Nonlinear Control. Nonlinear Control Lecture # 25 State Feedback Stabilization

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

Output Feedback and State Feedback. EL2620 Nonlinear Control. Nonlinear Observers. Nonlinear Controllers. ẋ = f(x,u), y = h(x)

TTK4150 Nonlinear Control Systems Solution 6 Part 2

Nonlinear Control Lecture 7: Passivity

Stabilization and Passivity-Based Control

Nonlinear Control. Nonlinear Control Lecture # 24 State Feedback Stabilization

MCE693/793: Analysis and Control of Nonlinear Systems

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

EG4321/EG7040. Nonlinear Control. Dr. Matt Turner

Linearization problem. The simplest example

Nonlinear Control Systems

Mathematics for Control Theory

Nonlinear Control. Nonlinear Control Lecture # 3 Stability of Equilibrium Points

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

Nonlinear Systems and Control Lecture # 12 Converse Lyapunov Functions & Time Varying Systems. p. 1/1

High-Gain Observers in Nonlinear Feedback Control

Automatic Control 2. Nonlinear systems. Prof. Alberto Bemporad. University of Trento. Academic year

DO NOT DO HOMEWORK UNTIL IT IS ASSIGNED. THE ASSIGNMENTS MAY CHANGE UNTIL ANNOUNCED.

Introduction to Nonlinear Control Lecture # 4 Passivity

Input to state Stability

Passivity-based Stabilization of Non-Compact Sets

Nonlinear Control. Nonlinear Control Lecture # 8 Time Varying and Perturbed Systems

NONLINEAR CONTROLLER DESIGN FOR ACTIVE SUSPENSION SYSTEMS USING THE IMMERSION AND INVARIANCE METHOD

Iterative methods to compute center and center-stable manifolds with application to the optimal output regulation problem

Nonlinear Control. Nonlinear Control Lecture # 8 Time Varying and Perturbed Systems

Prof. Krstic Nonlinear Systems MAE281A Homework set 1 Linearization & phase portrait

1 The Observability Canonical Form

LOWELL WEEKLY JOURNAL

Mathematics for Control Theory

Lecture 9 Nonlinear Control Design

Digital implementation of backstepping controllers via input/lyapunov matching

Lyapunov Based Control

Nonlinear Control. Nonlinear Control Lecture # 2 Stability of Equilibrium Points

Lyapunov-based methods in control

Lyapunov Stability Theory

Lyapunov stability ORDINARY DIFFERENTIAL EQUATIONS

Minimum-Phase Property of Nonlinear Systems in Terms of a Dissipation Inequality

Global output regulation through singularities

2 Lyapunov Stability. x(0) x 0 < δ x(t) x 0 < ɛ

A differential Lyapunov framework for contraction analysis

High-Gain Observers in Nonlinear Feedback Control. Lecture # 3 Regulation

Introduction to Nonlinear Control Lecture # 3 Time-Varying and Perturbed Systems

Game Theory Extra Lecture 1 (BoB)

Practice Problems for Final Exam

REGULATION OF NONLINEAR SYSTEMS USING CONDITIONAL INTEGRATORS. Abhyudai Singh

EN Nonlinear Control and Planning in Robotics Lecture 10: Lyapunov Redesign and Robust Backstepping April 6, 2015

EG4321/EG7040. Nonlinear Control. Dr. Matt Turner

Modeling and Analysis of Dynamic Systems

DO NOT DO HOMEWORK UNTIL IT IS ASSIGNED. THE ASSIGNMENTS MAY CHANGE UNTIL ANNOUNCED.

Optimal Control. Lecture 20. Control Lyapunov Function, Optimal Estimation. John T. Wen. April 5. Ref: Papers by R. Freeman (on-line), B&H Ch.

High-Gain Observers in Nonlinear Feedback Control. Lecture # 2 Separation Principle

EECE Adaptive Control

A nemlineáris rendszer- és irányításelmélet alapjai Relative degree and Zero dynamics (Lie-derivatives)

Feedback Linearization

Nonlinear Control Lecture # 14 Tracking & Regulation. Nonlinear Control

On the PDEs arising in IDA-PBC

Stabilization for a Class of Nonlinear Systems: A Fuzzy Logic Approach

ASTATISM IN NONLINEAR CONTROL SYSTEMS WITH APPLICATION TO ROBOTICS

Feedback Linearization Lectures delivered at IIT-Kanpur, TEQIP program, September 2016.

Topic # /31 Feedback Control Systems. Analysis of Nonlinear Systems Lyapunov Stability Analysis

7.1 Linear Systems Stability Consider the Continuous-Time (CT) Linear Time-Invariant (LTI) system

MCE693/793: Analysis and Control of Nonlinear Systems

Equilibrium points: continuous-time systems

Nonlinear Control Systems

Robust Semiglobal Nonlinear Output Regulation The case of systems in triangular form

Weighted balanced realization and model reduction for nonlinear systems

Stabilization of a Chain of Exponential Integrators Using a Strict Lyapunov Function

PSEUDO-HAMILTONIAN REALIZATION AND ITS APPLICATION

Contraction Based Adaptive Control of a Class of Nonlinear Systems

Nonlinear Control Lecture # 36 Tracking & Regulation. Nonlinear Control

Port-Hamiltonian systems: network modeling and control of nonlinear physical systems

Homework Solution # 3

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

Formula Sheet for Optimal Control

To sample or not to sample: Self-triggered control for nonlinear systems

LOWELL WEEKLY JOURNAL

Output Regulation of Uncertain Nonlinear Systems with Nonlinear Exosystems

CDS 101/110a: Lecture 2.1 Dynamic Behavior

MA 201: Partial Differential Equations D Alembert s Solution Lecture - 7 MA 201 (2016), PDE 1 / 20

Hybrid active and semi-active control for pantograph-catenary system of high-speed train

State-Feedback Optimal Controllers for Deterministic Nonlinear Systems

Backstepping Design for Time-Delay Nonlinear Systems

Nash Equilibrium Seeking with Output Regulation. Andrew R. Romano

Port-Hamiltonian systems: a theory for modeling, simulation and control of complex physical systems

Pithy 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

CDS 101/110a: Lecture 2.1 Dynamic Behavior

1 Controllability and Observability

Feedback stabilisation with positive control of dissipative compartmental systems

Robust Control of Robot Manipulator by Model Based Disturbance Attenuation

Model reduction of nonlinear systems using incremental system properties

EE222 - Spring 16 - Lecture 2 Notes 1

EE363 homework 8 solutions

Solution of Additional Exercises for Chapter 4

A framework for stabilization of nonlinear sampled-data systems based on their approximate discrete-time models

Nonlinear Control Lecture 9: Feedback Linearization

MA 201, Mathematics III, July-November 2016, Partial Differential Equations: 1D wave equation (contd.) and 1D heat conduction equation

EML5311 Lyapunov Stability & Robust Control Design

Modeling & Control of Hybrid Systems Chapter 4 Stability

Introduction to Geometric Control

1. Find the solution of the following uncontrolled linear system. 2 α 1 1

Transcription:

Nonlinear Control Lecture # 25 State Feedback Stabilization

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)

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

] 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

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)

V(x) = 1 2 x2 1 + 1 2 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

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 x2 1 + 1 2 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

V = V a + 1 2 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

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

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

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 0 + 1 2 (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

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

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,

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

Example 9.14 ẋ 1 = x 2, ẋ 2 = x 3 1 +u V(x) = 1 4 x4 1 + 1 2 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)

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

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

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