Dissipativity. Outline. Motivation. Dissipative Systems. M. Sami Fadali EBME Dept., UNR

Size: px
Start display at page:

Download "Dissipativity. Outline. Motivation. Dissipative Systems. M. Sami Fadali EBME Dept., UNR"

Transcription

1 Dissipativity M. Sami Fadali EBME Dept., UNR 1 Outline Differential storage functions. QSR Dissipativity. Algebraic conditions for dissipativity. Stability of dissipative systems. Feedback Interconnections Control design. 2 Motivation Passivity requires a positive energy function Generalize the energy supplied. Use state space realizations. Show the connections between inputoutput stability and Lyapunov stability analysis. Dissipative Systems = space of input functions = space of output functions =state space 3 4

2 Supply Rate Function of the input output and the Dissipative Dynamical System Definition 9.1: A dynamical system is dissipative w.r.t. the supply rate if a storage function that and, satisfies the dissipation inequality Locally absolutely integrable function storage function = energy stored by the system at time 5 = energy externally supplied to in 6 Implications of Definition 9.1 A dissipative system does not create energy. For a motion along a closed trajectory Nonnegative energy required to complete a closed trajectory. 7 Passivity A system is passive if a positive semidefinite storage function s.t. for all admissible inputs solutions and Identical to the definition of Chapter 8 with, supply rate 8

3 Differentiable Storage Function Take limit as Dissipativity Restated Definition 9.2: A dynamical system is dissipative w.r.t. the supply rate if a continuously differentiable positive definite storage function that, and, satisfies the dissipation inequality ) Differential dissipation inequality 9 Pos. Definite: class 10 ISS and Dissipativity Lemma 9.1: A system is input to state stable (ISS) if and only if it is dissipative w.r.t. the supply rate = functions of class Proof Definition 9.2: A system is input to state stable (ISS) iff it is dissipative w.r.t. the supply rate, Dissipative i.e. Theorem 7.6: ISS Lyapunov function is positive definite and satisfies the condition 11 Theorem 7.3 (p.191) A system is ISS iff an ISS Lyapunov function=. 12

4 Supply Rate Definition 9.3: Given constant matrices the supply rate is 13 Dissipativity Definition 9.4: A system is dissipative if a storage function s.t. and, Note: This is a special case of the storage function of Definition 9.1 with no statespace model. It is an input output property 14 Dissipativity: Passive Passive= is dissipative with Dissipativity: Strictly Passive Striclty Passive= is dissipative with =stored energy at 15 16

5 Dissipativity: Finite gain Stable dissipative with Strictly Output Passive dissipative with L L Very Strictly Passive Lemma 9.2 dissipative with If is strictly output passive then it has a finitel gain

6 Example: Spring Mass Damper Spring Mass Damper (cont.) continuously differentiable, positive definite (supply rate) 21 Spring mass damper is QSR dissipative and strictly output passive, with With no damping, system is passive., and the 22 Available Storage Theorem 9.1 Maximum amount of energy that can be extracted from a dissipative system at a given time starting from the initial state.. A dynamical system is dissipative if and only if for the available storage is finite. Moreover, for a dissipative system we have So is a possible storage function

7 Proof: Sufficiency Assume. is zero for positive otherwise Let be an arbitrary input that takes the system from to. Show that is a storage function satisfying 25 Proof: Sufficiency (cont.) Available Storage: starting at.. (dissipative) 26 Proof: Necessity Assume is dissipative, then s.t. Finite. 27 Algebraic Condition for Dissipativity Available storage is not a good way to check dissipativity. Under certain assumptions, we can check dissipativity using the state space realization. Check leads to the KYP lemma for LTI passive systems. 28

8 Assumptions 1. Assume that the state space realization is affine in the input 2. The state space of the system is reachable from, i.e. and and an input s.t. 3. If is dissipative, the available storage is a differentiable function of 29 Theorem 9.2 The affine nonlinear system is dissipative if a differentiable function and functions and satisfying 30 Theorem 9.2 Conditions Proof: Sufficiency 31 32

9 Sufficiency (cont.) Dissipative. 33 Corollary 9.1 If the affine nonlinear system is dissipative, then a differentiable function satisfying Proved in the sufficiency proof of Theorem Special Case: Passive Passive= is dissipative with Special Case: Passive LTI LTI Model Guided by LTI, choose Can consider this as a nonlinear version of KYP Lemma. To show this consider LTI case

10 Strictly Output Passive dissipative with Strictly Passive Requires i.e. no real exists for the strictly passive case. The conditions of Theorem 9.2 cannot be satisfied for the affine system with Stability of Dissipative Systems Assume a continuously differentiable storage function satisfying Assume that is an equilibrium of the unforced system 39 Theorem 9.3 Let be a dissipative dynamical system w.r.t. the continuously differentiable storage function satisfying and assume 1. The equilibrium is a strictly local minimum for 2. The supply rate satisfies Then is a stable equilibrium of the unforced system 40

11 Proof Significance of Theorem 9.3 Define the function is continuously differentiable and positive definite in a neighborhood of Hence, is a stable equilibrium of the unforced system 41 Shows that can provide means of constructing a Lyapunov function. Ties dissipativity to stability i. s. Lyapunov. dissipativity is a special case of the results of the theorem but, because it is an input output property, it provides important links between dissipativity and stability i. s. Lyapunov. 42 Corollary 9.2 Let be a dissipative dynamical system w.r.t. the continuously differentiable storage function satisfying Proof From Theorem 9.3 and assume is a strictly local minimum for 2. The supply rate satisfies is the only solution for which =0 Then is an asymptotically stable equilibrium of the unforced system 43 only at the equilibrium The Corollary follows from La Salle s Theorem. 44

