Nonlinear Control. Nonlinear Control Lecture # 25 State Feedback Stabilization
|
|
- Florence Lewis
- 5 years ago
- Views:
Transcription
1 Nonlinear Control Lecture # 25 State Feedback Stabilization
2 Backstepping η = f a (η)+g a (η)ξ ξ = f b (η,ξ)+g b (η,ξ)u, g b 0, η R n, ξ, u R Stabilize the origin using state feedback View ξ as virtual control input to the system η = f a (η)+g a (η)ξ Suppose there is ξ = φ(η) that stabilizes the origin of η = f a (η)+g a (η)φ(η) V a η [f a(η)+g a (η)φ(η)] W(η)
3 z = ξ φ(η) η = [f a (η)+g a (η)φ(η)]+g a (η)z ż = F(η,ξ)+g b (η,ξ)u V(η,ξ) = V a (η)+ 1 2 z2 = V a (η)+ 1 [ξ φ(η)]2 2 V = V a η [f a(η)+g a (η)φ(η)]+ V a η g a(η)z +zf(η,ξ)+zg b (η,ξ)u [ ] Va W(η)+z η g a(η)+f(η,ξ)+g b (η,ξ)u
4 ] Va V W(η)+z[ η g a(η)+f(η,ξ)+g b (η,ξ)u [ ] 1 Va u = g b (η,ξ) η g a(η)+f(η,ξ)+kz, k > 0 V W(η) kz 2
5 Example 9.9 ẋ 1 = x 2 1 x3 1 +x 2, ẋ 2 = u ẋ 1 = x 2 1 x3 1 +x 2 x 2 = φ(x 1 ) = x 2 1 x 1 ẋ 1 = x 1 x 3 1 V a (x 1 ) = 1 2 x2 1 V a = x 2 1 x 4 1, x 1 R z 2 = x 2 φ(x 1 ) = x 2 +x 1 +x 2 1 ẋ 1 = x 1 x 3 1 +z 2 ż 2 = u+(1+2x 1 )( x 1 x 3 1 +z 2)
6 V(x) = 1 2 x z2 2 V = x 1 ( x 1 x 3 1 +z 2) +z 2 [u+(1+2x 1 )( x 1 x 3 1 +z 2 )] V = x 2 1 x 4 1 +z 2 [x 1 +(1+2x 1 )( x 1 x 3 1 +z 2)+u] u = x 1 (1+2x 1 )( x 1 x 3 1 +z 2) z 2 V = x 2 1 x 4 1 z 2 2 The origin is globally asymptotically stable
7 Example 9.10 ẋ 1 = x 2 1 x 3 1 +x 2, ẋ 2 = x 3, ẋ 3 = u ẋ 1 = x 2 1 x3 1 +x 2, ẋ 2 = x 3 x 3 = x 1 (1+2x 1 )( x 1 x 3 1 +z 2) z 2 def = φ(x 1,x 2 ) V a (x) = 1 2 x z2 2, Va = x 2 1 x4 1 z2 2 z 3 = x 3 φ(x 1,x 2 ) ẋ 1 = x 2 1 x 3 1 +x 2, ẋ 2 = φ(x 1,x 2 )+z 3 ż 3 = u φ (x 2 1 x 3 1 +x 2 ) φ (φ+z 3 ) x 1 x 2
8 V = V a z2 3 V = V a x 1 (x 2 1 x3 1 +x 2)+ V a x 2 (z 3 +φ) +z 3 [ u φ x 1 (x 2 1 x3 1 +x 2) φ x 2 (z 3 +φ) ] V = x 2 1 x4 1 (x 2 +x 1 +x 2 1 [ )2 Va +z 3 φ (x 2 1 x 3 1 +x 2 ) φ ] (z 3 +φ)+u x 2 x 1 x 2 u = V a x 2 + φ x 1 (x 2 1 x3 1 +x 2)+ φ x 2 (z 3 +φ) z 3 The origin is globally asymptotically stable
9 Strict-Feedback Form ẋ = f 0 (x)+g 0 (x)z 1 ż 1 = f 1 (x,z 1 )+g 1 (x,z 1 )z 2 ż 2 = f 2 (x,z 1,z 2 )+g 2 (x,z 1,z 2 )z 3. ż k 1 = f k 1 (x,z 1,...,z k 1 )+g k 1 (x,z 1,...,z k 1 )z k ż k = f k (x,z 1,...,z k )+g k (x,z 1,...,z k )u g i (x,z 1,...,z i ) 0 for 1 i k
10 Example 9.12 ẋ = x+x 2 z, ż = u ẋ = x+x 2 z z = 0 ẋ = x, V a = 1 2 x2 V a = x 2 V = 1 2 (x2 +z 2 ) V = x( x+x 2 z)+zu = x 2 +z(x 3 +u) u = x 3 kz, k > 0, V = x 2 kz 2 Global stabilization Compare with semiglobal stabilization in Example 9.7
11 Example 9.13 ẋ = x 2 xz, ż = u ẋ = x 2 xz z = x+x 2 ẋ = x 3, V 0 (x) = 1 2 x2 V = x 4 V = V (z x x2 ) 2 V = x 4 +(z x x 2 )[ x 2 +u (1+2x)(x 2 xz)] u = (1+2x)(x 2 xz)+x 2 k(z x x 2 ), k > 0 V = x 4 k(z x x 2 ) 2 Global stabilization
12 Passivity-Based Control ẋ = f(x,u), y = h(x), f(0,0) = 0 u T y V = V x f(x,u) Theorem 9.1 If the system is (1) passive with a radially unbounded positive definite storage function and (2) zero-state observable, then the origin can be globally stabilized by u = φ(y), φ(0) = 0, y T φ(y) > 0 y 0
13 Proof V = V x f(x, φ(y)) yt φ(y) 0 V(x(t)) 0 y(t) 0 u(t) 0 x(t) 0 Apply the invariance principle A given system may be made passive by or both (1) Choice of output, (2) Feedback,
14 Choice of Output V ẋ = f(x)+g(x)u, f(x) 0, x x No output is defined. Choose the output as y = h(x) def = [ ] T V x G(x) V = V V f(x)+ x x G(x)u yt u Check zero-state observability
