Nonlinear Control Lecture # 14 Input-Output Stability. Nonlinear Control
|
|
- Kelly Holt
- 5 years ago
- Views:
Transcription
1 Nonlinear Control Lecture # 14 Input-Output Stability
2 L Stability Input-Output Models: y = Hu u(t) is a piecewise continuous function of t and belongs to a linear space of signals The space of bounded functions: sup t u(t) < The space of square-integrable functions: u T (t)u(t) dt < Norm of a signal u : u and u = u = au = a u for any a > Triangle Inequality: u 1 +u 2 u 1 + u 2
3 L p spaces: L : u L = sup u(t) < t L 2 : u L2 = u T (t)u(t) dt < ( 1/p L p : u Lp = u(t) dt) p <, 1 p < Notation L m p : p is the type of p-norm used to define the space and m is the dimension of u
4 Extended Space: L e = {u u τ L, { τ [, )} u(t), t τ u τ is a truncation of u: u τ (t) =, t > τ L e is a linear space and L L e Example u(t) = t, u τ (t) = { t, t τ, t > τ u / L but u τ L e
5 Causality: A mapping H : L m e Lq e is causal if the value of the output (Hu)(t) at any time t depends only on the values of the input up to time t (Hu) τ = (Hu τ ) τ Definition 6.1 A scalar continuous function g(r), defined for r [,a), is a gain function if it is nondecreasing and g() = A class K function is a gain function but not the other way around. By not requiring the gain function to be strictly increasing we can have g = or g(r) = sat(r)
6 Definition 6.2 A mapping H : L m e L q e is L stable if there exist a gain function g, defined on [, ), and a nonnegative constant β such that (Hu) τ L g( u τ L )+β, u L m e and τ [, ) It is finite-gain L stable if there exist nonnegative constants γ and β such that (Hu) τ L γ u τ L +β, u L m e and τ [, ) In this case, we say that the system has L gain γ. The bias term β is included in the definition to allow for systems where Hu does not vanish at u =.
7 Example 6.1: Memoryless function y = h(u) Suppose h(u) a+b u, u R Finite-gain L stable with β = a and γ = b If a =, then for each p [1, ) h(u(t)) p dt (b) p u(t) p dt Finite-gain L p stable with β = and γ = b For h(u) = u 2, H is L stable with zero bias and g(r) = r 2. It is not finite-gain L stable because h(u) = u 2 cannot be bounded γ u +β for all u R
8 Example 6.2: SISO causal convolution operator y(t) = t h(t σ)u(σ) dσ, h(t) = for t < Suppose h L 1 h L1 = h(σ) dσ < y(t) t h(t σ) u(σ) dσ t h(t σ) dσsup σ τ u(σ) = t h(s) dssup σ τ u(σ) y τ L h L1 u τ L, τ [, ) Finite-gain L stable Also, finite-gain L p stable for p [1, ) (see textbook)
9 Small-signal L Stability Example 6.3 y = tanu The output y(t) is defined only when the input signal is restricted to u(t) < π/2 for all t ( ) tanr u(t) { u r < π/2} y u r ( ) tanr y Lp u Lp, p [1, ] r
10 Definition 6.3 A mapping H : L m e Lq e is small-signal L stable (respectively, small-signal finite-gain L stable) if there is a positive constant r such that the condition for L stability ( respectively, finite-gain L stability ) is satisfied for all u L m e with sup t τ u(t) r
11 L Stability of State Models ẋ = f(x,u), y = h(x,u), = f(,), = h(,) Case 1: The origin of ẋ = f(x,) is exponentially stable Theorem 6.1 Suppose, x r, u r u, c 1 x 2 V(x) c 2 x 2 V x f(x,) c 3 x 2, V x c 4 x f(x,u) f(x,) L u, h(x,u) η 1 x +η 2 u Then, for each x with x r c 1 /c 2, the system is small-signal finite-gain L p stable for each p [1, ]. It is finite-gain L p stable x R n if the assumptions hold globally [see the textbook for β and γ]