12 Zero state Detectable Definition 9.6: A state space realization is zero state detectable if for any trajectory s.t. for, we have i.e. Some books (Haddad) call this zero state observable. Theorem 9.4 If the system is dissipative and zero state detectable, then the free system is Lyapunov stable if Asymptotically stable if Proof Using Theorem 9.2 and corollary 9.1, if is dissipative then Unforced system The stability results follow from Lyapunov stability theorems. 47 Corollary 9.3 Given a zero state detectable affine state space realization, then the unforced system is Lyapunov stable if is passive. Asymptotically stable if (i) finite gain stable, or (ii) strictly output passive, or (iii) very strictly passive. Proof: Follows from Theorem 9.4 with the appropriate choices of the matrices and of the supply rate. 48

13 Feedback Interconnections Assume that the systems are affine, zero state detectable and completely reachable 49 Theorem 9.5 The feedback interconnection of two dissipative, zero state detectable, completely reachable systems and is stable (asymptotically stable) if for some matrix is negative semidefinite (negative definite) with the supply rate of given by the 50 Proof of Theorem 9.5 Lyapunov function candidate (pos. definite) Substitute zero state detectable:, Result follows from Lyapunov stability theory 51 Corollary 9.4 Under the conditions of Theorem 9.5 If both and are passive, then the feedback system is Lyapunov stable The feedback system is asymptotically stable if one of the following is satisfied 1. Either or is very strictly passive and the other is passive. 2. Both and are strictly passive 3. Both and are strictly output passive 52

14 Proof Corollary 9.4 Under the conditions of Theorem 9.5 passive stable very strictly passive Proof Corollary 9.4 (Cont.) strictly passive strictly output passive Asymptotically stable 53 Asymptotically stable 54 Corollary 9.5 Under the conditions of Theorem 9.5, if and are finite gain stable with gains and respectively, then the feedback system is stable if (asymptotically stable if ) Proof Corollary 9.5 For finite gain stable and dissipative 55 for stability for asymptotic stability 56

15 Nonlinear L Gain Inequality Recall: System is finite gain stable with gain is dissipative with supply rate Assume a differentiable storage function if it Hamilton Jacobi Inequality Difficult Problem Hamilton Jacobi (H J) Inequality 59 has finite L gain less than or equal to if the Hamilton Jacobi inequality is satisfied. Find a storage function that satisfies the Hamilton Jacobi inequality with the maximum gain Easier: estimate an upper bound on that satisfies the inequality Procedure: (i) Guess a storage function, (ii) Find an approximate upper bound for the gain subject to the H J inequality 60

16 Example 9.1 Lyapunov function candidate Example 9.1: More Terms Hamilton Jacobi Inequality LTI Systems Provided that 63 64

17 Riccati Equation Strictly Output Passive has finite L gain less than or equal to R i.e. if the Riccati Equation: R has a solution if 65 dissipative with Lemma 9.2: If a differentiable storage function s. t. then is strictly output passive. denotes the Lie derivative. 66 Apply H J Condition Control Design Hamilton Jacobi inequality with Using the control input and the measurement vector, find a controller that stabilized the plant and reduces the effect of disturbances on the output Minimize gain of mapping L gain: minimize the norm of the mapping. L gain: minimize the L gain of the mapping. Choose 67 68

18 State Feedback Nonlinear L Gain Control Plant Assume State Feedback : selected to optimize the nonlinear L gain of the mapping Full information feedback: the entire state is available. 69 Very difficult to solve the nonlinear L gain problem. Solve a suboptimal problem. Given a desirable exogenous signal attenuation level, find a control such that the L gain of the mapping is less than or equal. Repeat for another controller and iterate till the controller approaches the optimal solution. 70 Theorem 9.6 The closed loop system has a finite L gain if and only if the Hamiltonian inequality has a solution by. The control law is given 71 Proof of Sufficiency Assume satisfies the inequality, and substitute 72

Outline. Input to state Stability. Nonlinear Realization. Recall: _ Space. _ Space: Space of all piecewise continuous functions

Outline. Input to state Stability. Nonlinear Realization. Recall: _ Space. _ Space: Space of all piecewise continuous functions Outline Input to state Stability Motivation for Input to State Stability (ISS) ISS Lyapunov function. Stability theorems. M. Sami Fadali Professor EBME University of Nevada, Reno 1 2 Recall: _ Space _

More information

Steady-state DKF. M. Sami Fadali Professor EE UNR

Steady-state DKF. M. Sami Fadali Professor EE UNR Steady-state DKF M. Sami Fadali Professor EE UNR 1 Outline Stability of linear estimators. Lyapunov equation. Uniform exponential stability. Steady-state behavior of Lyapunov equation. Riccati equation.

More information

Dissipative Systems Analysis and Control

Dissipative Systems Analysis and Control Bernard Brogliato, Rogelio Lozano, Bernhard Maschke and Olav Egeland Dissipative Systems Analysis and Control Theory and Applications 2nd Edition With 94 Figures 4y Sprin er 1 Introduction 1 1.1 Example

More information

Mathematics for Control Theory

Mathematics for Control Theory Mathematics for Control Theory Outline of Dissipativity and Passivity Hanz Richter Mechanical Engineering Department Cleveland State University Reading materials Only as a reference: Charles A. Desoer

More information

Contents. 1 State-Space Linear Systems 5. 2 Linearization Causality, Time Invariance, and Linearity 31

Contents. 1 State-Space Linear Systems 5. 2 Linearization Causality, Time Invariance, and Linearity 31 Contents Preamble xiii Linear Systems I Basic Concepts 1 I System Representation 3 1 State-Space Linear Systems 5 1.1 State-Space Linear Systems 5 1.2 Block Diagrams 7 1.3 Exercises 11 2 Linearization

More information

CDS Solutions to Final Exam

CDS Solutions to Final Exam CDS 22 - Solutions to Final Exam Instructor: Danielle C Tarraf Fall 27 Problem (a) We will compute the H 2 norm of G using state-space methods (see Section 26 in DFT) We begin by finding a minimal state-space

More information

Stability lectures. Stability of Linear Systems. Stability of Linear Systems. Stability of Continuous Systems. EECE 571M/491M, Spring 2008 Lecture 5

Stability lectures. Stability of Linear Systems. Stability of Linear Systems. Stability of Continuous Systems. EECE 571M/491M, Spring 2008 Lecture 5 EECE 571M/491M, Spring 2008 Lecture 5 Stability of Continuous Systems http://courses.ece.ubc.ca/491m moishi@ece.ubc.ca Dr. Meeko Oishi Electrical and Computer Engineering University of British Columbia,

More information

Lecture 8. Chapter 5: Input-Output Stability Chapter 6: Passivity Chapter 14: Passivity-Based Control. Eugenio Schuster.