15 Example 9.14 ẋ 1 = x 2, ẋ 2 = x 3 1 +u V(x) = 1 4 x x2 2 With u = 0 V = x 3 1 x 2 x 2 x 3 1 = 0 Is it zero-state observable? Take y = V x G = V x 2 = x 2 with u = 0, y(t) 0 x(t) 0 u = kx 2 or u = (2k/π)tan 1 (x 2 ) (k > 0)
16 Feedback Passivation Definition The system ẋ = f(x)+g(x)u, y = h(x) ( ) is equivalent to a passive system if u = α(x)+β(x)v such that ẋ = f(x)+g(x)α(x)+g(x)β(x)v, y = h(x) is passive Theorem [20] The system (*) is locally equivalent to a passive system (with a positive definite storage function) if it has relative degree one at x = 0 and the zero dynamics have a stable equilibrium point at the origin with a positive definite Lyapunov function
17 Example 9.15 (m-link Robot Manipulator) M(q) q +C(q, q) q +D q +g(q) = u M = M T > 0, (Ṁ 2C)T = ( M 2C), D = D T 0 Stabilize the system at q = q r e = q q r, ė = q M(q)ë+C(q, q)ė+dė+g(q) = u (e = 0,ė = 0) is not an open-loop equilibrium point u = g(q) K p e+v, (K p = Kp T > 0) M(q)ë+C(q, q)ė+dė+k p e = v
18 M(q)ë+C(q, q)ė+dė+k p e = v V = 1 M(q)ė+ 1 2ėT 2 et K p e V = 1 (Ṁ 2ėT 2C)ė ėt Dė ė T K p e+ė T v +e T K p ė ė T v y = ė Is it zero-state observable? Set v = 0 ė(t) 0 ë(t) 0 K p e(t) 0 e(t) 0 v = φ(ė), [φ(0) = 0, ė T φ(ė) > 0, ė 0] u = g(q) K p e φ(ė) Special case: u = g(q) K p e K d ė, K d = K T d > 0
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 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 informationTTK4150 Nonlinear Control Systems Solution 6 Part 2
TTK4150 Nonlinear Control Systems Solution 6 Part 2 Department of Engineering Cybernetics Norwegian University of Science and Technology Fall 2003 Solution 1 Thesystemisgivenby φ = R (φ) ω and J 1 ω 1
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 informationStabilization 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 informationNonlinear Control. Nonlinear Control Lecture # 24 State Feedback Stabilization
Nonlinear Control Lecture # 24 State Feedback Stabilization Feedback Lineaization What information do we need to implement the control u = γ 1 (x)[ ψ(x) KT(x)]? What is the effect of uncertainty in ψ,
More informationMCE693/793: Analysis and Control of Nonlinear Systems
MCE693/793: Analysis and Control of Nonlinear Systems Input-Output and Input-State Linearization Zero Dynamics of Nonlinear Systems Hanz Richter Mechanical Engineering Department Cleveland State University
More informationNonlinear 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 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 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 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 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 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 informationEN 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 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 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 informationAutomatic Control 2. Nonlinear systems. Prof. Alberto Bemporad. University of Trento. Academic year
Automatic Control 2 Nonlinear systems Prof. Alberto Bemporad University of Trento Academic year 2010-2011 Prof. Alberto Bemporad (University of Trento) Automatic Control 2 Academic year 2010-2011 1 / 18
More informationDO NOT DO HOMEWORK UNTIL IT IS ASSIGNED. THE ASSIGNMENTS MAY CHANGE UNTIL ANNOUNCED.
