon, Ferramosca, Camacho Set Invariance 9 The reach set Onestepset The reach set Definition (The reach set) The set R() is the set of states in R n to which the system will evolve at the next time step given any x k and admissible control input: R() = {z R n : x k, u k Us.t.z = f (x k, u k )} For closed-loop systems, R h () is the set of states in R n to which the system will evolve at the next time step given any x k : R h () = {z R n : x k, s.t.z = f (x k, h(x k ))} Limon, Ferramosca, Camacho Set Invariance

6 Definition The K i (X, ) is the set of states for which exists an admissible control sequence such that the system reaches the set X in exactly i steps, with an admissible evolution. K i (X, ) = {x X : k =,..., i, u k Us.t.x k Xandx i } This set represents the set of all states that can reach a given set in i steps, with an admissible evolution and an admissible control sequence. Limon, Ferramosca, Camacho Set Invariance Properties K i+ (X, ) = Q(K i (X, )) X, withk (X, ) =. K i (X, ) K i+ (X, ) iff is invariant. The set K (X, ) is finitely determined if and only if i N such that K (X, ) = K i (X, ). The smallest element i N such that K (X, ) = K i (X, ) is called the determinedness index. If j N such that K i+ (X, ) = K i (X, ), i j, then K (X, ) is finitely determined. For closed-loop systems: K h i (X, ) = {x X h : x k X h k =,..., i, andx i } where X h = {x X : h(x) U}. Limon, Ferramosca, Camacho Set Invariance

7 Definition The set C (X ) is the maximal control invariant set contained in X for system x k+ = f (x k, u k ) if and only if C (X) is a control invariant set and contains all the invariant sets Φ contained in X. Φ C (X) X This set is derived from the definition of the i-steps admissible set, C i (X ), that is the set of states for which exists an admissible control sequence such that the evolution of the system remains in X during the next i steps. C i (X )={x X : k =,..., i, u k Us.t.x k+ X} Limon, Ferramosca, Camacho Set Invariance Properties C i (X )=K i (X, X ). C i+ (X ) C i (X ). If x XC i (X ), there not exists an admissible control law which will ensure that the evolution of the system is admissible for i steps. C (X ) is the set of all states for which there exists an admissible control law which ensures the fulfillment of the constraints for all time. C (X ) is finitely determined if and only if there exists an element i N, such that C i+ (X) =C i (X), i i. Hence, C i (X )=C (X). K i (X, ) C (X ), i and X. Limon, Ferramosca, Camacho Set Invariance 4

8 Definition The set S (X, ) is the maximal stabilisable invariant set contained in X for system x k+ = f (x k, u k ) if and only if S (X, ) is the union of all i-step stabilisable sets contained in X. This set is derived from the definition of the i-steps stabilisable set, S i (X, ), that is the set of states for which exists an admissible control sequence that drive the system to the invariant set in i steps with an admissible evolution. S i (X, ) = {x X : k =,..., i, u k Us.t.x k Xandx i } The only difference between S i (X, ) and K i (X, ) is the invariance condition for. Limon, Ferramosca, Camacho Set Invariance 5 Properties S i+ (X, ) = Q(S i (X, )) X, withs (X, ) =. S i (X, ) S i+ (X, ). Any S i (X, ) is a control invariant set. Consider and invariant sets, such that. Then S i (X, ) S i (X, ). S i (X, S j (X, )) = S i+j (X, ). S (X, ) is finitely determined if and only if there exists an element i N such that S i+ (X, ) = S i (X, ), for any i i. Furthermore, S (X, ) = S i (X, ), for any i i. For closed-loop systems: S h i (X, ) = {x X h : x k X h k =,..., i, andx i } where X h = {x X : h(x) U}. Limon, Ferramosca, Camacho Set Invariance 6

9 i-steps controllable and stabilizable sets not Invariant Invariant S x x K x x Controllable set K (X, ) = Q() X Stabilisable set S (X, ) = Q() X Limon, Ferramosca, Camacho Set Invariance 7 i-steps controllable and stabilizable sets x x S S K K x x Controllable set K (X, ) = Q(K ) X Stabilisable set S (X, ) = Q(S ) X Limon, Ferramosca, Camacho Set Invariance 8

10 K K i-steps controllable and stabilizable sets S S S x x K x x Controllable set K (X, ) = Q(K ) X Stabilisable set S (X, ) = Q(S ) X Limon, Ferramosca, Camacho Set Invariance 9 i-steps controllable and stabilizable sets S 4 S S S x x K K K K x x Controllable set K i+ (X, ) = Q(K i ) X K i (X, ) K i+ X Stabilisable set S i+ (X, ) = Q(S i ) X S i (X, ) S i+ X Limon, Ferramosca, Camacho Set Invariance

11 i-steps controllable and stabilizable sets S 9 = S = S x x K K K K x x Controllable set K i+ (X, ) = Q(K i ) X K i (X, ) K i+ X Stabilisable set S i+ (X, ) = Q(S i ) X S i (X, ) S i+ X S finitely determined iff S i (X, ) = S i+ X Limon, Ferramosca, Camacho Set Invariance Comments The difference between the controllable set and the stabilisable set is the fact that the set is an invariant set. This difference is very important in relation to the concept of stability: if x S i (X, ), then there exists a control sequence such that the system is driven to, and there exist a control law such that the system remains inside. If is not invariant, then the system might evolve outside, hence loosing stability. S (X, ) C (X). Hence, x C (X) \ S i (X, ), there exists an admissible control law such that the system fulfills the constraints, but there not exists a control law that drives the system to. Set {} is an invariant set. Then, the set of states that asymptotically stabilize the system at the origin in i steps is given by S i (X, {}). Limon, Ferramosca, Camacho Set Invariance

