tools for analysis of dynamic systems: lyapunov s...
TRANSCRIPT
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Tools for Analysis of Dynamic
Systems: Lyapunov’s Methods
Stanisław H. Żak
School of Electrical and
Computer Engineering
ECE 680 Fall 2013
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A. M. Lyapunov’s (1857--1918) Thesis
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Lyapunov’s Thesis
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Lyapunov’s Thesis Translated
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Some Details About Translation
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Outline
Notation using simple examples of dynamical system models
Objective of analysis of a nonlinear system
Equilibrium points
Lyapunov functions
Stability
Barbalat’s lemma
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A Spring-Mass Mechanical System
x---displacement of the mass from the rest position
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Modeling the Mass-Spring System
Assume a linear mass, where k is the linear spring constant
Apply Newton’s law to obtain
Define state variables: x1=x and x2=dx/dt
The model in state-space format:
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Analysis of the Spring-Mass System
Model
The spring-mass system model is linear time-invariant (LTI)
Representing the LTI system in standard state-space format
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Modeling of the Simple Pendulum
The simple pendulum
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The Simple Pendulum Model
Apply Newton’s second law
where J is the moment of inertia,
Combining gives
sinmglJ
2mlJ
sinl
g
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State-Space Model of the Simple
Pendulum
Represent the second-order differential equation as an equivalent system of two first-order differential equations
First define state variables,
x1=θ and x2=dθ/dt
Use the above to obtain state–space model (nonlinear, time invariant)
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Objectives of Analysis of Nonlinear Systems
Similar to the objectives pursued when investigating complex linear systems
Not interested in detailed solutions, rather one seeks to characterize the system behavior---equilibrium points and their stability properties
A device needed for nonlinear system analysis summarizing the system behavior, suppressing detail
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Summarizing Function (D.G.
Luenberger, 1979)
A function of the system state vector
As the system evolves in time, the summarizing function takes on various values conveying some information about the system
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Summarizing Function as a First-Order
Differential Equation
The behavior of the summarizing function describes a first-order differential equation
Analysis of this first-order differential equation in some sense a summary analysis of the underlying system
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Dynamical System Models
Linear time-invariant (LTI) system model
Nonlinear system model
Shorthand notation of the above model
nnA,Axx
nxxtfx ,,
nn
n
n
n x,,x,tf
x,,x,tf
x,,x,tf
x
x
x
1
12
11
2
1
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More Notation
System model
Solution
Example: LTI model,
Solution of the LTI modeling equation
00 xtx,tx,tftx
00 x,t;txtx
00 xx,Axx
0xetx At
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Equilibrium Point
A vector is an equilibrium point for a dynamical system model
if once the state vector equals to it remains equal to for all future time. The equilibrium point satisfies
ex
tx,tftx
ex
ex
0, txtf
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Formal Definition of Equilibrium
A point xe is called an equilibrium point of dx/dt=f(t,x), or simply an equilibrium, at time t0 if for all t ≥ t0,
f(t, xe)=0
Note that if xe is an equilibrium of our system at t0, then it is also an equilibrium for all τ ≥ t0
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Equilibrium Points for LTI Systems
For the time invariant system
dx/dt=f(x)
a point is an equilibrium at some time τ if and only if it is an equilibrium at all times
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Equilibrium State for LTI Systems
LTI model
Any equilibrium state must satisfy
If exist, then we have unique equilibrium state
Axx,tfx
0eAx
ex
1A
0ex
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Equilibrium States of Nonlinear
Systems
A nonlinear system may have a number of equilibrium states
The origin, x=0, may or may not be an equilibrium state of a nonlinear system
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Translating the Equilibrium of
Interest to the Origin
If the origin is not the equilibrium state, it is always possible to translate the origin of the coordinate system to that state
So, no loss of generality is lost in assuming that the origin is the equilibrium state of interest
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Example of a Nonlinear System with
Multiple Equilibrium Points
Nonlinear system model
Two isolated equilibrium states
2121
2
2
1
xxx
x
x
x
0
1
0
021
ee xx
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Isolated Equilibrium
An equilibrium point xe in Rn is an isolated equilibrium point if there is an r>0 such that the r-neighborhood of xe contains no equilibrium points other than xe
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Neighborhood of xe
The r-neighborhood of xe can be a set of points of the form
where ||.|| can be any p-norm on Rn
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Remarks on Stability
Stability properties characterize the system behavior if its initial state is close but not at the equilibrium point of interest
When an initial state is close to the equilibrium pt., the state may remain close, or it may move away from the equilibrium point
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An Informal Definition of Stability
An equilibrium state is stable if whenever the initial state is near that point, the state remains near it, perhaps even tending toward the equilibrium point as time increases
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Stability Intuitive Interpretation
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Formal Definition of Stability
An equilibrium state is stable, in the sense
of Lyapunov, if for any given and any positive
scalar there exist a positive scalar
such that if
then
for all
eqx
0t
,0t
exxttx 00,;
extx 0
0tt
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Stability Concept in 1D
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Stability Concepts in 2D
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Further Discussion of Lyapunov
Stability
Think of a contest between you, the control system designer, and an adversary (nature?)---B. Friedland (ACSD, p. 43, Prentice-Hall, 1996)
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Lyapunov Stability Game
The adversary picks a region in the state space of radius ε
You are challenged to find a region of radius δ such that if the initial state starts out inside your region, it remains in his region---if you can do this, your system is stable, in the sense of Lyapunov
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Lyapunov Stability---Is It Any
Good?