EE 533 Homeworks Spring 07 Updated: Saturday, April 08, 07 DO NOT DO HOMEWORK UNTIL IT IS ASSIGNED. THE ASSIGNMENTS MAY CHANGE UNTIL ANNOUNCED. Some homework assignments refer to the textbooks: Slotine
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 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 informationPassivity-based Stabilization of Non-Compact Sets
Passivity-based Stabilization of Non-Compact Sets Mohamed I. El-Hawwary and Manfredi Maggiore Abstract We investigate the stabilization of closed sets for passive nonlinear systems which are contained
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 CONTROLLER DESIGN FOR ACTIVE SUSPENSION SYSTEMS USING THE IMMERSION AND INVARIANCE METHOD
NONLINEAR CONTROLLER DESIGN FOR ACTIVE SUSPENSION SYSTEMS USING THE IMMERSION AND INVARIANCE METHOD Ponesit Santhanapipatkul Watcharapong Khovidhungij Abstract: We present a controller design based on
More informationIterative methods to compute center and center-stable manifolds with application to the optimal output regulation problem
Iterative methods to compute center and center-stable manifolds with application to the optimal output regulation problem Noboru Sakamoto, Branislav Rehak N.S.: Nagoya University, Department of Aerospace
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 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 information1 The Observability Canonical Form
NONLINEAR OBSERVERS AND SEPARATION PRINCIPLE 1 The Observability Canonical Form In this Chapter we discuss the design of observers for nonlinear systems modelled by equations of the form ẋ = f(x, u) (1)
More informationLOWELL WEEKLY JOURNAL
Y -» $ 5 Y 7 Y Y -Y- Q x Q» 75»»/ q } # ]»\ - - $ { Q» / X x»»- 3 q $ 9 ) Y q - 5 5 3 3 3 7 Q q - - Q _»»/Q Y - 9 - - - )- [ X 7» -» - )»? / /? Q Y»» # X Q» - -?» Q ) Q \ Q - - - 3? 7» -? #»»» 7 - / Q
More informationMathematics for Control Theory
Mathematics for Control Theory Geometric Concepts in Control Involutivity and Frobenius Theorem Exact Linearization Hanz Richter Mechanical Engineering Department Cleveland State University Reading materials
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 informationDigital implementation of backstepping controllers via input/lyapunov matching
Digital implementation of backstepping controllers via input/lyapunov matching Fernando Tiefensee Valentin Tanasa Laboratoire des Signaux et Systèmes, CNRS- Université Paris Sud 11 -Supelec, Plateau de
More informationLyapunov Based Control
Lyapunov Based Control Control Lyapunov Functions Consider the system: x = f(x, u), x R n f(0,0) = 0 Idea: Construct a stabilizing controller in steps: 1. Choose a differentiable function V: R n R, such
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 informationLyapunov-based methods in control
Dr. Alexander Schaum Lyapunov-based methods in control Selected topics of control engineering Seminar Notes Stand: Summer term 2018 c Lehrstuhl für Regelungstechnik Christian Albrechts Universität zu Kiel
More informationLyapunov 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 informationLyapunov 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 informationMinimum-Phase Property of Nonlinear Systems in Terms of a Dissipation Inequality
Minimum-Phase Property of Nonlinear Systems in Terms of a Dissipation Inequality Christian Ebenbauer Institute for Systems Theory in Engineering, University of Stuttgart, 70550 Stuttgart, Germany ce@ist.uni-stuttgart.de
More informationGlobal output regulation through singularities
Global output regulation through singularities Yuh Yamashita Nara Institute of Science and Techbology Graduate School of Information Science Takayama 8916-5, Ikoma, Nara 63-11, JAPAN yamas@isaist-naraacjp
More information2 Lyapunov Stability. x(0) x 0 < δ x(t) x 0 < ɛ
1 2 Lyapunov Stability Whereas I/O stability is concerned with the effect of inputs on outputs, Lyapunov stability deals with unforced systems: ẋ = f(x, t) (1) where x R n, t R +, and f : R n R + R n.