12 Proof V = V V f(x,)+ x x [f(x,u) f(x,)] V c 3 x 2 +c 4 L x u c 3 V + c 4L u V c 2 c1 W(x) = V(x) Ẇ aw +b u(t), a = c 3 2c 2, b = c 4L 2 c 1 U(t) = e at W(x(t)) U = e at Ẇ +ae at W be at u U(t) U()+ t be aτ u(τ) dτ
13 W(x(t)) e at W(x())+ x(t) t c1 x W(x) c 2 x c2 c 1 x() e at + c 4L 2c 1 e a(t τ) b u(τ) dτ t y(t) η 1 x(t) +η 2 u(t) e at u(τ) dτ t y(t) k x() e at +k 2 e a(t τ) u(τ) dτ +k 3 u(t)
14 Example 6.4 ẋ = x x 3 +u, y = tanhx+u V = 1 2 x2 V = x( x x 3 ) x 2 c 1 = c 2 = 1 2, c 3 = c 4 = 1, L = η 1 = η 2 = 1 Finite-gain L p stable for each x() R and each p [1, ] Example 6.5 ẋ 1 = x 2, ẋ 2 = x 1 x 2 atanhx 1 +u, y = x 1, a V(x) = x T Px = p 11 x p 12 x 1 x 2 +p 22 x 2 2
15 V = 2p 12 (x 2 1 +ax 1tanhx 1 )+2(p 11 p 12 p 22 )x 1 x 2 2ap 22 x 2 tanhx 1 2(p 22 p 12 )x 2 2 p 11 = p 12 +p 22 the term x 1 x 2 is canceled p 22 = 2p 12 = 1 P is positive definite V = x 2 1 x2 2 ax 1tanhx 1 2ax 2 tanhx 1 V x 2 +2a x 1 x 2 (1 a) x 2 a < 1 c 1 = λ min (P),c 2 = λ max (P),c 3 = 1 a,c 4 = 2c 2 L = η 1 = 1, η 2 = For each x() R 2, p [1, ], the system is finite-gain L p stable γ = 2[λ max (P)] 2 /[(1 a)λ min (P)]
16 Case 2: The origin of ẋ = f(x,) is asymptotically stable Theorem 6.2 Suppose that, for all (x,u), f is locally Lipschitz and h is continuous and satisfies h(x,u) g 1 ( x )+g 2 ( u )+η, η for some gain functions g 1, g 2. If ẋ = f(x,u) is ISS, then, for each x() R n, the system is L stable ẋ = f(x,u), y = h(x,u)
17 Proof { ( )} x(t) max β( x,t),γ sup u(t) t τ ( { y(t) g 1 max β( x,t),γ ( sup t τ u(t) )}) +g 2 ( u(t) )+η g 1 (max{a,b}) g 1 (a)+g 1 (b) g = g 1 γ +g 2 y τ L g( u τ L )+β and β = g 1 (β( x,))+η
18 Theorem 6.3 Suppose f is locally Lipschitz and h is continuous in some neighborhood of (x =, u = ). If the origin of ẋ = f(x,) is asymptotically stable, then there is a constant k 1 > such that for each x() with x() < k 1, the system is small-signal L stable Proof ẋ = f(x,u), y = h(x,u) Use Lemma 4.7 (asymptotic stability is equivalent to local ISS)
19 Example 6.6 ẋ = x 2x 3 +(1+x 2 )u 2, y = x 2 +u ISS from Example 4.13 g 1 (r) = r 2, g 2 (r) = r, η = L stable
20 Example 6.7 ẋ 1 = x 3 1 +x 2, ẋ 2 = x 1 x 3 2 +u, y = x 1 +x 2 V = (x 2 1 +x2 2 ) V = 2x 4 1 2x4 2 +2x 2u x 4 1 +x x 4 V x 4 +2 x u = (1 θ) x 4 θ x 4 +2 x u, < θ < 1 ) 1/3 (1 θ) x 4, x ISS ( 2 u θ g 1 (r) = 2r, g 2 =, η = L stable
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 informationLecture 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 informationNonlinear 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 informationNonlinear Systems and Control Lecture # 19 Perturbed Systems & Input-to-State Stability
p. 1/1 Nonlinear Systems and Control Lecture # 19 Perturbed Systems & Input-to-State Stability p. 2/1 Perturbed Systems: Nonvanishing Perturbation Nominal System: Perturbed System: ẋ = f(x), f(0) = 0 ẋ
More informationNonlinear Systems and Control Lecture # 12 Converse Lyapunov Functions & Time Varying Systems. p. 1/1
Nonlinear Systems and Control Lecture # 12 Converse Lyapunov Functions & Time Varying Systems p. 1/1 p. 2/1 Converse Lyapunov Theorem Exponential Stability Let x = 0 be an exponentially stable equilibrium
More informationLecture 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 informationIntroduction to Nonlinear Control Lecture # 3 Time-Varying and Perturbed Systems