Lyapunov stability is weak---it does not even imply that x(t) converges to xe as t approaches infinity
The states are only required to hover around the equilibrium state
The stability condition bounds the amount of wiggling room for x(t)
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Asymptotic Stability i.s.L
The property of an equilibrium state of a differential equation that satisfies two conditions:
(stability) small perturbations in the initial condition produce small perturbations in the solution;
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Second Condition for Asymptotic
Stability of an Equilibrium
(attractivity of the equilibrium point) there is a domain of attraction such that whenever the initial condition belongs to this domain the solution approaches the equilibrium state at large times
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Asymptotic Stability in the sense of
Lyapunov (i.s.L.)
The equilibrium state is asymptotically stable if
it is stable, and
convergent, that is,
tasxx,t;tx e00
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Convergence Alone Does Not
Guarantee Asymptotic Stability
Note: it is not sufficient that just
for asymptotic stability. We need stability too! Why?
tasxx,t;tx e00
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How Long to the Equilibrium?
Asymptotic stability does not imply anything about how long it takes to converge to a prescribed neighborhood of xe
Exponential stability provides a way to express the rate of convergence
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Asymptotic Stability of Linear
Systems
An LTI system is asymptotically stable, meaning, the equilibrium state at the origin is asymptotically stable, if and only if the eigenvalues of A have negative real parts
For LTI systems asymptotic stability is equivalent with convergence (stability condition automatically satisfied)
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Asymptotic Stability of Nonlinear
Systems
For LTI systems asymptotic stability is equivalent with convergence (stability condition automatically satisfied)
For nonlinear systems the state may initially tend away from the equilibrium state of interest but subsequently may return to it
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Asymptotic Stability in 1D
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Convergence Does Not Mean
Asymptotic Stability (W. Hahn, 1967)
Hahn’s 1967 Example---A system whose all solutions are approaching the equilibrium, xe=0, without this equilibrium being asymptotically stable (Antsaklis and Michel, Linear Systems, 1997, p. 451)
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Convergence Does Not Mean
Asymptotic Stability (W. Hahn, 1967)
Nonlinear system of Hahn where the origin is attractive but not a.s.
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Phase Portrait of Hahn’s 1967 Example
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Instability in 1D
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Lyapunov Functions---Basic Idea
Seek an aggregate summarizing function that continually decreases toward a minimum
For mechanical systems---energy of a free mechanical system with friction always decreases unless the system is at rest, equilibrium
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Lyapunov Function Definition
A function that allows one to deduce stability is termed a Lyapunov function
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Lyapunov Function Properties
for Continuous Time Systems
Continuous-time system
Equilibrium state of interest
txftx
ex
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Three Properties of a Lyapunov
Function
We seek an aggregate summarizing function V
V is continuous
V has a unique minimum with respect to all other points in some neighborhood of the equilibrium of interest
Along any trajectory of the system, the value of V never increases
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Lyapunov Theorem for Continuous
Systems
Continuous-time system
Equilibrium state of interest
txftx
0ex
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Lyapunov Theorem---Negative Rate
of Increase of V
If x(t) is a trajectory, then V(x(t)) represents the corresponding values of V along the trajectory
In order for V(x(t)) not to increase, we require
0txV
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The Lyapunov Derivative
Use the chain rule to compute the derivative of V(x(t))
Use the plant model to obtain
Recall
xxVtxVT
xfxVtxVT
T
x
V
x
V
x
VxV
221
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Lyapunov Theorem for LTI Systems
The system dx/dt=Ax is asymptotically stable, that is, the equilibrium state xe=0 is asymptotically stable (a.s), if and only if any solution converges to xe=0 as t tends to infinity for any initial x0
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Lyapunov Theorem Interpretation
View the vector x(t) as defining the coordinates of a point in an n-dimensional state space
In an a.s. system the point x(t) converges to xe=0
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Lyapunov Theorem for n=2
If a trajectory is converging to xe=0, it should be possible to find a nested set of closed curves V(x1,x2)=c, c≥0, such that decreasing values of c yield level curves shrinking in on the equilibrium state xe=0