12 Robust positive control invariant set Consider the system: x k+ = f (x k, uk, w k ), x k X, u k U, w k W x k R n, u k R m, w k R q. Definition (Robust positive control invariant set) R n is a robust positive control invariant set if x X, there exists a control law u k = h(x k ) such that x k, for all k and w k W, and u k = h(x k ) U. Limon, Ferramosca, Camacho Set Invariance Robust one step set Definition (Robust one step set) The set Q() is the set of states in R n for which an admissible control inputs exists which will guarantee that the system will be driven to in one step, for any w W: Q() = {x k R n u k U : f (x k, u k, w k ) w k W} For closed-loop systems: Q h () = {x k R n : f (x k, h(x k ), w k ) w k W} Limon, Ferramosca, Camacho Set Invariance 4

13 Properties Monotonicity: consider sets, then: Q( ) Q( ) The one step set definition allow us to define a condition for guaranteing the invariance of a set. Geometric robust invariance condition: set is a control invariant set if and only if Q() Limon, Ferramosca, Camacho Set Invariance 5 Robust reach set Definition (Robust reach set) The set R() is the set of states in R n to which the system will evolve at the next time step given any x k, anyw k Wand admissible control input: R() = {z R n : x k, u k U, w k Ws.t.z = f (x k, u k, w k )} For closed-loop systems, R h () is the set of states in R n to which the system will evolve at the next time step given any x k and any w k W: R h () = {z R n : x k, w k Ws.t.z = f (x k, h(x k ), w k )} Limon, Ferramosca, Camacho Set Invariance 6

14 i-steps robust controllable set Definition The i-steps robust controllable set K i (X, ) is the set of states for which exists an admissible control sequence such that the system reaches the set X in exactly i steps, with an admissible evolution, for any w k W. K i (X, ) = {x X : k =,..., i, u k Us.t.x k Xandx i w k W} This set represents the set of all states that can reach a given set in i steps, with an admissible evolution and an admissible control sequence. Limon, Ferramosca, Camacho Set Invariance 7 Maximal robust control invariant set Definition The set C (X ) is the maximal control invariant set contained in X for system x k+ = f (x k, u k, w k ) if and only if C (X) is a robust control invariant set and contains all the robust invariant sets Φ contained in X. Φ C (X) X This set is derived from the definition of the i-steps robust admissible set, C i (X ), that is the set of states for which exists an admissible control sequence such that the evolution of the system remains in X during the next i steps, for any w k W. C i (X) ={x X : u k Us.t.x k+ X, w k W, k =,..., i } Limon, Ferramosca, Camacho Set Invariance 8

15 Maximal robust stabilisable set Definition The set S (X, ) is the maximal robust stabilisable invariant set contained in X for system x k+ = f (x k, u k, w k ) if and only if S (X, ) is the union of all i-step stabilisable sets contained in X, for any w k W. This set is derived from the definition of the i-steps robust stabilisable set, S i (X, ), that is the set of states for which exists an admissible control sequence that drive the system to the invariant set in i steps with an admissible evolution. S i (X, ) = {x X : k =,..., i, u k Us.t.x k Xandx i w k W} All the properties of the nominal invariant sets are applicable to the robust case. Limon, Ferramosca, Camacho Set Invariance 9 Bibliography F. Blanchini. Set invariance in control. Automatica. 5: , 999. E. Kerrigan. Robust Constrained Satisfaction: Invariant Sets and Predictive Control. PhD Dissertation. Limon, Ferramosca, Camacho Set Invariance

15 Limit sets. Lyapunov functions

15 Limit sets. Lyapunov functions 15 Limit sets. Lyapunov functions At this point, considering the solutions to ẋ = f(x), x U R 2, (1) we were most interested in the behavior of solutions when t (sometimes, this is called asymptotic behavior

More information

Example 4.1 (nonlinear pendulum dynamics with friction) Figure 4.1: Pendulum. asin. k, a, and b. We study stability of the origin x

Example 4.1 (nonlinear pendulum dynamics with friction) Figure 4.1: Pendulum. asin. k, a, and b. We study stability of the origin x Lecture 4. LaSalle s Invariance Principle We begin with a motivating eample. Eample 4.1 (nonlinear pendulum dynamics with friction) Figure 4.1: Pendulum Dynamics of a pendulum with friction can be written

More information

Predictive Control Algorithms: Stability despite Shortened Optimization Horizons

Predictive Control Algorithms: Stability despite Shortened Optimization Horizons Predictive Control Algorithms: Stability despite Shortened Optimization Horizons Philipp Braun Jürgen Pannek Karl Worthmann University of Bayreuth, 9544 Bayreuth, Germany University of the Federal Armed

More information

6.231 Dynamic Programming Midterm, Fall 2008. Instructions

6.231 Dynamic Programming Midterm, Fall 2008. Instructions 6.231 Dynamic Programming Midterm, Fall 2008 Instructions The midterm comprises three problems. Problem 1 is worth 60 points, problem 2 is worth 40 points, and problem 3 is worth 40 points. Your grade

More information

Using the Theory of Reals in. Analyzing Continuous and Hybrid Systems

Using the Theory of Reals in. Analyzing Continuous and Hybrid Systems Using the Theory of Reals in Analyzing Continuous and Hybrid Systems Ashish Tiwari Computer Science Laboratory (CSL) SRI International (SRI) Menlo Park, CA 94025 Email: ashish.tiwari@sri.com Ashish Tiwari

More information

Passive control. Carles Batlle. II EURON/GEOPLEX Summer School on Modeling and Control of Complex Dynamical Systems Bertinoro, Italy, July 18-22 2005