p. 1/5 Introduction to Nonlinear Control Lecture # 3 Time-Varying and Perturbed Systems p. 2/5 Time-varying Systems ẋ = f(t, x) f(t, x) is piecewise continuous in t and locally Lipschitz in x for all t
More informationNonlinear Control. Nonlinear Control Lecture # 8 Time Varying and Perturbed Systems
Nonlinear Control Lecture # 8 Time Varying and Perturbed Systems Time-varying Systems ẋ = f(t,x) f(t,x) is piecewise continuous in t and locally Lipschitz in x for all t 0 and all x D, (0 D). The origin
More informationNonlinear Control Systems
Nonlinear Control Systems António Pedro Aguiar pedro@isr.ist.utl.pt 3. Fundamental properties IST-DEEC PhD Course http://users.isr.ist.utl.pt/%7epedro/ncs2012/ 2012 1 Example Consider the system ẋ = f
More informationHigh-Gain Observers in Nonlinear Feedback Control
High-Gain Observers in Nonlinear Feedback Control Lecture # 1 Introduction & Stabilization High-Gain ObserversinNonlinear Feedback ControlLecture # 1Introduction & Stabilization p. 1/4 Brief History Linear
More informationIntroduction 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 informationNonlinear Control. Nonlinear Control Lecture # 3 Stability of Equilibrium Points
Nonlinear Control Lecture # 3 Stability of Equilibrium Points The Invariance Principle Definitions Let x(t) be a solution of ẋ = f(x) A point p is a positive limit point of x(t) if there is a sequence
More informationNonlinear Control. Nonlinear Control Lecture # 8 Time Varying and Perturbed Systems
Nonlinear Control Lecture # 8 Time Varying and Perturbed Systems Time-varying Systems ẋ = f(t,x) f(t,x) is piecewise continuous in t and locally Lipschitz in x for all t 0 and all x D, (0 D). The origin
More informationL2 gains and system approximation quality 1
Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science 6.242, Fall 24: MODEL REDUCTION L2 gains and system approximation quality 1 This lecture discusses the utility
More informationModeling and Analysis of Dynamic Systems
Modeling and Analysis of Dynamic Systems Dr. Guillaume Ducard Fall 2017 Institute for Dynamic Systems and Control ETH Zurich, Switzerland G. Ducard c 1 / 57 Outline 1 Lecture 13: Linear System - Stability
More informationNonlinear Control. Nonlinear Control Lecture # 2 Stability of Equilibrium Points
Nonlinear Control Lecture # 2 Stability of Equilibrium Points Basic Concepts ẋ = f(x) f is locally Lipschitz over a domain D R n Suppose x D is an equilibrium point; that is, f( x) = 0 Characterize and
More informationx(t) = t[u(t 1) u(t 2)] + 1[u(t 2) u(t 3)]
ECE30 Summer II, 2006 Exam, Blue Version July 2, 2006 Name: Solution Score: 00/00 You must show all of your work for full credit. Calculators may NOT be used.. (5 points) x(t) = tu(t ) + ( t)u(t 2) u(t
More informationBIBO STABILITY AND ASYMPTOTIC STABILITY
BIBO STABILITY AND ASYMPTOTIC STABILITY FRANCESCO NORI Abstract. In this report with discuss the concepts of bounded-input boundedoutput stability (BIBO) and of Lyapunov stability. Examples are given to
More informationProf. Krstic Nonlinear Systems MAE281A Homework set 1 Linearization & phase portrait
Prof. Krstic Nonlinear Systems MAE28A Homework set Linearization & phase portrait. For each of the following systems, find all equilibrium points and determine the type of each isolated equilibrium. Use