More informationA differential Lyapunov framework for contraction analysis
A differential Lyapunov framework for contraction analysis F. Forni, R. Sepulchre University of Liège Reykjavik, July 19th, 2013 Outline Incremental stability d(x 1 (t),x 2 (t)) 0 Differential Lyapunov
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 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 informationGame Theory Extra Lecture 1 (BoB)
Game Theory 2014 Extra Lecture 1 (BoB) Differential games Tools from optimal control Dynamic programming Hamilton-Jacobi-Bellman-Isaacs equation Zerosum linear quadratic games and H control Baser/Olsder,
More informationPractice Problems for Final Exam
Math 1280 Spring 2016 Practice Problems for Final Exam Part 2 (Sections 6.6, 6.7, 6.8, and chapter 7) S o l u t i o n s 1. Show that the given system has a nonlinear center at the origin. ẋ = 9y 5y 5,
More informationREGULATION OF NONLINEAR SYSTEMS USING CONDITIONAL INTEGRATORS. Abhyudai Singh
REGULATION OF NONLINEAR SYSTEMS USING CONDITIONAL INTEGRATORS By Abhyudai Singh A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE
More informationEN Nonlinear Control and Planning in Robotics Lecture 10: Lyapunov Redesign and Robust Backstepping April 6, 2015
EN530.678 Nonlinear Control and Planning in Robotics Lecture 10: Lyapunov Redesign and Robust Backstepping April 6, 2015 Prof: Marin Kobilarov 1 Uncertainty and Lyapunov Redesign Consider the system [1]
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 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 informationDO NOT DO HOMEWORK UNTIL IT IS ASSIGNED. THE ASSIGNMENTS MAY CHANGE UNTIL ANNOUNCED.
EE 5 Homewors Fall 04 Updated: Sunday, October 6, 04 Some homewor assignments refer to the textboos: Slotine and Li, etc. For full credit, show all wor. Some problems require hand calculations. In those
More informationOptimal Control. Lecture 20. Control Lyapunov Function, Optimal Estimation. John T. Wen. April 5. Ref: Papers by R. Freeman (on-line), B&H Ch.
Optimal Control Lecture 20 Control Lyapunov Function, Optimal Estimation John T. Wen April 5 Ref: Papers by R. Freeman (on-line), B&H Ch. 12 Outline Summary of Control Lyapunov Function and example Introduction
More informationHigh-Gain Observers in Nonlinear Feedback Control. Lecture # 2 Separation Principle
High-Gain Observers in Nonlinear Feedback Control Lecture # 2 Separation Principle High-Gain ObserversinNonlinear Feedback ControlLecture # 2Separation Principle p. 1/4 The Class of Systems ẋ = Ax + Bφ(x,
More informationEECE Adaptive Control
EECE 574 - Adaptive Control Model-Reference Adaptive Control - Part I Guy Dumont Department of Electrical and Computer Engineering University of British Columbia January 2010 Guy Dumont (UBC EECE) EECE
More informationA nemlineáris rendszer- és irányításelmélet alapjai Relative degree and Zero dynamics (Lie-derivatives)
A nemlineáris rendszer- és irányításelmélet alapjai Relative degree and Zero dynamics (Lie-derivatives) Hangos Katalin BME Analízis Tanszék Rendszer- és Irányításelméleti Kutató Laboratórium MTA Számítástechnikai
More informationFeedback Linearization
Feedback Linearization Peter Al Hokayem and Eduardo Gallestey May 14, 2015 1 Introduction Consider a class o single-input-single-output (SISO) nonlinear systems o the orm ẋ = (x) + g(x)u (1) y = h(x) (2)