Passive control. Carles Batlle. II EURON/GEOPLEX Summer School on Modeling and Control of Complex Dynamical Systems Bertinoro, Italy, July 18-22 2005 Passive control theory I Carles Batlle II EURON/GEOPLEX Summer School on Modeling and Control of Complex Dynamical Systems Bertinoro, Italy, July 18-22 25 Contents of this lecture Change of paradigm in

More information

PID Controller Design for Nonlinear Systems Using Discrete-Time Local Model Networks

PID Controller Design for Nonlinear Systems Using Discrete-Time Local Model Networks PID Controller Design for Nonlinear Systems Using Discrete-Time Local Model Networks 4. Workshop für Modellbasierte Kalibriermethoden Nikolaus Euler-Rolle, Christoph Hametner, Stefan Jakubek Christian

More information

Elgersburg Workshop 2010, 1.-4. März 2010 1. Path-Following for Nonlinear Systems Subject to Constraints Timm Faulwasser

Elgersburg Workshop 2010, 1.-4. März 2010 1. Path-Following for Nonlinear Systems Subject to Constraints Timm Faulwasser #96230155 2010 Photos.com, ein Unternehmensbereich von Getty Images. Alle Rechte vorbehalten. Steering a Car as a Control Problem Path-Following for Nonlinear Systems Subject to Constraints Chair for Systems

More information

Dimension Theory for Ordinary Differential Equations

Dimension Theory for Ordinary Differential Equations Vladimir A. Boichenko, Gennadij A. Leonov, Volker Reitmann Dimension Theory for Ordinary Differential Equations Teubner Contents Singular values, exterior calculus and Lozinskii-norms 15 1 Singular values

More information

The Max-Distance Network Creation Game on General Host Graphs

The Max-Distance Network Creation Game on General Host Graphs The Max-Distance Network Creation Game on General Host Graphs 13 Luglio 2012 Introduction Network Creation Games are games that model the formation of large-scale networks governed by autonomous agents.

More information

Formulations of Model Predictive Control. Dipartimento di Elettronica e Informazione

Formulations of Model Predictive Control. Dipartimento di Elettronica e Informazione Formulations of Model Predictive Control Riccardo Scattolini Riccardo Scattolini Dipartimento di Elettronica e Informazione Impulse and step response models 2 At the beginning of the 80, the early formulations

More information

A Robust Optimization Approach to Supply Chain Management

A Robust Optimization Approach to Supply Chain Management A Robust Optimization Approach to Supply Chain Management Dimitris Bertsimas and Aurélie Thiele Massachusetts Institute of Technology, Cambridge MA 0139, dbertsim@mit.edu, aurelie@mit.edu Abstract. We

More information

Maximization versus environmental compliance

Maximization versus environmental compliance Maximization versus environmental compliance Increase use of alternative fuels with no risk for quality and environment Reprint from World Cement March 2005 Dr. Eduardo Gallestey, ABB, Switzerland, discusses

More information

INPUT-TO-STATE STABILITY FOR DISCRETE-TIME NONLINEAR SYSTEMS

INPUT-TO-STATE STABILITY FOR DISCRETE-TIME NONLINEAR SYSTEMS INPUT-TO-STATE STABILITY FOR DISCRETE-TIME NONLINEAR SYSTEMS Zhong-Ping Jiang Eduardo Sontag,1 Yuan Wang,2 Department of Electrical Engineering, Polytechnic University, Six Metrotech Center, Brooklyn,

More information

Chapter 3 Nonlinear Model Predictive Control

Chapter 3 Nonlinear Model Predictive Control Chapter 3 Nonlinear Model Predictive Control In this chapter, we introduce the nonlinear model predictive control algorithm in a rigorous way. We start by defining a basic NMPC algorithm for constant reference

More information

Lecture 13 Linear quadratic Lyapunov theory

Lecture 13 Linear quadratic Lyapunov theory EE363 Winter 28-9 Lecture 13 Linear quadratic Lyapunov theory the Lyapunov equation Lyapunov stability conditions the Lyapunov operator and integral evaluating quadratic integrals analysis of ARE discrete-time

More information

2.3 Convex Constrained Optimization Problems

2.3 Convex Constrained Optimization Problems 42 CHAPTER 2. FUNDAMENTAL CONCEPTS IN CONVEX OPTIMIZATION Theorem 15 Let f : R n R and h : R R. Consider g(x) = h(f(x)) for all x R n. The function g is convex if either of the following two conditions

More information

Nonlinear Systems and Control Lecture # 15 Positive Real Transfer Functions & Connection with Lyapunov Stability. p. 1/?

Nonlinear Systems and Control Lecture # 15 Positive Real Transfer Functions & Connection with Lyapunov Stability. p. 1/? Nonlinear Systems and Control Lecture # 15 Positive Real Transfer Functions & Connection with Lyapunov Stability p. 1/? p. 2/? Definition: A p p proper rational transfer function matrix G(s) is positive

More information

A Passivity Measure Of Systems In Cascade Based On Passivity Indices

A Passivity Measure Of Systems In Cascade Based On Passivity Indices 49th IEEE Conference on Decision and Control December 5-7, Hilton Atlanta Hotel, Atlanta, GA, USA A Passivity Measure Of Systems In Cascade Based On Passivity Indices Han Yu and Panos J Antsaklis Abstract

More information

State-Driven Testing of Distributed Systems: Appendix

State-Driven Testing of Distributed Systems: Appendix State-Driven Testing of Distributed Systems: Appendix Domenico Cotroneo, Roberto Natella, Stefano Russo, Fabio Scippacercola Università degli Studi di Napoli Federico II {cotroneo,roberto.natella,sterusso,fabio.scippacercola}@unina.it

More information

Notes V General Equilibrium: Positive Theory. 1 Walrasian Equilibrium and Excess Demand