More informationReflected Brownian Motion
Chapter 6 Reflected Brownian Motion Often we encounter Diffusions in regions with boundary. If the process can reach the boundary from the interior in finite time with positive probability we need to decide
More informationDiscrete and continuous dynamic systems
Discrete and continuous dynamic systems Bounded input bounded output (BIBO) and asymptotic stability Continuous and discrete time linear time-invariant systems Katalin Hangos University of Pannonia Faculty
More information6.241 Dynamic Systems and Control
6.241 Dynamic Systems and Control Lecture 12: I/O Stability Readings: DDV, Chapters 15, 16 Emilio Frazzoli Aeronautics and Astronautics Massachusetts Institute of Technology March 14, 2011 E. Frazzoli
More informationME Fall 2001, Fall 2002, Spring I/O Stability. Preliminaries: Vector and function norms
I/O Stability Preliminaries: Vector and function norms 1. Sup norms are used for vectors for simplicity: x = max i x i. Other norms are also okay 2. Induced matrix norms: let A R n n, (i stands for induced)
More information3 Gramians and Balanced Realizations
3 Gramians and Balanced Realizations In this lecture, we use an optimization approach to find suitable realizations for truncation and singular perturbation of G. It turns out that the recommended realizations
More informationOutput Feedback and State Feedback. EL2620 Nonlinear Control. Nonlinear Observers. Nonlinear Controllers. ẋ = f(x,u), y = h(x)
Output Feedback and State Feedback EL2620 Nonlinear Control Lecture 10 Exact feedback linearization Input-output linearization Lyapunov-based control design methods ẋ = f(x,u) y = h(x) Output feedback:
More informationConverse 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 informationEG4321/EG7040. Nonlinear Control. Dr. Matt Turner
EG4321/EG7040 Nonlinear Control Dr. Matt Turner EG4321/EG7040 [An introduction to] Nonlinear Control Dr. Matt Turner EG4321/EG7040 [An introduction to] Nonlinear [System Analysis] and Control Dr. Matt
More informationHysteresis rarefaction in the Riemann problem
Hysteresis rarefaction in the Riemann problem Pavel Krejčí 1 Institute of Mathematics, Czech Academy of Sciences, Žitná 25, 11567 Praha 1, Czech Republic E-mail: krejci@math.cas.cz Abstract. We consider
More informationIN [1], an Approximate Dynamic Inversion (ADI) control
1 On Approximate Dynamic Inversion Justin Teo and Jonathan P How Technical Report ACL09 01 Aerospace Controls Laboratory Department of Aeronautics and Astronautics Massachusetts Institute of Technology
More informationMathematics 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 information2 Classification of Continuous-Time Systems
Continuous-Time Signals and Systems 1 Preliminaries Notation for a continuous-time signal: x(t) Notation: If x is the input to a system T and y the corresponding output, then we use one of the following
More informationEE Control Systems LECTURE 9
Updated: Sunday, February, 999 EE - Control Systems LECTURE 9 Copyright FL Lewis 998 All rights reserved STABILITY OF LINEAR SYSTEMS We discuss the stability of input/output systems and of state-space
More information1.17 : Consider a continuous-time system with input x(t) and output y(t) related by y(t) = x( sin(t)).