Lecture 8. Chapter 5: Input-Output Stability Chapter 6: Passivity Chapter 14: Passivity-Based Control. Eugenio Schuster. Lecture 8 Chapter 5: Input-Output Stability Chapter 6: Passivity Chapter 14: Passivity-Based Control Eugenio Schuster schuster@lehigh.edu Mechanical Engineering and Mechanics Lehigh University Lecture

More information

Passivity-based Control of Euler-Lagrange Systems

Passivity-based Control of Euler-Lagrange Systems Romeo Ortega, Antonio Loria, Per Johan Nicklasson and Hebertt Sira-Ramfrez Passivity-based Control of Euler-Lagrange Systems Mechanical, Electrical and Electromechanical Applications Springer Contents

More information

Stability of Parameter Adaptation Algorithms. Big picture

Stability of Parameter Adaptation Algorithms. Big picture ME5895, UConn, Fall 215 Prof. Xu Chen Big picture For ˆθ (k + 1) = ˆθ (k) + [correction term] we haven t talked about whether ˆθ(k) will converge to the true value θ if k. We haven t even talked about

More information

Nonlinear Observers. Jaime A. Moreno. Eléctrica y Computación Instituto de Ingeniería Universidad Nacional Autónoma de México

Nonlinear Observers. Jaime A. Moreno. Eléctrica y Computación Instituto de Ingeniería Universidad Nacional Autónoma de México Nonlinear Observers Jaime A. Moreno JMorenoP@ii.unam.mx Eléctrica y Computación Instituto de Ingeniería Universidad Nacional Autónoma de México XVI Congreso Latinoamericano de Control Automático October

More information

Balancing of Lossless and Passive Systems

Balancing of Lossless and Passive Systems Balancing of Lossless and Passive Systems Arjan van der Schaft Abstract Different balancing techniques are applied to lossless nonlinear systems, with open-loop balancing applied to their scattering representation.

More information

Copyrighted Material. 1.1 Large-Scale Interconnected Dynamical Systems

Copyrighted Material. 1.1 Large-Scale Interconnected Dynamical Systems Chapter One Introduction 1.1 Large-Scale Interconnected Dynamical Systems Modern complex dynamical systems 1 are highly interconnected and mutually interdependent, both physically and through a multitude

More information

Stabilization and Passivity-Based Control

Stabilization and Passivity-Based Control DISC Systems and Control Theory of Nonlinear Systems, 2010 1 Stabilization and Passivity-Based Control Lecture 8 Nonlinear Dynamical Control Systems, Chapter 10, plus handout from R. Sepulchre, Constructive

More information

Observations on the Stability Properties of Cooperative Systems

Observations on the Stability Properties of Cooperative Systems 1 Observations on the Stability Properties of Cooperative Systems Oliver Mason and Mark Verwoerd Abstract We extend two fundamental properties of positive linear time-invariant (LTI) systems to homogeneous

More information

Nonlinear Control Lecture 5: Stability Analysis II

Nonlinear Control Lecture 5: Stability Analysis II Nonlinear Control Lecture 5: Stability Analysis II Farzaneh Abdollahi Department of Electrical Engineering Amirkabir University of Technology Fall 2010 Farzaneh Abdollahi Nonlinear Control Lecture 5 1/41

More information

Lecture 4. Chapter 4: Lyapunov Stability. Eugenio Schuster. Mechanical Engineering and Mechanics Lehigh University.

Lecture 4. Chapter 4: Lyapunov Stability. Eugenio Schuster. Mechanical Engineering and Mechanics Lehigh University. Lecture 4 Chapter 4: Lyapunov Stability Eugenio Schuster schuster@lehigh.edu Mechanical Engineering and Mechanics Lehigh University Lecture 4 p. 1/86 Autonomous Systems Consider the autonomous system ẋ

More information

Nonlinear Control Lecture 7: Passivity

Nonlinear Control Lecture 7: Passivity Nonlinear Control Lecture 7: Passivity Farzaneh Abdollahi Department of Electrical Engineering Amirkabir University of Technology Fall 2011 Farzaneh Abdollahi Nonlinear Control Lecture 7 1/26 Passivity

More information

Digital Control Engineering Analysis and Design

Digital Control Engineering Analysis and Design Digital Control Engineering Analysis and Design M. Sami Fadali Antonio Visioli AMSTERDAM BOSTON HEIDELBERG LONDON NEW YORK OXFORD PARIS SAN DIEGO SAN FRANCISCO SINGAPORE SYDNEY TOKYO Academic Press is

More information

MCE/EEC 647/747: Robot Dynamics and Control. Lecture 8: Basic Lyapunov Stability Theory

MCE/EEC 647/747: Robot Dynamics and Control. Lecture 8: Basic Lyapunov Stability Theory MCE/EEC 647/747: Robot Dynamics and Control Lecture 8: Basic Lyapunov Stability Theory Reading: SHV Appendix Mechanical Engineering Hanz Richter, PhD MCE503 p.1/17 Stability in the sense of Lyapunov A

More information

We are devoted to advance in the study of the behaviour of nonlinear discrete-time systems by means of its energy properties.