Notes V General Equilibrium: Positive Theory. 1 Walrasian Equilibrium and Excess Demand Notes V General Equilibrium: Positive Theory In this lecture we go on considering a general equilibrium model of a private ownership economy. In contrast to the Notes IV, we focus on positive issues such

More information

Change Management in Enterprise IT Systems: Process Modeling and Capacity-optimal Scheduling

Change Management in Enterprise IT Systems: Process Modeling and Capacity-optimal Scheduling Change Management in Enterprise IT Systems: Process Modeling and Capacity-optimal Scheduling Praveen K. Muthusamy, Koushik Kar, Sambit Sahu, Prashant Pradhan and Saswati Sarkar Rensselaer Polytechnic Institute

More information

An Asymptotically Optimal Scheme for P2P File Sharing

An Asymptotically Optimal Scheme for P2P File Sharing An Asymptotically Optimal Scheme for P2P File Sharing Panayotis Antoniadis Costas Courcoubetis Richard Weber Athens University of Economics and Business Athens University of Economics and Business Centre

More information

Strengthening International Courts and the Early Settlement of Disputes

Strengthening International Courts and the Early Settlement of Disputes Strengthening International Courts and the Early Settlement of Disputes Michael Gilligan, Leslie Johns, and B. Peter Rosendorff November 18, 2008 Technical Appendix Definitions σ(ŝ) {π [0, 1] s(π) = ŝ}

More information

4 Lyapunov Stability Theory

4 Lyapunov Stability Theory 4 Lyapunov Stability Theory In this section we review the tools of Lyapunov stability theory. These tools will be used in the next section to analyze the stability properties of a robot controller. We

More information

6.231 Dynamic Programming and Stochastic Control Fall 2008

6.231 Dynamic Programming and Stochastic Control Fall 2008 MIT OpenCourseWare http://ocw.mit.edu 6.231 Dynamic Programming and Stochastic Control Fall 2008 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. 6.231

More information

Predictive Control Algorithms for Nonlinear Systems

Predictive Control Algorithms for Nonlinear Systems Predictive Control Algorithms for Nonlinear Systems DOCTORAL THESIS for receiving the doctoral degree from the Gh. Asachi Technical University of Iaşi, România The Defense will take place on 15 September

More information

A New Nature-inspired Algorithm for Load Balancing

A New Nature-inspired Algorithm for Load Balancing A New Nature-inspired Algorithm for Load Balancing Xiang Feng East China University of Science and Technology Shanghai, China 200237 Email: xfeng{@ecusteducn, @cshkuhk} Francis CM Lau The University of

More information

Proposal for Communication Ph.D. at UC Davis 8

Proposal for Communication Ph.D. at UC Davis 8 Proposal for Communication Ph.D. at UC Davis 8 6. Administration of the Program The program will be administered by the Department of Communication, which is currently administering the Communication M.A.

More information

Mobile Networking Tutorial

Mobile Networking Tutorial Dynamically Managing the Real-time Fabric of a Wireless Sensor-Actuator Network Award No: CNS-09-31195 Duration: Sept. 1 2009 - Aug. 31 2012 M.D. Lemmon, Univ. of Notre Dame (PI) S.X. Hu, Univ. of Notre

More information

Probability Generating Functions

Probability Generating Functions page 39 Chapter 3 Probability Generating Functions 3 Preamble: Generating Functions Generating functions are widely used in mathematics, and play an important role in probability theory Consider a sequence

More information

How will the programme be delivered (e.g. inter-institutional, summerschools, lectures, placement, rotations, on-line etc.):

How will the programme be delivered (e.g. inter-institutional, summerschools, lectures, placement, rotations, on-line etc.): Titles of Programme: Hamilton Hamilton Institute Institute Structured PhD Structured PhD Minimum 30 credits. 15 of Programme which must be obtained from Generic/Transferable skills modules and 15 from

More information

20 Selfish Load Balancing

20 Selfish Load Balancing 20 Selfish Load Balancing Berthold Vöcking Abstract Suppose that a set of weighted tasks shall be assigned to a set of machines with possibly different speeds such that the load is distributed evenly among

More information

Control Systems with Actuator Saturation

Control Systems with Actuator Saturation Control Systems with Actuator Saturation Analysis and Design Tingshu Hu Zongli Lin With 67 Figures Birkhauser Boston Basel Berlin Preface xiii 1 Introduction 1 1.1 Linear Systems with Actuator Saturation

More information

LOOP TRANSFER RECOVERY FOR SAMPLED-DATA SYSTEMS 1

LOOP TRANSFER RECOVERY FOR SAMPLED-DATA SYSTEMS 1 LOOP TRANSFER RECOVERY FOR SAMPLED-DATA SYSTEMS 1 Henrik Niemann Jakob Stoustrup Mike Lind Rank Bahram Shafai Dept. of Automation, Technical University of Denmark, Building 326, DK-2800 Lyngby, Denmark

More information

Investigación Operativa. The uniform rule in the division problem

Investigación Operativa. The uniform rule in the division problem Boletín de Estadística e Investigación Operativa Vol. 27, No. 2, Junio 2011, pp. 102-112 Investigación Operativa The uniform rule in the division problem Gustavo Bergantiños Cid Dept. de Estadística e

More information

Programme Specification MSc/PGDip/PGCert Sustainable Building: Performance and Design

Programme Specification MSc/PGDip/PGCert Sustainable Building: Performance and Design Programme Specification MSc/PGDip/PGCert Sustainable Building: Performance and Design Valid from: September 2012 Faculty of Technology, Design and Environment Oxford Brookes University SECTION 1: GENERAL