(Note: here are the solution, only showing you the approach to solve the problems. If you find some typos or calculation error, please post it on Piazza and let us know ).7 : Consider a continuous-time
More informationECEEN 5448 Fall 2011 Homework #5 Solutions
ECEEN 5448 Fall 211 Homework #5 Solutions Professor David G. Meyer December 8, 211 1. Consider the 1-dimensional time-varying linear system ẋ t (u x) (a) Find the state-transition matrix, Φ(t, τ). Here
More informationSolution of Linear State-space Systems
Solution of Linear State-space Systems Homogeneous (u=0) LTV systems first Theorem (Peano-Baker series) The unique solution to x(t) = (t, )x 0 where The matrix function is given by is called the state
More informationRobust Multivariable Control
Lecture 2 Anders Helmersson anders.helmersson@liu.se ISY/Reglerteknik Linköpings universitet Today s topics Today s topics Norms Today s topics Norms Representation of dynamic systems Today s topics Norms
More information06/12/ rws/jMc- modif SuFY10 (MPF) - Textbook Section IX 1
IV. Continuous-Time Signals & LTI Systems [p. 3] Analog signal definition [p. 4] Periodic signal [p. 5] One-sided signal [p. 6] Finite length signal [p. 7] Impulse function [p. 9] Sampling property [p.11]
More information1 Continuity Classes C m (Ω)
0.1 Norms 0.1 Norms A norm on a linear space X is a function : X R with the properties: Positive Definite: x 0 x X (nonnegative) x = 0 x = 0 (strictly positive) λx = λ x x X, λ C(homogeneous) x + y x +
More informationLecture 10: Singular Perturbations and Averaging 1
Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science 6.243j (Fall 2003): DYNAMICS OF NONLINEAR SYSTEMS by A. Megretski Lecture 10: Singular Perturbations and
More informationRaktim Bhattacharya. . AERO 632: Design of Advance Flight Control System. Norms for Signals and Systems
. AERO 632: Design of Advance Flight Control System Norms for Signals and. Raktim Bhattacharya Laboratory For Uncertainty Quantification Aerospace Engineering, Texas A&M University. Norms for Signals ...
More informationL 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 informationCDS Solutions to the Midterm Exam
CDS 22 - Solutions to the Midterm Exam Instructor: Danielle C. Tarraf November 6, 27 Problem (a) Recall that the H norm of a transfer function is time-delay invariant. Hence: ( ) Ĝ(s) = s + a = sup /2
More informationAn asymptotic ratio characterization of input-to-state stability
1 An asymptotic ratio characterization of input-to-state stability Daniel Liberzon and Hyungbo Shim Abstract For continuous-time nonlinear systems with inputs, we introduce the notion of an asymptotic
More informationLinearization problem. The simplest example
Linear Systems Lecture 3 1 problem Consider a non-linear time-invariant system of the form ( ẋ(t f x(t u(t y(t g ( x(t u(t (1 such that x R n u R m y R p and Slide 1 A: f(xu f(xu g(xu and g(xu exist and
More informationModeling & Control of Hybrid Systems Chapter 4 Stability
Modeling & Control of Hybrid Systems Chapter 4 Stability Overview 1. Switched systems 2. Lyapunov theory for smooth and linear systems 3. Stability for any switching signal 4. Stability for given switching
More informationNonlinear Control Lecture # 1 Introduction. Nonlinear Control
Nonlinear Control Lecture # 1 Introduction Nonlinear State Model ẋ 1 = f 1 (t,x 1,...,x n,u 1,...,u m ) ẋ 2 = f 2 (t,x 1,...,x n,u 1,...,u m ).. ẋ n = f n (t,x 1,...,x n,u 1,...,u m ) ẋ i denotes the derivative
More informationFrom the Newton equation to the wave equation in some simple cases
From the ewton equation to the wave equation in some simple cases Xavier Blanc joint work with C. Le Bris (EPC) and P.-L. Lions (Collège de France) Université Paris Diderot, FRACE http://www.ann.jussieu.fr/
More informationAn 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 informationLecture 9 Nonlinear Control Design
Lecture 9 Nonlinear Control Design Exact-linearization Lyapunov-based design Lab 2 Adaptive control Sliding modes control Literature: [Khalil, ch.s 13, 14.1,14.2] and [Glad-Ljung,ch.17] Course Outline
More informationCDS 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 informationHigh-Gain Observers in Nonlinear Feedback Control. Lecture # 3 Regulation
High-Gain Observers in Nonlinear Feedback Control Lecture # 3 Regulation High-Gain ObserversinNonlinear Feedback ControlLecture # 3Regulation p. 1/5 Internal Model Principle d r Servo- Stabilizing u y
More information6.245: MULTIVARIABLE CONTROL SYSTEMS by A. Megretski. Solutions to Problem Set 1 1. Massachusetts Institute of Technology
Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science 6.245: MULTIVARIABLE CONTROL SYSTEMS by A. Megretski Solutions to Problem Set 1 1 Problem 1.1T Consider the