We are devoted to advance in the study of the behaviour of nonlinear discrete-time systems by means of its energy properties. Chapter 1 Introduction In this chapter, the reasons for the dissipativity and passivity-related properties to be studied in nonlinear discrete-time systems will be described. The new contributions and

More information

Finite-Time Thermodynamics of Port-Hamiltonian Systems

Finite-Time Thermodynamics of Port-Hamiltonian Systems Finite-Time Thermodynamics of Port-Hamiltonian Systems Henrik Sandberg Automatic Control Lab, ACCESS Linnaeus Centre, KTH (Currently on sabbatical leave at LIDS, MIT) Jean-Charles Delvenne CORE, UC Louvain

More information

6.241 Dynamic Systems and Control

6.241 Dynamic Systems and Control 6.41 Dynamic Systems and Control Lecture 5: H Synthesis Emilio Frazzoli Aeronautics and Astronautics Massachusetts Institute of Technology May 11, 011 E. Frazzoli (MIT) Lecture 5: H Synthesis May 11, 011

More information

AUTOMATIC CONTROL. Andrea M. Zanchettin, PhD Spring Semester, Introduction to Automatic Control & Linear systems (time domain)

AUTOMATIC CONTROL. Andrea M. Zanchettin, PhD Spring Semester, Introduction to Automatic Control & Linear systems (time domain) 1 AUTOMATIC CONTROL Andrea M. Zanchettin, PhD Spring Semester, 2018 Introduction to Automatic Control & Linear systems (time domain) 2 What is automatic control? From Wikipedia Control theory is an interdisciplinary

More information

EE363 homework 8 solutions

EE363 homework 8 solutions EE363 Prof. S. Boyd EE363 homework 8 solutions 1. Lyapunov condition for passivity. The system described by ẋ = f(x, u), y = g(x), x() =, with u(t), y(t) R m, is said to be passive if t u(τ) T y(τ) dτ

More information

Input to state Stability

Input to state Stability Input to state Stability Mini course, Universität Stuttgart, November 2004 Lars Grüne, Mathematisches Institut, Universität Bayreuth Part IV: Applications ISS Consider with solutions ϕ(t, x, w) ẋ(t) =

More information

Hybrid Control and Switched Systems. Lecture #11 Stability of switched system: Arbitrary switching

Hybrid Control and Switched Systems. Lecture #11 Stability of switched system: Arbitrary switching Hybrid Control and Switched Systems Lecture #11 Stability of switched system: Arbitrary switching João P. Hespanha University of California at Santa Barbara Stability under arbitrary switching Instability

More information

OPTIMAL CONTROL AND ESTIMATION

OPTIMAL CONTROL AND ESTIMATION OPTIMAL CONTROL AND ESTIMATION Robert F. Stengel Department of Mechanical and Aerospace Engineering Princeton University, Princeton, New Jersey DOVER PUBLICATIONS, INC. New York CONTENTS 1. INTRODUCTION

More information

AN OVERVIEW OF MODEL REDUCTION TECHNIQUES APPLIED TO LARGE-SCALE STRUCTURAL DYNAMICS AND CONTROL MOTIVATING EXAMPLE INVERTED PENDULUM

AN OVERVIEW OF MODEL REDUCTION TECHNIQUES APPLIED TO LARGE-SCALE STRUCTURAL DYNAMICS AND CONTROL MOTIVATING EXAMPLE INVERTED PENDULUM Controls Lab AN OVERVIEW OF MODEL REDUCTION TECHNIQUES APPLIED TO LARGE-SCALE STRUCTURAL DYNAMICS AND CONTROL Eduardo Gildin (UT ICES and Rice Univ.) with Thanos Antoulas (Rice ECE) Danny Sorensen (Rice

More information

Denis ARZELIER arzelier

Denis ARZELIER   arzelier COURSE ON LMI OPTIMIZATION WITH APPLICATIONS IN CONTROL PART II.2 LMIs IN SYSTEMS CONTROL STATE-SPACE METHODS PERFORMANCE ANALYSIS and SYNTHESIS Denis ARZELIER www.laas.fr/ arzelier arzelier@laas.fr 15

More information

A Novel Integral-Based Event Triggering Control for Linear Time-Invariant Systems

A Novel Integral-Based Event Triggering Control for Linear Time-Invariant Systems 53rd IEEE Conference on Decision and Control December 15-17, 2014. Los Angeles, California, USA A Novel Integral-Based Event Triggering Control for Linear Time-Invariant Systems Seyed Hossein Mousavi 1,

More information

Converse Lyapunov theorem and Input-to-State Stability

Converse Lyapunov theorem and Input-to-State Stability Converse Lyapunov theorem and Input-to-State Stability April 6, 2014 1 Converse Lyapunov theorem In the previous lecture, we have discussed few examples of nonlinear control systems and stability concepts

More information

João P. Hespanha. January 16, 2009

João P. Hespanha. January 16, 2009 LINEAR SYSTEMS THEORY João P. Hespanha January 16, 2009 Disclaimer: This is a draft and probably contains a few typos. Comments and information about typos are welcome. Please contact the author at hespanha@ece.ucsb.edu.

More information

QUANTIZED SYSTEMS AND CONTROL. Daniel Liberzon. DISC HS, June Dept. of Electrical & Computer Eng., Univ. of Illinois at Urbana-Champaign

QUANTIZED SYSTEMS AND CONTROL. Daniel Liberzon. DISC HS, June Dept. of Electrical & Computer Eng., Univ. of Illinois at Urbana-Champaign QUANTIZED SYSTEMS AND CONTROL Daniel Liberzon Coordinated Science Laboratory and Dept. of Electrical & Computer Eng., Univ. of Illinois at Urbana-Champaign DISC HS, June 2003 HYBRID CONTROL Plant: u y

More information

Nonlinear Control Systems

Nonlinear Control Systems Nonlinear Control Systems António Pedro Aguiar pedro@isr.ist.utl.pt 5. Input-Output Stability DEEC PhD Course http://users.isr.ist.utl.pt/%7epedro/ncs2012/ 2012 1 Input-Output Stability y = Hu H denotes