More information

RELATIONSHIPS BETWEEN AFFINE FEEDBACK POLICIES FOR ROBUST CONTROL WITH CONSTRAINTS. Paul J. Goulart Eric C. Kerrigan

RELATIONSHIPS BETWEEN AFFINE FEEDBACK POLICIES FOR ROBUST CONTROL WITH CONSTRAINTS. Paul J. Goulart Eric C. Kerrigan RELATIOSHIPS BETWEE AFFIE FEEDBACK POLICIES FOR ROBUST COTROL WITH COSTRAITS Paul J Goulart Eric C Kerrigan Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK

More information

Chapter 5. Linear Inequalities and Linear Programming. Linear Programming in Two Dimensions: A Geometric Approach

Chapter 5. Linear Inequalities and Linear Programming. Linear Programming in Two Dimensions: A Geometric Approach Chapter 5 Linear Programming in Two Dimensions: A Geometric Approach Linear Inequalities and Linear Programming Section 3 Linear Programming gin Two Dimensions: A Geometric Approach In this section, we

More information

PUTNAM TRAINING POLYNOMIALS. Exercises 1. Find a polynomial with integral coefficients whose zeros include 2 + 5.

PUTNAM TRAINING POLYNOMIALS. Exercises 1. Find a polynomial with integral coefficients whose zeros include 2 + 5. PUTNAM TRAINING POLYNOMIALS (Last updated: November 17, 2015) Remark. This is a list of exercises on polynomials. Miguel A. Lerma Exercises 1. Find a polynomial with integral coefficients whose zeros include

More information

Generating Direct Powers

Generating Direct Powers Generating Direct Powers Nik Ruškuc nik@mcs.st-and.ac.uk School of Mathematics and Statistics, Novi Sad, 16 March 2012 Algebraic structures Classical: groups, rings, modules, algebras, Lie algebras. Semigroups.

More information

Chapter 7: Termination Detection

Chapter 7: Termination Detection Chapter 7: Termination Detection Ajay Kshemkalyani and Mukesh Singhal Distributed Computing: Principles, Algorithms, and Systems Cambridge University Press A. Kshemkalyani and M. Singhal (Distributed Computing)

More information

Network Traffic Modelling

Network Traffic Modelling University of York Dissertation submitted for the MSc in Mathematics with Modern Applications, Department of Mathematics, University of York, UK. August 009 Network Traffic Modelling Author: David Slade

More information

Exam Introduction Mathematical Finance and Insurance

Exam Introduction Mathematical Finance and Insurance Exam Introduction Mathematical Finance and Insurance Date: January 8, 2013. Duration: 3 hours. This is a closed-book exam. The exam does not use scrap cards. Simple calculators are allowed. The questions

More information

Chapter 7 Robust Stabilization and Disturbance Attenuation of Switched Linear Parameter-Varying Systems in Discrete Time

Chapter 7 Robust Stabilization and Disturbance Attenuation of Switched Linear Parameter-Varying Systems in Discrete Time Chapter 7 Robust Stabilization and Disturbance Attenuation of Switched Linear Parameter-Varying Systems in Discrete Time Ji-Woong Lee and Geir E. Dullerud Abstract Nonconservative analysis of discrete-time

More information

Lecture 2: Consumer Theory

Lecture 2: Consumer Theory Lecture 2: Consumer Theory Preferences and Utility Utility Maximization (the primal problem) Expenditure Minimization (the dual) First we explore how consumers preferences give rise to a utility fct which

More information

LECTURE 15: AMERICAN OPTIONS

LECTURE 15: AMERICAN OPTIONS LECTURE 15: AMERICAN OPTIONS 1. Introduction All of the options that we have considered thus far have been of the European variety: exercise is permitted only at the termination of the contract. These

More information

Proximal mapping via network optimization

Proximal mapping via network optimization L. Vandenberghe EE236C (Spring 23-4) Proximal mapping via network optimization minimum cut and maximum flow problems parametric minimum cut problem application to proximal mapping Introduction this lecture:

More information

Chapter 7. Sealed-bid Auctions

Chapter 7. Sealed-bid Auctions Chapter 7 Sealed-bid Auctions An auction is a procedure used for selling and buying items by offering them up for bid. Auctions are often used to sell objects that have a variable price (for example oil)

More information

Spanish Regional Accounts. Base 2010. Regional Gross Domestic Product. Year 2014 Income accounts of the household sector.

Spanish Regional Accounts. Base 2010. Regional Gross Domestic Product. Year 2014 Income accounts of the household sector. 27 March 2015 Spanish Regional Accounts. Base 2010 Regional Gross Domestic Product. Year 2014 Income accounts of the household sector. 2010-2012 series Main results Regional Gross Domestic Product. Year

More information

Chapter 7 ELECTRICITY PRICES IN A GAME THEORY CONTEXT. 1. Introduction. Mireille Bossy. Geert Jan Olsder Odile Pourtallier Etienne Tanré

Chapter 7 ELECTRICITY PRICES IN A GAME THEORY CONTEXT. 1. Introduction. Mireille Bossy. Geert Jan Olsder Odile Pourtallier Etienne Tanré Chapter 7 ELECTRICITY PRICES IN A GAME THEORY CONTEXT Mireille Bossy Nadia Maïzi Geert Jan Olsder Odile Pourtallier Etienne Tanré Abstract We consider a model of an electricity market in which S suppliers

More information

Hacking-proofness and Stability in a Model of Information Security Networks

Hacking-proofness and Stability in a Model of Information Security Networks Hacking-proofness and Stability in a Model of Information Security Networks Sunghoon Hong Preliminary draft, not for citation. March 1, 2008 Abstract We introduce a model of information security networks.