More informationNonlinear Control Lecture # 14 Tracking & Regulation. Nonlinear Control
Nonlinear Control Lecture # 14 Tracking & Regulation Normal form: η = f 0 (η,ξ) ξ i = ξ i+1, for 1 i ρ 1 ξ ρ = a(η,ξ)+b(η,ξ)u y = ξ 1 η D η R n ρ, ξ = col(ξ 1,...,ξ ρ ) D ξ R ρ Tracking Problem: Design
More informationOn 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 informationStabilization for a Class of Nonlinear Systems: A Fuzzy Logic Approach
Proceedings of the 17th World Congress The International Federation of Automatic Control Seoul, Korea, July 6-11, 8 Stabilization for a Class of Nonlinear Systems: A Fuzzy Logic Approach Bernardino Castillo
More informationASTATISM IN NONLINEAR CONTROL SYSTEMS WITH APPLICATION TO ROBOTICS
dx dt DIFFERENTIAL EQUATIONS AND CONTROL PROCESSES N 1, 1997 Electronic Journal, reg. N P23275 at 07.03.97 http://www.neva.ru/journal e-mail: diff@osipenko.stu.neva.ru Control problems in nonlinear systems
More informationFeedback Linearization Lectures delivered at IIT-Kanpur, TEQIP program, September 2016.
Feedback Linearization Lectures delivered at IIT-Kanpur, TEQIP program, September 216 Ravi N Banavar banavar@iitbacin September 24, 216 These notes are based on my readings o the two books Nonlinear Control
More informationTopic # /31 Feedback Control Systems. Analysis of Nonlinear Systems Lyapunov Stability Analysis
Topic # 16.30/31 Feedback Control Systems Analysis of Nonlinear Systems Lyapunov Stability Analysis Fall 010 16.30/31 Lyapunov Stability Analysis Very general method to prove (or disprove) stability of
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 informationMCE693/793: Analysis and Control of Nonlinear Systems
MCE693/793: Analysis and Control of Nonlinear Systems Lyapunov Stability - I Hanz Richter Mechanical Engineering Department Cleveland State University Definition of Stability - Lyapunov Sense Lyapunov
More informationEquilibrium points: continuous-time systems
Capitolo 0 INTRODUCTION 81 Equilibrium points: continuous-time systems Let us consider the following continuous-time linear system ẋ(t) Ax(t)+Bu(t) y(t) Cx(t)+Du(t) The equilibrium points x 0 of the system
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 informationRobust Semiglobal Nonlinear Output Regulation The case of systems in triangular form
Robust Semiglobal Nonlinear Output Regulation The case of systems in triangular form Andrea Serrani Department of Electrical and Computer Engineering Collaborative Center for Control Sciences The Ohio
More informationWeighted balanced realization and model reduction for nonlinear systems
Weighted balanced realization and model reduction for nonlinear systems Daisuke Tsubakino and Kenji Fujimoto Abstract In this paper a weighted balanced realization and model reduction for nonlinear systems
More informationStabilization of a Chain of Exponential Integrators Using a Strict Lyapunov Function
Stabilization of a Chain of Exponential Integrators Using a Strict Lyapunov Function Michael Malisoff Miroslav Krstic Chain of Exponential Integrators { Ẋ = (Y Y )X Ẏ = (D D)Y, (X, Y ) (0, ) 2 X = pest
More informationPSEUDO-HAMILTONIAN REALIZATION AND ITS APPLICATION
COMMUNICATIONS IN INFORMATION AND SYSTEMS c 2002 International Press Vol. 2, No. 2, pp. 91-120, December 2002 001 PSEUDO-HAMILTONIAN REALIZATION AND ITS APPLICATION DAIZHAN CHENG, TIELONG SHEN, AND T.