More information

Lecture Note 5: Semidefinite Programming for Stability Analysis

Lecture Note 5: Semidefinite Programming for Stability Analysis ECE7850: Hybrid Systems:Theory and Applications Lecture Note 5: Semidefinite Programming for Stability Analysis Wei Zhang Assistant Professor Department of Electrical and Computer Engineering Ohio State

More information

Semidefinite Programming Duality and Linear Time-invariant Systems

Semidefinite Programming Duality and Linear Time-invariant Systems Semidefinite Programming Duality and Linear Time-invariant Systems Venkataramanan (Ragu) Balakrishnan School of ECE, Purdue University 2 July 2004 Workshop on Linear Matrix Inequalities in Control LAAS-CNRS,

More information

ECE7850 Lecture 8. Nonlinear Model Predictive Control: Theoretical Aspects

ECE7850 Lecture 8. Nonlinear Model Predictive Control: Theoretical Aspects ECE7850 Lecture 8 Nonlinear Model Predictive Control: Theoretical Aspects Model Predictive control (MPC) is a powerful control design method for constrained dynamical systems. The basic principles and

More information

Video 6.1 Vijay Kumar and Ani Hsieh

Video 6.1 Vijay Kumar and Ani Hsieh Video 6.1 Vijay Kumar and Ani Hsieh Robo3x-1.6 1 In General Disturbance Input + - Input Controller + + System Output Robo3x-1.6 2 Learning Objectives for this Week State Space Notation Modeling in the

More information

Introduction to Nonlinear Control Lecture # 4 Passivity

Introduction to Nonlinear Control Lecture # 4 Passivity p. 1/6 Introduction to Nonlinear Control Lecture # 4 Passivity È p. 2/6 Memoryless Functions ¹ y È Ý Ù È È È È u (b) µ power inflow = uy Resistor is passive if uy 0 p. 3/6 y y y u u u (a) (b) (c) Passive

More information

Can Thermodynamics Be Used to Design Control Systems?

Can Thermodynamics Be Used to Design Control Systems? 211 American Control Conference on O'Farrell Street, San Francisco, CA, USA June 29 - July 1, 211 Can Thermodynamics Be Used to Design Control Systems? Qing Hui Abstract Thermodynamics is a physical branch

More information

Chapter One. Introduction

Chapter One. Introduction Chapter One Introduction With the ever-increasing influence of mathematical modeling and engineering on biological, social, and medical sciences, it is not surprising that dynamical system theory has played

More information

arxiv: v1 [cs.sy] 20 Nov 2017

arxiv: v1 [cs.sy] 20 Nov 2017 DISSIPATIVITY OF SYSTEM ABSTRACTIONS OBTAINED USING APPROXIMATE INPUT-OUTPUT SIMULATION ETIKA AGARWAL, SHRAVAN SAJJA, PANOS J. ANTSAKLIS, AND VIJAY GUPTA arxiv:1711.07529v1 [cs.sy] 20 Nov 2017 Abstract.

More information

Dissipative Systems Analysis and Control, Theory and Applications: Addendum/Erratum

Dissipative Systems Analysis and Control, Theory and Applications: Addendum/Erratum Dissipative Systems Analysis and Control, Theory and Applications: Addendum/Erratum Bernard Brogliato To cite this version: Bernard Brogliato. Dissipative Systems Analysis and Control, Theory and Applications:

More information

Matrix Mathematics. Theory, Facts, and Formulas with Application to Linear Systems Theory. Dennis S. Bernstein

Matrix Mathematics. Theory, Facts, and Formulas with Application to Linear Systems Theory. Dennis S. Bernstein Matrix Mathematics Theory, Facts, and Formulas with Application to Linear Systems Theory Dennis S. Bernstein PRINCETON UNIVERSITY PRESS PRINCETON AND OXFORD Contents Special Symbols xv Conventions, Notation,

More information

Fast Algorithms for SDPs derived from the Kalman-Yakubovich-Popov Lemma

Fast Algorithms for SDPs derived from the Kalman-Yakubovich-Popov Lemma Fast Algorithms for SDPs derived from the Kalman-Yakubovich-Popov Lemma Venkataramanan (Ragu) Balakrishnan School of ECE, Purdue University 8 September 2003 European Union RTN Summer School on Multi-Agent

More information

L 2 -induced Gains of Switched Systems and Classes of Switching Signals

L 2 -induced Gains of Switched Systems and Classes of Switching Signals L 2 -induced Gains of Switched Systems and Classes of Switching Signals Kenji Hirata and João P. Hespanha Abstract This paper addresses the L 2-induced gain analysis for switched linear systems. We exploit

More information

Applied Nonlinear Control

Applied Nonlinear Control Applied Nonlinear Control JEAN-JACQUES E. SLOTINE Massachusetts Institute of Technology WEIPING LI Massachusetts Institute of Technology Pearson Education Prentice Hall International Inc. Upper Saddle

More information

An Alternative Proof of Primitivity of Indecomposable Nonnegative Matrices with a Positive Trace

An Alternative Proof of Primitivity of Indecomposable Nonnegative Matrices with a Positive Trace An Alternative Proof of Primitivity of Indecomposable Nonnegative Matrices with a Positive Trace Takao Fujimoto Abstract. This research memorandum is aimed at presenting an alternative proof to a well

More information

Chap. 3. Controlled Systems, Controllability

Chap. 3. Controlled Systems, Controllability Chap. 3. Controlled Systems, Controllability 1. Controllability of Linear Systems 1.1. Kalman s Criterion Consider the linear system ẋ = Ax + Bu where x R n : state vector and u R m : input vector. A :

More information

An Input-Output Approach to Structured Stochastic Uncertainty

An Input-Output Approach to Structured Stochastic Uncertainty 1 An Input-Output Approach to Structured Stochastic Uncertainty Bassam Bamieh, Fellow, IEEE, and Maurice Filo, Member, IEEE arxiv:1806.07473v1 [cs.sy] 19 Jun 2018 Abstract We consider linear time invariant