More informationJosé C. Geromel. Australian National University Canberra, December 7-8, 2017
5 1 15 2 25 3 35 4 45 5 1 15 2 25 3 35 4 45 5 55 Differential LMI in Optimal Sampled-data Control José C. Geromel School of Electrical and Computer Engineering University of Campinas - Brazil Australian
More informationPiecewise Smooth Solutions to the Burgers-Hilbert Equation
Piecewise Smooth Solutions to the Burgers-Hilbert Equation Alberto Bressan and Tianyou Zhang Department of Mathematics, Penn State University, University Park, Pa 68, USA e-mails: bressan@mathpsuedu, zhang
More informationCyber-Physical Systems Modeling and Simulation of Continuous Systems
Cyber-Physical Systems Modeling and Simulation of Continuous Systems Matthias Althoff TU München 29. May 2015 Matthias Althoff Modeling and Simulation of Cont. Systems 29. May 2015 1 / 38 Ordinary Differential
More informationChapter 1 Fundamental Concepts
Chapter 1 Fundamental Concepts 1 Signals A signal is a pattern of variation of a physical quantity, often as a function of time (but also space, distance, position, etc). These quantities are usually the
More informationStabilization in spite of matched unmodelled dynamics and An equivalent definition of input-to-state stability
Stabilization in spite of matched unmodelled dynamics and An equivalent definition of input-to-state stability Laurent Praly Centre Automatique et Systèmes École des Mines de Paris 35 rue St Honoré 7735
More informationEE 380. Linear Control Systems. Lecture 10
EE 380 Linear Control Systems Lecture 10 Professor Jeffrey Schiano Department of Electrical Engineering Lecture 10. 1 Lecture 10 Topics Stability Definitions Methods for Determining Stability Lecture 10.
More informationECEN 420 LINEAR CONTROL SYSTEMS. Lecture 2 Laplace Transform I 1/52
1/52 ECEN 420 LINEAR CONTROL SYSTEMS Lecture 2 Laplace Transform I Linear Time Invariant Systems A general LTI system may be described by the linear constant coefficient differential equation: a n d n
More informationNonlinear 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 informationEG4321/EG7040. Nonlinear Control. Dr. Matt Turner
EG4321/EG7040 Nonlinear Control Dr. Matt Turner EG4321/EG7040 [An introduction to] Nonlinear Control Dr. Matt Turner EG4321/EG7040 [An introduction to] Nonlinear [System Analysis] and Control Dr. Matt
More informationOne-Sided Laplace Transform and Differential Equations
One-Sided Laplace Transform and Differential Equations As in the dcrete-time case, the one-sided transform allows us to take initial conditions into account. Preliminaries The one-sided Laplace transform
More informationInput 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 informationExistence And Uniqueness Of Mild Solutions Of Second Order Volterra Integrodifferential Equations With Nonlocal Conditions
Applied Mathematics E-Notes, 9(29), 11-18 c ISSN 167-251 Available free at mirror sites of http://www.math.nthu.edu.tw/ amen/ Existence And Uniqueness Of Mild Solutions Of Second Order Volterra Integrodifferential
More informationChapter 1 Fundamental Concepts
Chapter 1 Fundamental Concepts Signals A signal is a pattern of variation of a physical quantity as a function of time, space, distance, position, temperature, pressure, etc. These quantities are usually
More informationNonlinear Control Systems
Nonlinear Control Systems António Pedro Aguiar pedro@isr.ist.utl.pt 7. Feedback Linearization IST-DEEC PhD Course http://users.isr.ist.utl.pt/%7epedro/ncs1/ 1 1 Feedback Linearization Given a nonlinear
More informationMASSACHUSETTS INSTITUTE OF TECHNOLOGY Department of Electrical Engineering and Computer Science : Dynamic Systems Spring 2011
MASSACHUSETTS INSTITUTE OF TECHNOLOGY Department of Electrical Engineering and Computer Science 6.4: Dynamic Systems Spring Homework Solutions Exercise 3. a) We are given the single input LTI system: [
More informationBalanced Truncation 1
Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science 6.242, Fall 2004: MODEL REDUCTION Balanced Truncation This lecture introduces balanced truncation for LTI
More informationNonlinear Control. Nonlinear Control Lecture # 18 Stability of Feedback Systems
Nonlinear Control Lecture # 18 Stability of Feedback Systems Absolute Stability + r = 0 u y G(s) ψ( ) Definition 7.1 The system is absolutely stable if the origin is globally uniformly asymptotically stable
More informationDifferential and Difference LTI systems
Signals and Systems Lecture: 6 Differential and Difference LTI systems Differential and difference linear time-invariant (LTI) systems constitute an extremely important class of systems in engineering.