More informationContraction Based Adaptive Control of a Class of Nonlinear Systems
9 American Control Conference Hyatt Regency Riverfront, St. Louis, MO, USA June -, 9 WeB4.5 Contraction Based Adaptive Control of a Class of Nonlinear Systems B. B. Sharma and I. N. Kar, Member IEEE Abstract
More informationNonlinear Control Lecture # 36 Tracking & Regulation. Nonlinear Control
Nonlinear Control Lecture # 36 Tracking & Regulation Normal form: η = f 0 (η,ξ) ξ i = ξ i+1, for 1 i ρ 1 ξ ρ = a(η,ξ)+b(η,ξ)u y = ξ 1 η D η R n ρ, ξ = col(ξ 1,...,ξ ρ ) D ξ R ρ Tracking Problem: Design
More informationPort-Hamiltonian systems: network modeling and control of nonlinear physical systems
Port-Hamiltonian systems: network modeling and control of nonlinear physical systems A.J. van der Schaft February 3, 2004 Abstract It is shown how port-based modeling of lumped-parameter complex physical
More informationHomework Solution # 3
ECSE 644 Optimal Control Feb, 4 Due: Feb 17, 4 (Tuesday) Homework Solution # 3 1 (5%) Consider the discrete nonlinear control system in Homework # For the optimal control and trajectory that you have found
More informationMCE/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 informationFormula Sheet for Optimal Control
Formula Sheet for Optimal Control Division of Optimization and Systems Theory Royal Institute of Technology 144 Stockholm, Sweden 23 December 1, 29 1 Dynamic Programming 11 Discrete Dynamic Programming
More informationTo sample or not to sample: Self-triggered control for nonlinear systems
arxiv:86.79v1 [math.oc] 4 Jun 28 To sample or not to sample: Self-triggered control for nonlinear systems Adolfo Anta and Paulo Tabuada This research was partially supported by the National Science Foundation
More informationLOWELL WEEKLY JOURNAL
G $ G 2 G ««2 ««q ) q «\ { q «««/ 6 «««««q «] «q 6 ««Z q «««Q \ Q «q «X ««G X G ««? G Q / Q Q X ««/«X X «««Q X\ «q «X \ / X G XX «««X «x «X «x X G X 29 2 ««Q G G «) 22 G XXX GG G G G G G X «x G Q «) «G
More informationOutput Regulation of Uncertain Nonlinear Systems with Nonlinear Exosystems
Output Regulation of Uncertain Nonlinear Systems with Nonlinear Exosystems Zhengtao Ding Manchester School of Engineering, University of Manchester Oxford Road, Manchester M3 9PL, United Kingdom zhengtaoding@manacuk
More informationCDS 101/110a: Lecture 2.1 Dynamic Behavior
CDS 11/11a: Lecture.1 Dynamic Behavior Richard M. Murray 6 October 8 Goals: Learn to use phase portraits to visualize behavior of dynamical systems Understand different types of stability for an equilibrium
More informationMA 201: Partial Differential Equations D Alembert s Solution Lecture - 7 MA 201 (2016), PDE 1 / 20
MA 201: Partial Differential Equations D Alembert s Solution Lecture - 7 MA 201 (2016), PDE 1 / 20 MA 201 (2016), PDE 2 / 20 Vibrating string and the wave equation Consider a stretched string of length
More informationHybrid active and semi-active control for pantograph-catenary system of high-speed train
Hybrid active and semi-active control for pantograph-catenary system of high-speed train I.U. Khan 1, D. Wagg 1, N.D. Sims 1 1 University of Sheffield, Department of Mechanical Engineering, S1 3JD, Sheffield,
More informationState-Feedback Optimal Controllers for Deterministic Nonlinear Systems
State-Feedback Optimal Controllers for Deterministic Nonlinear Systems Chang-Hee Won*, Abstract A full-state feedback optimal control problem is solved for a general deterministic nonlinear system. The
More informationBackstepping Design for Time-Delay Nonlinear Systems
Backstepping Design for Time-Delay Nonlinear Systems Frédéric Mazenc, Projet MERE INRIA-INRA, UMR Analyse des Systèmes et Biométrie, INRA, pl. Viala, 346 Montpellier, France, e-mail: mazenc@helios.ensam.inra.fr
More informationNash Equilibrium Seeking with Output Regulation. Andrew R. Romano
Nash Equilibrium Seeking with Output Regulation by Andrew R. Romano A thesis submitted in conformity with the requirements for the degree of Master of Applied Science The Edward S. Rogers Sr. Department
More informationPort-Hamiltonian systems: a theory for modeling, simulation and control of complex physical systems