More information

Decentralized Utility-based Sensor Network Design

Decentralized Utility-based Sensor Network Design Decentralized Utility-based Sensor Network Design Narayanan Sadagopan and Bhaskar Krishnamachari University of Southern California, Los Angeles, CA 90089-0781, USA narayans@cs.usc.edu, bkrishna@usc.edu

More information

Roots of Polynomials

Roots of Polynomials Roots of Polynomials (Com S 477/577 Notes) Yan-Bin Jia Sep 24, 2015 A direct corollary of the fundamental theorem of algebra is that p(x) can be factorized over the complex domain into a product a n (x

More information

Load balancing of temporary tasks in the l p norm

Load balancing of temporary tasks in the l p norm Load balancing of temporary tasks in the l p norm Yossi Azar a,1, Amir Epstein a,2, Leah Epstein b,3 a School of Computer Science, Tel Aviv University, Tel Aviv, Israel. b School of Computer Science, The

More information

A Note on Best Response Dynamics

A Note on Best Response Dynamics Games and Economic Behavior 29, 138 150 (1999) Article ID game.1997.0636, available online at http://www.idealibrary.com on A Note on Best Response Dynamics Ed Hopkins Department of Economics, University

More information

CPC/CPA Hybrid Bidding in a Second Price Auction

CPC/CPA Hybrid Bidding in a Second Price Auction CPC/CPA Hybrid Bidding in a Second Price Auction Benjamin Edelman Hoan Soo Lee Working Paper 09-074 Copyright 2008 by Benjamin Edelman and Hoan Soo Lee Working papers are in draft form. This working paper

More information

SOLUTIONS TO ASSIGNMENT 1 MATH 576

SOLUTIONS TO ASSIGNMENT 1 MATH 576 SOLUTIONS TO ASSIGNMENT 1 MATH 576 SOLUTIONS BY OLIVIER MARTIN 13 #5. Let T be the topology generated by A on X. We want to show T = J B J where B is the set of all topologies J on X with A J. This amounts

More information

Evolution Feature Oriented Model Driven Product Line Engineering Approach for Synergistic and Dynamic Service Evolution in Clouds

Evolution Feature Oriented Model Driven Product Line Engineering Approach for Synergistic and Dynamic Service Evolution in Clouds Evolution Feature Oriented Model Driven Product Line Engineering Approach for Synergistic and Dynamic Service Evolution in Clouds Zhe Wang, Xiaodong Liu, Kevin Chalmers School of Computing Edinburgh Napier

More information

Scheduling Real-time Tasks: Algorithms and Complexity

Scheduling Real-time Tasks: Algorithms and Complexity Scheduling Real-time Tasks: Algorithms and Complexity Sanjoy Baruah The University of North Carolina at Chapel Hill Email: baruah@cs.unc.edu Joël Goossens Université Libre de Bruxelles Email: joel.goossens@ulb.ac.be

More information

Linear Programming Notes V Problem Transformations

Linear Programming Notes V Problem Transformations Linear Programming Notes V Problem Transformations 1 Introduction Any linear programming problem can be rewritten in either of two standard forms. In the first form, the objective is to maximize, the material

More information

4/1/2017. PS. Sequences and Series FROM 9.2 AND 9.3 IN THE BOOK AS WELL AS FROM OTHER SOURCES. TODAY IS NATIONAL MANATEE APPRECIATION DAY

4/1/2017. PS. Sequences and Series FROM 9.2 AND 9.3 IN THE BOOK AS WELL AS FROM OTHER SOURCES. TODAY IS NATIONAL MANATEE APPRECIATION DAY PS. Sequences and Series FROM 9.2 AND 9.3 IN THE BOOK AS WELL AS FROM OTHER SOURCES. TODAY IS NATIONAL MANATEE APPRECIATION DAY 1 Oh the things you should learn How to recognize and write arithmetic sequences

More information

Scheduling Algorithm with Optimization of Employee Satisfaction

Scheduling Algorithm with Optimization of Employee Satisfaction Washington University in St. Louis Scheduling Algorithm with Optimization of Employee Satisfaction by Philip I. Thomas Senior Design Project http : //students.cec.wustl.edu/ pit1/ Advised By Associate

More information

Linear-Quadratic Optimal Controller 10.3 Optimal Linear Control Systems

Linear-Quadratic Optimal Controller 10.3 Optimal Linear Control Systems Linear-Quadratic Optimal Controller 10.3 Optimal Linear Control Systems In Chapters 8 and 9 of this book we have designed dynamic controllers such that the closed-loop systems display the desired transient

More information

SOFTWARE ENGINEERING OVERVIEW

SOFTWARE ENGINEERING OVERVIEW SOFTWARE ENGINEERING OVERVIEW http://www.tutorialspoint.com/software_engineering/software_engineering_overview.htm Copyright tutorialspoint.com Let us first understand what software engineering stands

More information

Development of dynamically evolving and self-adaptive software. 1. Background

Development of dynamically evolving and self-adaptive software. 1. Background Development of dynamically evolving and self-adaptive software 1. Background LASER 2013 Isola d Elba, September 2013 Carlo Ghezzi Politecnico di Milano Deep-SE Group @ DEIB 1 Requirements Functional requirements

More information

DYNAMICAL NETWORKS: structural analysis and synthesis

DYNAMICAL NETWORKS: structural analysis and synthesis A social networks synchronization DYNAMICAL NETWORKS: structural analysis and synthesis traffic management B C (bio)chemical processes water distribution networks biological systems, ecosystems production

More information

OPTIMAL CONTROL OF A COMMERCIAL LOAN REPAYMENT PLAN. E.V. Grigorieva. E.N. Khailov

OPTIMAL CONTROL OF A COMMERCIAL LOAN REPAYMENT PLAN. E.V. Grigorieva. E.N. Khailov DISCRETE AND CONTINUOUS Website: http://aimsciences.org DYNAMICAL SYSTEMS Supplement Volume 2005 pp. 345 354 OPTIMAL CONTROL OF A COMMERCIAL LOAN REPAYMENT PLAN E.V. Grigorieva Department of Mathematics