More informationLinear System Theory
Linear System Theory Wonhee Kim Chapter 6: Controllability & Observability Chapter 7: Minimal Realizations May 2, 217 1 / 31 Recap State space equation Linear Algebra Solutions of LTI and LTV system Stability
More informationNonlinear stabilization via a linear observability
via a linear observability Kaïs Ammari Department of Mathematics University of Monastir Joint work with Fathia Alabau-Boussouira Collocated feedback stabilization Outline 1 Introduction and main result
More informationConvex Functions. Daniel P. Palomar. Hong Kong University of Science and Technology (HKUST)
Convex Functions Daniel P. Palomar Hong Kong University of Science and Technology (HKUST) ELEC5470 - Convex Optimization Fall 2017-18, HKUST, Hong Kong Outline of Lecture Definition convex function Examples
More information7.1 Linear Systems Stability Consider the Continuous-Time (CT) Linear Time-Invariant (LTI) system
7 Stability 7.1 Linear Systems Stability Consider the Continuous-Time (CT) Linear Time-Invariant (LTI) system ẋ(t) = A x(t), x(0) = x 0, A R n n, x 0 R n. (14) The origin x = 0 is a globally asymptotically
More informationGramians based model reduction for hybrid switched systems
Gramians based model reduction for hybrid switched systems Y. Chahlaoui Younes.Chahlaoui@manchester.ac.uk Centre for Interdisciplinary Computational and Dynamical Analysis (CICADA) School of Mathematics
More informationNonlinear 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 informationIntroduction. Performance and Robustness (Chapter 1) Advanced Control Systems Spring / 31
Introduction Classical Control Robust Control u(t) y(t) G u(t) G + y(t) G : nominal model G = G + : plant uncertainty Uncertainty sources : Structured : parametric uncertainty, multimodel uncertainty Unstructured
More informationECE 301 Division 1 Exam 1 Solutions, 10/6/2011, 8-9:45pm in ME 1061.
ECE 301 Division 1 Exam 1 Solutions, 10/6/011, 8-9:45pm in ME 1061. Your ID will be checked during the exam. Please bring a No. pencil to fill out the answer sheet. This is a closed-book exam. No calculators
More informationOn finite time BV blow-up for the p-system
On finite time BV blow-up for the p-system Alberto Bressan ( ), Geng Chen ( ), and Qingtian Zhang ( ) (*) Department of Mathematics, Penn State University, (**) Department of Mathematics, University of
More informationPDEs, Homework #3 Solutions. 1. Use Hölder s inequality to show that the solution of the heat equation
PDEs, Homework #3 Solutions. Use Hölder s inequality to show that the solution of the heat equation u t = ku xx, u(x, = φ(x (HE goes to zero as t, if φ is continuous and bounded with φ L p for some p.