Port-Hamiltonian systems: a theory for modeling, simulation and control of complex physical systems A.J. van der Schaft B.M. Maschke July 2, 2003 Abstract It is shown how port-based modeling of lumped-parameter
More informationPithy P o i n t s Picked I ' p and Patljr Put By Our P e r i p a tetic Pencil Pusher VOLUME X X X X. Lee Hi^h School Here Friday Ni^ht
G G QQ K K Z z U K z q Z 22 x z - z 97 Z x z j K K 33 G - 72 92 33 3% 98 K 924 4 G G K 2 G x G K 2 z K j x x 2 G Z 22 j K K x q j - K 72 G 43-2 2 G G z G - -G G U q - z q - G x) z q 3 26 7 x Zz - G U-
More informationCDS 101/110a: Lecture 2.1 Dynamic Behavior
CDS 11/11a: Lecture 2.1 Dynamic Behavior Richard M. Murray 6 October 28 Goals: Learn to use phase portraits to visualize behavior of dynamical systems Understand different types of stability for an equilibrium
More information1 Controllability and Observability
1 Controllability and Observability 1.1 Linear Time-Invariant (LTI) Systems State-space: Dimensions: Notation Transfer function: ẋ = Ax+Bu, x() = x, y = Cx+Du. x R n, u R m, y R p. Note that H(s) is always
More informationFeedback stabilisation with positive control of dissipative compartmental systems
Feedback stabilisation with positive control of dissipative compartmental systems G. Bastin and A. Provost Centre for Systems Engineering and Applied Mechanics (CESAME Université Catholique de Louvain
More informationRobust Control of Robot Manipulator by Model Based Disturbance Attenuation
IEEE/ASME Trans. Mechatronics, vol. 8, no. 4, pp. 511-513, Nov./Dec. 2003 obust Control of obot Manipulator by Model Based Disturbance Attenuation Keywords : obot manipulators, MBDA, position control,
More informationModel reduction of nonlinear systems using incremental system properties
Model redction of nonlinear systems sing incremental system properties Bart Besselink ACCESS Linnaes Centre & Department of Atomatic Control KTH Royal Institte of Technology, Stockholm, Sweden L2S, Spelec,
More informationEE222 - Spring 16 - Lecture 2 Notes 1
EE222 - Spring 16 - Lecture 2 Notes 1 Murat Arcak January 21 2016 1 Licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Essentially Nonlinear Phenomena Continued
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 informationSolution of Additional Exercises for Chapter 4
1 1. (1) Try V (x) = 1 (x 1 + x ). Solution of Additional Exercises for Chapter 4 V (x) = x 1 ( x 1 + x ) x = x 1 x + x 1 x In the neighborhood of the origin, the term (x 1 + x ) dominates. Hence, the
More informationA framework for stabilization of nonlinear sampled-data systems based on their approximate discrete-time models
1 A framework for stabilization of nonlinear sampled-data systems based on their approximate discrete-time models D.Nešić and A.R.Teel Abstract A unified framework for design of stabilizing controllers
More informationNonlinear Control Lecture 9: Feedback Linearization
Nonlinear Control Lecture 9: Feedback Linearization Farzaneh Abdollahi Department of Electrical Engineering Amirkabir University of Technology Fall 2011 Farzaneh Abdollahi Nonlinear Control Lecture 9 1/75
More informationMA 201, Mathematics III, July-November 2016, Partial Differential Equations: 1D wave equation (contd.) and 1D heat conduction equation
MA 201, Mathematics III, July-November 2016, Partial Differential Equations: 1D wave equation (contd.) and 1D heat conduction equation Lecture 12 Lecture 12 MA 201, PDE (2016) 1 / 24 Formal Solution of
More informationEML5311 Lyapunov Stability & Robust Control Design
EML5311 Lyapunov Stability & Robust Control Design 1 Lyapunov Stability criterion In Robust control design of nonlinear uncertain systems, stability theory plays an important role in engineering systems.
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 informationIntroduction to Geometric Control
Introduction to Geometric Control Relative Degree Consider the square (no of inputs = no of outputs = p) affine control system ẋ = f(x) + g(x)u = f(x) + [ g (x),, g p (x) ] u () h (x) y = h(x) = (2) h
More information1. Find the solution of the following uncontrolled linear system. 2 α 1 1
Appendix B Revision Problems 1. Find the solution of the following uncontrolled linear system 0 1 1 ẋ = x, x(0) =. 2 3 1 Class test, August 1998 2. Given the linear system described by 2 α 1 1 ẋ = x +
More information