More information

Stationary random graphs on Z with prescribed iid degrees and finite mean connections

Stationary random graphs on Z with prescribed iid degrees and finite mean connections Stationary random graphs on Z with prescribed iid degrees and finite mean connections Maria Deijfen Johan Jonasson February 2006 Abstract Let F be a probability distribution with support on the non-negative

More information

When Promotions Meet Operations: Cross-Selling and Its Effect on Call-Center Performance

When Promotions Meet Operations: Cross-Selling and Its Effect on Call-Center Performance When Promotions Meet Operations: Cross-Selling and Its Effect on Call-Center Performance Mor Armony 1 Itay Gurvich 2 Submitted July 28, 2006; Revised August 31, 2007 Abstract We study cross-selling operations

More information

Modèle géométrique de plissement constraint par la rhéologie et l équilibre mécanique: Application possibe en régime extensif?

Modèle géométrique de plissement constraint par la rhéologie et l équilibre mécanique: Application possibe en régime extensif? Modèle géométrique de plissement constraint par la rhéologie et l équilibre mécanique: Application possibe en régime extensif? Yves M. Leroy (Laboratoire de Géologie, ENS), Bertrand Maillot (University

More information

Training on Quality Assurance in PhD. Dissertation and mentorship: what standards should there be? by Mariangela SCIANDRA

Training on Quality Assurance in PhD. Dissertation and mentorship: what standards should there be? by Mariangela SCIANDRA Training on Quality Assurance in PhD Dissertation and mentorship: what standards should there be? by Mariangela SCIANDRA Guidelines and Principles for Accreditation Aim: to illustrate the main Principles

More information

Cyber-Security Analysis of State Estimators in Power Systems

Cyber-Security Analysis of State Estimators in Power Systems Cyber-Security Analysis of State Estimators in Electric Power Systems André Teixeira 1, Saurabh Amin 2, Henrik Sandberg 1, Karl H. Johansson 1, and Shankar Sastry 2 ACCESS Linnaeus Centre, KTH-Royal Institute

More information

Title: Integrating Management of Truck and Rail Systems in LA. INTERIM REPORT August 2015

Title: Integrating Management of Truck and Rail Systems in LA. INTERIM REPORT August 2015 Title: Integrating Management of Truck and Rail Systems in LA Project Number: 3.1a Year: 2013-2017 INTERIM REPORT August 2015 Principal Investigator Maged Dessouky Researcher Lunce Fu MetroFreight Center

More information

Brief Paper. of discrete-time linear systems. www.ietdl.org

Brief Paper. of discrete-time linear systems. www.ietdl.org Published in IET Control Theory and Applications Received on 28th August 2012 Accepted on 26th October 2012 Brief Paper ISSN 1751-8644 Temporal and one-step stabilisability and detectability of discrete-time

More information

Student Project Allocation Using Integer Programming

Student Project Allocation Using Integer Programming IEEE TRANSACTIONS ON EDUCATION, VOL. 46, NO. 3, AUGUST 2003 359 Student Project Allocation Using Integer Programming A. A. Anwar and A. S. Bahaj, Member, IEEE Abstract The allocation of projects to students

More information

24. The Branch and Bound Method

24. The Branch and Bound Method 24. The Branch and Bound Method It has serious practical consequences if it is known that a combinatorial problem is NP-complete. Then one can conclude according to the present state of science that no

More information

Development of global specification for dynamically adaptive software

Development of global specification for dynamically adaptive software Development of global specification for dynamically adaptive software Yongwang Zhao School of Computer Science & Engineering Beihang University zhaoyw@act.buaa.edu.cn 22/02/2013 1 2 About me Assistant

More information

Analysis of an Artificial Hormone System (Extended abstract)

Analysis of an Artificial Hormone System (Extended abstract) c 2013. This is the author s version of the work. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purpose or for creating

More information

Faculty of Computer Science

Faculty of Computer Science Faculty of Computer Science PhD programme in COMPUTER SCIENCE Website: http://www.unibz.it/en/inf/progs/phdcs/default.html Duration: 3 years Academic year: 2015/2016 Start date: 01/11/2015 Official programme

More information

Specific amendments to the Capacity Allocation and Congestion Management Network Code

Specific amendments to the Capacity Allocation and Congestion Management Network Code Annex: Specific amendments to the Capacity Allocation and Congestion Management Network Code I. Amendments with respect to entry into force and application The Network Code defines deadlines for several

More information

Review of Basic Options Concepts and Terminology

Review of Basic Options Concepts and Terminology Review of Basic Options Concepts and Terminology March 24, 2005 1 Introduction The purchase of an options contract gives the buyer the right to buy call options contract or sell put options contract some

More information

10.2 Series and Convergence

10.2 Series and Convergence 10.2 Series and Convergence Write sums using sigma notation Find the partial sums of series and determine convergence or divergence of infinite series Find the N th partial sums of geometric series and

More information

Optimization models for congestion control with multipath routing in TCP/IP networks

Optimization models for congestion control with multipath routing in TCP/IP networks Optimization models for congestion control with multipath routing in TCP/IP networks Roberto Cominetti Cristóbal Guzmán Departamento de Ingeniería Industrial Universidad de Chile Workshop on Optimization,

More information

Analysis of Approximation Algorithms for k-set Cover using Factor-Revealing Linear Programs

Analysis of Approximation Algorithms for k-set Cover using Factor-Revealing Linear Programs Analysis of Approximation Algorithms for k-set Cover using Factor-Revealing Linear Programs Stavros Athanassopoulos, Ioannis Caragiannis, and Christos Kaklamanis Research Academic Computer Technology Institute