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Lyapunov Theorem and Level
Curves
The limiting level curve V(x1,x2)=V(0)=0 is 0 at the equilibrium state xe=0
The trajectory moves through the level curves by cutting them in the inward direction ultimately ending at xe=0
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The trajectory is moving in the
direction of decreasing V Note that
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Level Sets
The level curves can be thought of as contours of a cup-shaped surface
For an a.s. system, that is, for an a.s. equilibrium state xe=0, each trajectory falls to the bottom of the cup
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Positive Definite Function---General
Definition
The function V is positive definite in S, with respect to xe, if V has continuous partials, V(xe)=0, and V(x)>0 for all x in S, where x≠xe
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Positive Definite Function With
Respect to the Origin
Assume, for simplicity, xe=0, then the function V is positive definite in S if V has continuous partials, V(0)=0, and V(x)>0 for all x in S, where x≠0
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Example: Positive Definite Function
Positive definite function of two variables
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Positive Semi-Definite Function---
General Definition
The function V is positive semi-definite in S, with respect to xe, if V has continuous partials, V(xe)=0, and V(x)≥0 for all x in S
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Positive Semi-Definite Function With
Respect to the Origin
Assume, for simplicity, xe=0, then the function V is positive semi-definite in S if V has continuous partials, V(0)=0, and V(x)≥0 for all x in S
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Example: Positive Semi-Definite
Function
An example of positive semi-definite function of two variables
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Quadratic Forms
V=xTPx, where P=PT
If P not symmetric, need to symmetrize it
First observe that because the transposition of a scalar equals itself, we have
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Symmetrizing Quadratic Form
Perform manipulations
Note that
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Tests for Positive and Positive Semi-
Definiteness of Quadratic Form
V=xTPx, where P=PT, is positive definite if and only if all eigenvalues of P are positive
V=xTPx, where P=PT, is positive semi-definite if and only if all eigenvalues of P are non-negative
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Comments on the Eigenvalue Tests
These tests are only good for the case when P=PT. You must symmetrize P before applying the above tests
Other tests, the Sylvester’s criteria, involve checking the signs of principal minors of P
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Negative Definite Quadratic Form
V=xTPx is negative definite if and only if
-xTPx=xT(-P)x
is positive definite
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Negative Semi-Definite Quadratic
Form
V=xTPx is negative semi-definite if and only if
-xTPx=xT(-P)x
is positive semi-definite
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Example: Checking the Sign
Definiteness of a Quadratic Form
Is P, equivalently, is the associated
quadratic form, V=xTPx, pd, psd,
nd, nsd, or neither?
The associated quadratic form
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Example: Symmetrizing the Underlying
Matrix of the Quadratic Form
Applying the eigenvalue test to the given quadratic form would seem to indicate that the quadratic form is pd, which turns out to be false
Need to symmetrize the underlying matrix first and then can apply the eigenvalue test
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Example: Symetrized Matrix
Symmetrizing manipulations
The eigenvalues of the symmetrized matrix are: 5 and -1
The quadratic form is indefinite!
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Example: Further Analysis Direct check that the quadratic form
is indefinite
Take x=[1 0]T. Then
Take x=[1 1]T. Then
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Stability Test for xe=0 of dx/dt=Ax Let V=xTPx where P=PT>0
For V to be a Lyapunov function, that is, for xe=0 to be a.s.,
Evaluate the time derivative of V on the solution of the system dx/dt=Ax---Lyapunov derivative
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Lyapunov Derivative for dx/dt=Ax
Note that V(x(t))=x(t)TPx(t)
Use the chain rule
We used
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Lyapunov Matrix Equation Denote
Then the Lyapunov derivative can be represented as
where
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Terms to Our Vocabulary
Theorem---a major result of independent interest
Lemma---an auxiliary result that is used as a stepping stone toward a theorem
Corollary---a direct consequence of a theorem, or even a lemma
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Lyapunov Theorem
The real matrix A is a.s., that is, all eigenvalues of A have negative real parts if and only if for any the solution of the continuous matrix Lyapunov equation
is (symmetric) positive definite
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How Do We Use the Lyapunov
Theorem?