More information

Controller synthesis for positive systems under l 1-induced performance

Controller synthesis for positive systems under l 1-induced performance Title Controller synthesis for positive systems under l 1-induced performance Author(s) Chen, X; Lam, J; Li, P; Shu, Z Citation The 24th Chinese Control and Decision Conference (CCDC 212), Taiyuan, China,

More information

Book review for Stability and Control of Dynamical Systems with Applications: A tribute to Anthony M. Michel

Book review for Stability and Control of Dynamical Systems with Applications: A tribute to Anthony M. Michel To appear in International Journal of Hybrid Systems c 2004 Nonpareil Publishers Book review for Stability and Control of Dynamical Systems with Applications: A tribute to Anthony M. Michel João Hespanha

More information

Lyapunov stability ORDINARY DIFFERENTIAL EQUATIONS

Lyapunov stability ORDINARY DIFFERENTIAL EQUATIONS Lyapunov stability ORDINARY DIFFERENTIAL EQUATIONS An ordinary differential equation is a mathematical model of a continuous state continuous time system: X = < n state space f: < n! < n vector field (assigns

More information

arxiv: v3 [math.oc] 1 Sep 2018

arxiv: v3 [math.oc] 1 Sep 2018 arxiv:177.148v3 [math.oc] 1 Sep 218 The converse of the passivity and small-gain theorems for input-output maps Sei Zhen Khong, Arjan van der Schaft Version: June 25, 218; accepted for publication in Automatica

More information

EN Nonlinear Control and Planning in Robotics Lecture 3: Stability February 4, 2015

EN Nonlinear Control and Planning in Robotics Lecture 3: Stability February 4, 2015 EN530.678 Nonlinear Control and Planning in Robotics Lecture 3: Stability February 4, 2015 Prof: Marin Kobilarov 0.1 Model prerequisites Consider ẋ = f(t, x). We will make the following basic assumptions

More information

Robust and Optimal Control, Spring 2015

Robust and Optimal Control, Spring 2015 Robust and Optimal Control, Spring 2015 Instructor: Prof. Masayuki Fujita (S5-303B) D. Linear Matrix Inequality D.1 Convex Optimization D.2 Linear Matrix Inequality(LMI) D.3 Control Design and LMI Formulation

More information

Nonlinear Control. Nonlinear Control Lecture # 6 Passivity and Input-Output Stability

Nonlinear Control. Nonlinear Control Lecture # 6 Passivity and Input-Output Stability Nonlinear Control Lecture # 6 Passivity and Input-Output Stability Passivity: Memoryless Functions y y y u u u (a) (b) (c) Passive Passive Not passive y = h(t,u), h [0, ] Vector case: y = h(t,u), h T =

More information

Hybrid Systems Course Lyapunov stability

Hybrid Systems Course Lyapunov stability Hybrid Systems Course Lyapunov stability OUTLINE Focus: stability of an equilibrium point continuous systems decribed by ordinary differential equations (brief review) hybrid automata OUTLINE Focus: stability

More information

Disturbance Attenuation for a Class of Nonlinear Systems by Output Feedback

Disturbance Attenuation for a Class of Nonlinear Systems by Output Feedback Disturbance Attenuation for a Class of Nonlinear Systems by Output Feedback Wei in Chunjiang Qian and Xianqing Huang Submitted to Systems & Control etters /5/ Abstract This paper studies the problem of

More information

On the Equivalence Between Dissipativity and Optimality of Discontinuous Nonlinear Regulators for Filippov Dynamical Systems

On the Equivalence Between Dissipativity and Optimality of Discontinuous Nonlinear Regulators for Filippov Dynamical Systems IEEE TRANSACTIONS ON AUTOMATIC CONTROL VOL 59 NO 2 FEBRUARY 2014 423 On the Equivalence Between Dissipativity and Optimality of Discontinuous Nonlinear Regulators for Filippov Dynamical Systems Teymur

More information

The Important State Coordinates of a Nonlinear System

The Important State Coordinates of a Nonlinear System The Important State Coordinates of a Nonlinear System Arthur J. Krener 1 University of California, Davis, CA and Naval Postgraduate School, Monterey, CA ajkrener@ucdavis.edu Summary. We offer an alternative

More information

Graph and Controller Design for Disturbance Attenuation in Consensus Networks

Graph and Controller Design for Disturbance Attenuation in Consensus Networks 203 3th International Conference on Control, Automation and Systems (ICCAS 203) Oct. 20-23, 203 in Kimdaejung Convention Center, Gwangju, Korea Graph and Controller Design for Disturbance Attenuation in

More information

Solving Linear Systems

Solving Linear Systems Solving Linear Systems Iterative Solutions Methods Philippe B. Laval KSU Fall 207 Philippe B. Laval (KSU) Linear Systems Fall 207 / 2 Introduction We continue looking how to solve linear systems of the

More information

QUATERNION FEEDBACK ATTITUDE CONTROL DESIGN: A NONLINEAR H APPROACH

QUATERNION FEEDBACK ATTITUDE CONTROL DESIGN: A NONLINEAR H APPROACH Asian Journal of Control, Vol. 5, No. 3, pp. 406-4, September 003 406 Brief Paper QUAERNION FEEDBACK AIUDE CONROL DESIGN: A NONLINEAR H APPROACH Long-Life Show, Jyh-Ching Juang, Ying-Wen Jan, and Chen-zung

More information

Chapter 7 Interconnected Systems and Feedback: Well-Posedness, Stability, and Performance 7. Introduction Feedback control is a powerful approach to o

Chapter 7 Interconnected Systems and Feedback: Well-Posedness, Stability, and Performance 7. Introduction Feedback control is a powerful approach to o Lectures on Dynamic Systems and Control Mohammed Dahleh Munther A. Dahleh George Verghese Department of Electrical Engineering and Computer Science Massachuasetts Institute of Technology c Chapter 7 Interconnected

More information

Observer-based quantized output feedback control of nonlinear systems

Observer-based quantized output feedback control of nonlinear systems Proceedings of the 17th World Congress The International Federation of Automatic Control Observer-based quantized output feedback control of nonlinear systems Daniel Liberzon Coordinated Science Laboratory,