More informationLecture 9 Time-domain properties of convolution systems
EE 12 spring 21-22 Handout #18 Lecture 9 Time-domain properties of convolution systems impulse response step response fading memory DC gain peak gain stability 9 1 Impulse response if u = δ we have y(t)
More informationUnconstrained optimization
Chapter 4 Unconstrained optimization An unconstrained optimization problem takes the form min x Rnf(x) (4.1) for a target functional (also called objective function) f : R n R. In this chapter and throughout
More informationChapter 3 Convolution Representation
Chapter 3 Convolution Representation DT Unit-Impulse Response Consider the DT SISO system: xn [ ] System yn [ ] xn [ ] = δ[ n] If the input signal is and the system has no energy at n = 0, the output yn
More informationStability of optimization problems with stochastic dominance constraints
Stability of optimization problems with stochastic dominance constraints D. Dentcheva and W. Römisch Stevens Institute of Technology, Hoboken Humboldt-University Berlin www.math.hu-berlin.de/~romisch SIAM
More informationLinear dynamical systems with inputs & outputs
EE263 Autumn 215 S. Boyd and S. Lall Linear dynamical systems with inputs & outputs inputs & outputs: interpretations transfer function impulse and step responses examples 1 Inputs & outputs recall continuous-time
More informationLMI Methods in Optimal and Robust Control
LMI Methods in Optimal and Robust Control Matthew M. Peet Arizona State University Lecture 15: Nonlinear Systems and Lyapunov Functions Overview Our next goal is to extend LMI s and optimization to nonlinear
More informationLecture 8 Plus properties, merit functions and gap functions. September 28, 2008
Lecture 8 Plus properties, merit functions and gap functions September 28, 2008 Outline Plus-properties and F-uniqueness Equation reformulations of VI/CPs Merit functions Gap merit functions FP-I book:
More informationarxiv: v3 [math.ds] 22 Feb 2012
Stability of interconnected impulsive systems with and without time-delays using Lyapunov methods arxiv:1011.2865v3 [math.ds] 22 Feb 2012 Sergey Dashkovskiy a, Michael Kosmykov b, Andrii Mironchenko b,
More informationLTI Systems (Continuous & Discrete) - Basics
LTI Systems (Continuous & Discrete) - Basics 1. A system with an input x(t) and output y(t) is described by the relation: y(t) = t. x(t). This system is (a) linear and time-invariant (b) linear and time-varying
More informationDeterministic Dynamic Programming
Deterministic Dynamic Programming 1 Value Function Consider the following optimal control problem in Mayer s form: V (t 0, x 0 ) = inf u U J(t 1, x(t 1 )) (1) subject to ẋ(t) = f(t, x(t), u(t)), x(t 0
More informationHankel Optimal Model Reduction 1
Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science 6.242, Fall 2004: MODEL REDUCTION Hankel Optimal Model Reduction 1 This lecture covers both the theory and
More informationECE 301 Fall 2011 Division 1 Homework 5 Solutions
ECE 301 Fall 2011 ivision 1 Homework 5 Solutions Reading: Sections 2.4, 3.1, and 3.2 in the textbook. Problem 1. Suppose system S is initially at rest and satisfies the following input-output difference
More informationThe incompressible Navier-Stokes equations in vacuum
The incompressible, Université Paris-Est Créteil Piotr Bogus law Mucha, Warsaw University Journées Jeunes EDPistes 218, Institut Elie Cartan, Université de Lorraine March 23th, 218 Incompressible Navier-Stokes
More informationThe goal of this chapter is to study linear systems of ordinary differential equations: dt,..., dx ) T
1 1 Linear Systems The goal of this chapter is to study linear systems of ordinary differential equations: ẋ = Ax, x(0) = x 0, (1) where x R n, A is an n n matrix and ẋ = dx ( dt = dx1 dt,..., dx ) T n.
More informationECEEN 5448 Fall 2011 Homework #4 Solutions
ECEEN 5448 Fall 2 Homework #4 Solutions Professor David G. Meyer Novemeber 29, 2. The state-space realization is A = [ [ ; b = ; c = [ which describes, of course, a free mass (in normalized units) with
More informationEE363 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 informationOn some nonlinear parabolic equation involving variable exponents
On some nonlinear parabolic equation involving variable exponents Goro Akagi (Kobe University, Japan) Based on a joint work with Giulio Schimperna (Pavia Univ., Italy) Workshop DIMO-2013 Diffuse Interface
More informationLearning parameters in ODEs
Learning parameters in ODEs Application to biological networks Florence d Alché-Buc Joint work with Minh Quach and Nicolas Brunel IBISC FRE 3190 CNRS, Université d Évry-Val d Essonne, France /14 Florence
More informationPerspective. ECE 3640 Lecture 11 State-Space Analysis. To learn about state-space analysis for continuous and discrete-time. Objective: systems
ECE 3640 Lecture State-Space Analysis Objective: systems To learn about state-space analysis for continuous and discrete-time Perspective Transfer functions provide only an input/output perspective of
More informationME 234, Lyapunov and Riccati Problems. 1. This problem is to recall some facts and formulae you already know. e Aτ BB e A τ dτ
ME 234, Lyapunov and Riccati Problems. This problem is to recall some facts and formulae you already know. (a) Let A and B be matrices of appropriate dimension. Show that (A, B) is controllable if and
More information