Select an arbitrary symmetric positive definite Q , for example, an identity matrix, In
Solve the Lyapunov equation for P=PT
If P is positive definite, the matrix A is a.s. If P is not p.d. then A is not a.s.
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How NOT to Use the Lyapunov
Theorem
It would be no use choosing P to be positive definite and then calculating Q
For unless Q turns out to be positive definite, nothing can be said about a.s. of A from the Lyapunov equation
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Example: How NOT to Use the
Lyapunov Theorem
Consider an a.s. matrix
Try
Compute
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Example: Computing Q
The matrix Q is indefinite!---recall the previous example
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Solving the Continuous Matrix
Lyapunov Equation Using MATLAB
Use the MATLAB’s command lyap
Example:
Q=I2
P=lyap(A,Q)
Eigenvalues of P are positive: 0.2729 and 2.9771; P is positive definite
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Limitations of the Lyapunov
Method
Usually, it is challenging to analyze the asymptotic stability of time-varying systems because it is very difficult to find Lyapunov functions with negative definite derivatives
When can one conclude asymptotic stability when the Lyapunov derivative is only negative semi-definite?
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Some Properties of Time-Varying
Functions
does not imply that f(t)
has a limit as
f(t) has a limit as does not
imply that
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More Properties of Time-Varying
Functions
If f(t) is lower bounded and
decreasing ( ), then it
converges to a limit. (A well-known result from calculus.)
But we do not know whether
or not as
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Preparation for Barbalat’s Lemma
Under what conditions
We already know that the existence
of the limit of f(t) as
is not enough for
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Continuous Function
A function f(t) is continuous if small changes in t result in small changes in f(t)
Intuitively, a continuous function is a function whose graph can be drawn without lifting the pencil from the paper
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Continuity on an Interval
Continuity is a local property of a function—that is, a function f is continuous, or not, at a particular point
A function being continuous on an interval means only that it is continuous at each point of the interval
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Uniform Continuity
A function f(t) is uniformly continuous if it is continuous and, in addition, the size of the changes in f(t) depends only on the size of the changes in t but not on t itself
The slope of an uniformly continuous function slope is bounded, that is,
is bounded
Uniform continuity is a global property of a function
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Properties of Uniformly Continuous
Function
Every uniformly continuous function is continuous, but the converse is not true
A function is uniformly continuous, or not, on an entire interval
A function may be continuous at each point of an interval without being uniformly continuous on the entire interval
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Examples
Uniformly continuous:
f(t) = sin(t)
Note that the slope of the above function is bounded
Continuous, but not uniformly continuous on positive real numbers:
f(t) = 1/t
Note that as t approaches 0, the changes in f(t) grow beyond any bound
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State of an a.s. System With
Bounded Input is Bounded
Example of Slotine and Li, ―Applied Nonlinear Control,‖ p. 124, Prentice Hall, 1991
Consider an a.s. stable LTI system with bounded input
The state x is bounded because u is bounded and A is a.s.
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Output of a.s. System With Bounded
Input is Uniformly Continuous
Because x is bounded and u is bounded, is bounded
Derivative of the output equation is
The time derivative of the output is bounded
Hence, y is uniformly continuous
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Barbalat’s Lemma
If f(t) has a finite limit as
and if is uniformly continuous
(or is bounded), then
as
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Lyapunov-Like Lemma
Given a real-valued function W(t,x) such that
W(t,x) is bounded below
W(t,x) is negative semi-definite
is uniformly continuous in
t (or bounded) then
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Lyapunov-Like Lemma---Example;
see p. 211 of the Text
Interested in the stability of the origin of the system
where u is bounded
Consider the Lyapunov function candidate
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Stability Analysis of the System in
the Example
The Lyapunov derivative of V is
The origin is stable; cannot say anything about asymptotic stability
Stability implies that x1 and x2 are bounded
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Example: Using the Lyapunov-Like
Lemma
We now show that
Note that V=x12+x2
2 is bounded from below and non-increasing as
Thus V has a limit as
Need to show that is uniformly continuous
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Example: Uniform Continuity of
Compute the derivative of and check if it is bounded
The function is uniformly
continuous because is bounded
Hence
Therefore
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Benefits of the Lyapunov Theory
Solution to differential equation are not needed to infer about stability properties of equilibrium state of interest
Barbalat’s lemma complements the
Lyapunov Theorem
Lyapunov functions are useful in designing robust and adaptive controllers