More information

Chapter One. Introduction

Chapter One. Introduction Chapter One Introduction A system is a combination of components or parts that is perceived as a single entity. The parts making up the system may be clearly or vaguely defined. These parts are related

More information

IN THIS paper, we study the problem of asymptotic stabilization

IN THIS paper, we study the problem of asymptotic stabilization IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL 49, NO 11, NOVEMBER 2004 1975 Nonlinear Control of Feedforward Systems With Bounded Signals Georgia Kaliora and Alessandro Astolfi Abstract The stabilization

More information

Research Article A Symbolic Computation Approach to Parameterizing Controller for Polynomial Hamiltonian Systems

Research Article A Symbolic Computation Approach to Parameterizing Controller for Polynomial Hamiltonian Systems Mathematical Problems in Engineering, Article ID 86428, 8 pages http://dx.doi.org/1.1155/214/86428 Research Article A Symbolic Computation Approach to Parameterizing Controller for Polynomial Hamiltonian

More information

Postface to Model Predictive Control: Theory and Design

Postface to Model Predictive Control: Theory and Design Postface to Model Predictive Control: Theory and Design J. B. Rawlings and D. Q. Mayne August 19, 2012 The goal of this postface is to point out and comment upon recent MPC papers and issues pertaining

More information

Output Input Stability and Minimum-Phase Nonlinear Systems

Output Input Stability and Minimum-Phase Nonlinear Systems 422 IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 47, NO. 3, MARCH 2002 Output Input Stability and Minimum-Phase Nonlinear Systems Daniel Liberzon, Member, IEEE, A. Stephen Morse, Fellow, IEEE, and Eduardo

More information

Input to state Stability

Input to state Stability Input to state Stability Mini course, Universität Stuttgart, November 2004 Lars Grüne, Mathematisches Institut, Universität Bayreuth Part III: Lyapunov functions and quantitative aspects ISS Consider with

More information

Linear Matrix Inequalities in Robust Control. Venkataramanan (Ragu) Balakrishnan School of ECE, Purdue University MTNS 2002

Linear Matrix Inequalities in Robust Control. Venkataramanan (Ragu) Balakrishnan School of ECE, Purdue University MTNS 2002 Linear Matrix Inequalities in Robust Control Venkataramanan (Ragu) Balakrishnan School of ECE, Purdue University MTNS 2002 Objective A brief introduction to LMI techniques for Robust Control Emphasis on

More information

THE INVERSE FUNCTION THEOREM

THE INVERSE FUNCTION THEOREM THE INVERSE FUNCTION THEOREM W. PATRICK HOOPER The implicit function theorem is the following result: Theorem 1. Let f be a C 1 function from a neighborhood of a point a R n into R n. Suppose A = Df(a)

More information

Quadratic Stability of Dynamical Systems. Raktim Bhattacharya Aerospace Engineering, Texas A&M University

Quadratic Stability of Dynamical Systems. Raktim Bhattacharya Aerospace Engineering, Texas A&M University .. Quadratic Stability of Dynamical Systems Raktim Bhattacharya Aerospace Engineering, Texas A&M University Quadratic Lyapunov Functions Quadratic Stability Dynamical system is quadratically stable if

More information

Lyapunov Stability Theory

Lyapunov Stability Theory Lyapunov Stability Theory Peter Al Hokayem and Eduardo Gallestey March 16, 2015 1 Introduction In this lecture we consider the stability of equilibrium points of autonomous nonlinear systems, both in continuous

More information

MCE/EEC 647/747: Robot Dynamics and Control. Lecture 12: Multivariable Control of Robotic Manipulators Part II

MCE/EEC 647/747: Robot Dynamics and Control. Lecture 12: Multivariable Control of Robotic Manipulators Part II MCE/EEC 647/747: Robot Dynamics and Control Lecture 12: Multivariable Control of Robotic Manipulators Part II Reading: SHV Ch.8 Mechanical Engineering Hanz Richter, PhD MCE647 p.1/14 Robust vs. Adaptive

More information

Video 8.1 Vijay Kumar. Property of University of Pennsylvania, Vijay Kumar

Video 8.1 Vijay Kumar. Property of University of Pennsylvania, Vijay Kumar Video 8.1 Vijay Kumar 1 Definitions State State equations Equilibrium 2 Stability Stable Unstable Neutrally (Critically) Stable 3 Stability Translate the origin to x e x(t) =0 is stable (Lyapunov stable)

More information

Robust Observer for Uncertain T S model of a Synchronous Machine

Robust Observer for Uncertain T S model of a Synchronous Machine Recent Advances in Circuits Communications Signal Processing Robust Observer for Uncertain T S model of a Synchronous Machine OUAALINE Najat ELALAMI Noureddine Laboratory of Automation Computer Engineering

More information

3. Fundamentals of Lyapunov Theory

3. Fundamentals of Lyapunov Theory Applied Nonlinear Control Nguyen an ien -.. Fundamentals of Lyapunov heory he objective of this chapter is to present Lyapunov stability theorem and illustrate its use in the analysis and the design of

More information

Multi-objective Controller Design:

Multi-objective Controller Design: Multi-objective Controller Design: Evolutionary algorithms and Bilinear Matrix Inequalities for a passive suspension A. Molina-Cristobal*, C. Papageorgiou**, G. T. Parks*, M. C. Smith**, P. J. Clarkson*

More information

Control of Mobile Robots

Control of Mobile Robots Control of Mobile Robots Regulation and trajectory tracking Prof. Luca Bascetta (luca.bascetta@polimi.it) Politecnico di Milano Dipartimento di Elettronica, Informazione e Bioingegneria Organization and

More information

Stabilization of a 3D Rigid Pendulum

Stabilization of a 3D Rigid Pendulum 25 American Control Conference June 8-, 25. Portland, OR, USA ThC5.6 Stabilization of a 3D Rigid Pendulum Nalin A. Chaturvedi, Fabio Bacconi, Amit K. Sanyal, Dennis Bernstein, N. Harris McClamroch Department