More information

Tail inequalities for order statistics of log-concave vectors and applications

Tail inequalities for order statistics of log-concave vectors and applications Tail inequalities for order statistics of log-concave vectors and applications Rafał Latała Based in part on a joint work with R.Adamczak, A.E.Litvak, A.Pajor and N.Tomczak-Jaegermann Banff, May 2011 Basic

More information

Management and optimization of multiple supply chains

Management and optimization of multiple supply chains Management and optimization of multiple supply chains J. Dorn Technische Universität Wien, Institut für Informationssysteme Paniglgasse 16, A-1040 Wien, Austria Phone ++43-1-58801-18426, Fax ++43-1-58801-18494

More information

Linear Programming I

Linear Programming I Linear Programming I November 30, 2003 1 Introduction In the VCR/guns/nuclear bombs/napkins/star wars/professors/butter/mice problem, the benevolent dictator, Bigus Piguinus, of south Antarctica penguins

More information

The Performance Management Process How to establish goals, objectives and KPI s

The Performance Management Process How to establish goals, objectives and KPI s Performance Management Part 3 The Performance Management Process How to establish goals, objectives and KPI s Agenda Review of what is Performance Management? Developing measures Goals, Objectives & KPI

More information

Product Synthesis. CATIA - Product Engineering Optimizer 2 (PEO) CATIA V5R18

Product Synthesis. CATIA - Product Engineering Optimizer 2 (PEO) CATIA V5R18 Product Synthesis CATIA - Product Engineering Optimizer 2 (PEO) CATIA V5R18 Product Synthesis CATIA - Product Engineering Optimizer Accelerates design alternatives exploration and optimization according

More information

MSc International Banking and Financial Services For students entering in 2006

MSc International Banking and Financial Services For students entering in 2006 MSc International Banking and Financial Services For students entering in 2006 Awarding Institution Teaching Institution Faculty of Economic and Social Sciences Date of specification: October 2006 Programme

More information

Lecture 11: Sponsored search

Lecture 11: Sponsored search Computational Learning Theory Spring Semester, 2009/10 Lecture 11: Sponsored search Lecturer: Yishay Mansour Scribe: Ben Pere, Jonathan Heimann, Alon Levin 11.1 Sponsored Search 11.1.1 Introduction Search

More information

Applied mathematics and mathematical statistics

Applied mathematics and mathematical statistics Applied mathematics and mathematical statistics The graduate school is organised within the Department of Mathematical Sciences.. Deputy head of department: Aila Särkkä Director of Graduate Studies: Marija

More information

Linear Programming for Optimization. Mark A. Schulze, Ph.D. Perceptive Scientific Instruments, Inc.

Linear Programming for Optimization. Mark A. Schulze, Ph.D. Perceptive Scientific Instruments, Inc. 1. Introduction Linear Programming for Optimization Mark A. Schulze, Ph.D. Perceptive Scientific Instruments, Inc. 1.1 Definition Linear programming is the name of a branch of applied mathematics that

More information

Chapter 3: Section 3-3 Solutions of Linear Programming Problems

Chapter 3: Section 3-3 Solutions of Linear Programming Problems Chapter 3: Section 3-3 Solutions of Linear Programming Problems D. S. Malik Creighton University, Omaha, NE D. S. Malik Creighton University, Omaha, NE Chapter () 3: Section 3-3 Solutions of Linear Programming

More information

CONTROL SYSTEMS, ROBOTICS AND AUTOMATION Vol. XVI - Fault Accomodation Using Model Predictive Methods - Jovan D. Bošković and Raman K.

CONTROL SYSTEMS, ROBOTICS AND AUTOMATION Vol. XVI - Fault Accomodation Using Model Predictive Methods - Jovan D. Bošković and Raman K. FAULT ACCOMMODATION USING MODEL PREDICTIVE METHODS Scientific Systems Company, Inc., Woburn, Massachusetts, USA. Keywords: Fault accommodation, Model Predictive Control (MPC), Failure Detection, Identification

More information

The Heat Equation. Lectures INF2320 p. 1/88

The Heat Equation. Lectures INF2320 p. 1/88 The Heat Equation Lectures INF232 p. 1/88 Lectures INF232 p. 2/88 The Heat Equation We study the heat equation: u t = u xx for x (,1), t >, (1) u(,t) = u(1,t) = for t >, (2) u(x,) = f(x) for x (,1), (3)

More information

30.10.2014 Prof. Jamie Ward Director of Doctoral Studies School of Psychology, University of Sussex jamiew@sussex.ac.uk

30.10.2014 Prof. Jamie Ward Director of Doctoral Studies School of Psychology, University of Sussex jamiew@sussex.ac.uk 30.10.2014 Prof. Jamie Ward Director of Doctoral Studies School of Psychology, University of Sussex jamiew@sussex.ac.uk Applying for a PhD in Psychology at the University of Sussex for 2015 Entry What

More information

1 Introduction. Linear Programming. Questions. A general optimization problem is of the form: choose x to. max f(x) subject to x S. where.

1 Introduction. Linear Programming. Questions. A general optimization problem is of the form: choose x to. max f(x) subject to x S. where. Introduction Linear Programming Neil Laws TT 00 A general optimization problem is of the form: choose x to maximise f(x) subject to x S where x = (x,..., x n ) T, f : R n R is the objective function, S

More information

large-scale machine learning revisited Léon Bottou Microsoft Research (NYC)

large-scale machine learning revisited Léon Bottou Microsoft Research (NYC) large-scale machine learning revisited Léon Bottou Microsoft Research (NYC) 1 three frequent ideas in machine learning. independent and identically distributed data This experimental paradigm has driven

More information