More information

NONLINEAR AND ADAPTIVE (INTELLIGENT) SYSTEMS MODELING, DESIGN, & CONTROL A Building Block Approach

NONLINEAR AND ADAPTIVE (INTELLIGENT) SYSTEMS MODELING, DESIGN, & CONTROL A Building Block Approach NONLINEAR AND ADAPTIVE (INTELLIGENT) SYSTEMS MODELING, DESIGN, & CONTROL A Building Block Approach P.A. (Rama) Ramamoorthy Electrical & Computer Engineering and Comp. Science Dept., M.L. 30, University

More information

A State-Space Approach to Control of Interconnected Systems

A State-Space Approach to Control of Interconnected Systems A State-Space Approach to Control of Interconnected Systems Part II: General Interconnections Cédric Langbort Center for the Mathematics of Information CALIFORNIA INSTITUTE OF TECHNOLOGY clangbort@ist.caltech.edu

More information

6.241 Dynamic Systems and Control

6.241 Dynamic Systems and Control 6.241 Dynamic Systems and Control Lecture 24: H2 Synthesis Emilio Frazzoli Aeronautics and Astronautics Massachusetts Institute of Technology May 4, 2011 E. Frazzoli (MIT) Lecture 24: H 2 Synthesis May

More information

IN many practical systems, there is such a kind of systems

IN many practical systems, there is such a kind of systems L 1 -induced Performance Analysis and Sparse Controller Synthesis for Interval Positive Systems Xiaoming Chen, James Lam, Ping Li, and Zhan Shu Abstract This paper is concerned with the design of L 1 -

More information

Small Gain Theorems on Input-to-Output Stability

Small Gain Theorems on Input-to-Output Stability Small Gain Theorems on Input-to-Output Stability Zhong-Ping Jiang Yuan Wang. Dept. of Electrical & Computer Engineering Polytechnic University Brooklyn, NY 11201, U.S.A. zjiang@control.poly.edu Dept. of

More information

On the PDEs arising in IDA-PBC

On the PDEs arising in IDA-PBC On the PDEs arising in IDA-PBC JÁ Acosta and A Astolfi Abstract The main stumbling block of most nonlinear control methods is the necessity to solve nonlinear Partial Differential Equations In this paper

More information

CONTROLLABILITY OF QUANTUM SYSTEMS. Sonia G. Schirmer

CONTROLLABILITY OF QUANTUM SYSTEMS. Sonia G. Schirmer CONTROLLABILITY OF QUANTUM SYSTEMS Sonia G. Schirmer Dept of Applied Mathematics + Theoretical Physics and Dept of Engineering, University of Cambridge, Cambridge, CB2 1PZ, United Kingdom Ivan C. H. Pullen

More information

An introduction to Mathematical Theory of Control

An introduction to Mathematical Theory of Control An introduction to Mathematical Theory of Control Vasile Staicu University of Aveiro UNICA, May 2018 Vasile Staicu (University of Aveiro) An introduction to Mathematical Theory of Control UNICA, May 2018

More information

Systems. Active Vibration Isolation of Multi-Degree-of-Freedom

Systems. Active Vibration Isolation of Multi-Degree-of-Freedom Active Vibration Isolation of Multi-Degree-of-Freedom Systems WASSIM M. HADDAD School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0150 USA ALI RAZAVI George W Woodruff

More information

Nonlinear System Analysis

Nonlinear System Analysis Nonlinear System Analysis Lyapunov Based Approach Lecture 4 Module 1 Dr. Laxmidhar Behera Department of Electrical Engineering, Indian Institute of Technology, Kanpur. January 4, 2003 Intelligent Control

More information

Module 6 : Solving Ordinary Differential Equations - Initial Value Problems (ODE-IVPs) Section 1 : Introduction

Module 6 : Solving Ordinary Differential Equations - Initial Value Problems (ODE-IVPs) Section 1 : Introduction Module 6 : Solving Ordinary Differential Equations - Initial Value Problems (ODE-IVPs) Section 1 : Introduction 1 Introduction In this module, we develop solution techniques for numerically solving ordinary

More information

A Characterization of the Hurwitz Stability of Metzler Matrices

A Characterization of the Hurwitz Stability of Metzler Matrices 29 American Control Conference Hyatt Regency Riverfront, St Louis, MO, USA June -2, 29 WeC52 A Characterization of the Hurwitz Stability of Metzler Matrices Kumpati S Narendra and Robert Shorten 2 Abstract

More information

Dynamical Systems & Lyapunov Stability

Dynamical Systems & Lyapunov Stability Dynamical Systems & Lyapunov Stability Harry G. Kwatny Department of Mechanical Engineering & Mechanics Drexel University Outline Ordinary Differential Equations Existence & uniqueness Continuous dependence

More information

An Introduction to Linear Matrix Inequalities. Raktim Bhattacharya Aerospace Engineering, Texas A&M University

An Introduction to Linear Matrix Inequalities. Raktim Bhattacharya Aerospace Engineering, Texas A&M University An Introduction to Linear Matrix Inequalities Raktim Bhattacharya Aerospace Engineering, Texas A&M University Linear Matrix Inequalities What are they? Inequalities involving matrix variables Matrix variables

More information

Control Systems Theory and Applications for Linear Repetitive Processes

Control Systems Theory and Applications for Linear Repetitive Processes Eric Rogers, Krzysztof Galkowski, David H. Owens Control Systems Theory and Applications for Linear Repetitive Processes Springer Contents 1 Examples and Representations 1 1.1 Examples and Control Problems

More information

Piecewise Linear Quadratic Optimal Control

Piecewise Linear Quadratic Optimal Control IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 45, NO. 4, APRIL 2000 629 Piecewise Linear Quadratic Optimal Control Anders Rantzer and Mikael Johansson Abstract The use of piecewise quadratic cost functions

More information