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Lyapunov Stability Criterion and

Stability Analysis using this Criterion
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Introduction
The stability of linear system can be determined by asymptotically stability criterion and Bounded-
Input-Bounded-Output (BIBO) stability criterion. But Lyapunov stability criterion is basically
useful for checking the stability of nonlinear systems. Actually, stability of both linear and non-
linear systems can be determined using Lyapunov method.

Lyapunov stability criterion was developed by A. M. Lyapunov in 1892 and because of his name
this criterion is known as Lyapunov stability criterion. This criterion is based on the concept of
energy if the total energy of a system is dissipated then the system is always stable.

Equilibrium State
A state of an autonomous system is called an equilibrium state if the system will stay at that point
after starting from that point in the absence of the forcing input.
If a system is described by 𝑋̇ = 𝑓(𝑋), then at equilibrium state (𝑋𝑒 ),
𝑋̇ = 𝑓(𝑋𝑒 ) = 0.

Stability in the sense of Lyapunov

Consider an autonomous system described by
𝑋̇ = 𝑓(𝑋)

The above system is said to be stable in the sense of Lyapunov about an equilibrium point 𝑋𝑒 if
for every 𝜀 > 0,there exist 𝛿(𝜀) > 0 such that ‖𝑥0 − 𝑥𝑒 ‖ < 𝛿 and ‖𝑥(𝑡) − 𝑥𝑒 ‖ < 𝜀 for ∀𝑡 ≥ 0.

Here ‖. ‖ represents the Euclidean norm. The ‖𝑋‖ = √𝑥12 + 𝑥22 + ⋯ + 𝑥𝑛2 , which represents a
hyper-spherical region.

A dynamic system is Lyapunov stable about an equilibrium point if state trajectories are confined
to a bounded region whenever the initial condition is chosen sufficiently close to equilibrium point.

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Asymptotically Stability Theorem

An equilibrium state 𝑋𝑒 of an autonomous system is asymptotically stable if
• it is stable and 𝛿𝑎 < 𝛿
• exist if ‖𝑥0 − 𝑥𝑒 ‖ < 𝛿𝑎 and ‖𝑥(𝑡) − 𝑥𝑒 ‖ → 0 as 𝑡 → ∞.

For asymptotically stable, state trajectories converge to the equilibrium point.

Instability Theorem
A dynamic system is unstable about an equilibrium point if state trajectories leave a bounded
region whenever the initial condition is chosen sufficiently close to equilibrium point.

Local Stability (stability in-the-small) and Global stability (Stability in-the-large)

The stability in the sense of Lyapunov is applicable in a small region as in this definition, the
region considered around the equilibrium point is very small. So, this type of stability is known as
local stability or stable in-the-small region.
If the region considered is large i.e. if entire state-space is considered then the stability is known
as stable in-the -large or global stability.

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Definiteness of scalar function

Positive definite function:
The function 𝑓(𝑥) is positive definite if 𝑓(0) = 0 and 𝑓(𝑥) > 0 for all 𝑥 ∈ 𝑅 𝑛 .
Example: 𝑓(𝑥1 , 𝑥2 ) = 𝑥12 + 𝑥22 .

Positive semi-definite Function:

The function 𝑓(𝑥) is positive semidefinite if 𝑓(0) = 0 and 𝑓(𝑥) ≥ 0 for all 𝑥 ∈ 𝑅 𝑛 .
Example: 𝑓(𝑥1 , 𝑥2 ) = (𝑥1 + 𝑥2 )2 .

Negative definite function:

If the function −𝑓(𝑥) is positive definite then function is called negative definite function.
Example: 𝑓(𝑥1 , 𝑥2 ) = −(𝑥12 + 𝑥22 ).

Negative semi-definite function:

If the function −𝑓(𝑥) is positive semidefinite then function is called negative semidefinite
function.
Example: 𝑓(𝑥1 , 𝑥2 ) = −(𝑥1 + 𝑥2 )2

Indefinite function:
A function is not definite or semidefinite in either sense is defined to be indefinite function. For
some values of x, f(x) is positive and for some values x, it is negative.
Example: 𝑓(𝑥1 , 𝑥2 ) = 𝑥1 𝑥2 + 𝑥22 .

Sign definiteness of quadratic function

Consider a continuous function V (x) that can be expressed as
𝑉(𝑥) = 𝑥 𝑇 𝑃𝑥

where 𝑃 ∈ 𝑅 𝑛×𝑛 is a symmetric matrix.

If the matrix P is positive definite, then the corresponding quadratic form 𝑉(𝑥)is also will be
positive definite.
The sign of matrix P can be determined using Sylvester’s criterion and also form the eigenvalues
of the matrix P.

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Sylvester’s criterion:
A symmetric matrix P is positive definite if and only if all its principle minors are positive.
𝑃11 𝑃12 𝑃13
Consider P is a 3 × 3 symmetric matrix and 𝑃 = [𝑃12 𝑃22 𝑃23 ]
𝑃13 𝑃23 𝑃33

𝑃11 𝑃12 𝑃13

𝑃11 𝑃12
If 𝑃11 > 0; | | > 0 and |𝑃12 𝑃22 𝑃23 | > 0, then P will be positive definite.
𝑃12 𝑃22
𝑃13 𝑃23 𝑃33

Alternatively, the sign definiteness of P can be determined from the eigen values of P also. The
matrix P will be positive definite, if and only if all the eigenvalues of P are positive. If eigenvalues
are positive or zero, then P will be positive semidefinite.

Lyapunov stability criterion (Direct method)

In this approach, stability of the system can be determined without solving the differential
equation. So, this approach is known as direct method. The direct method of Lyapunov stability
criterion is based upon the concept of energy and the relation of stored energy with system stability.

Consider a spring-mass-damper system as shown in Fig. above. Here the dynamics of the system
is given by-

𝑥̈ + 𝐷𝑥̇ + 𝐾𝑥 = 0
Where x is the linear displacement of the body, M is the mass of the body and here M=1, D is the
damping co-efficient and K is the spring constant.
Here state variables are-

𝑥 = 𝑥1 ; 𝑥̇ = 𝑥2

So the equations are-

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𝑥̇ 1 = 𝑥2

𝑥̇ 2 = −𝐷𝑥2 − 𝐾𝑥1

For this system, the energy storage elements are mass and the spring. So the internal energy stored
by the system-
1 2 1 2
𝑉(𝑋) = 𝑥 + 𝐾𝑥
2 2 2 1
The rate of change of energy is given by-

𝑉̇ (𝑋) = 𝑥2 𝑥̇ 2 + 𝐾𝑥1 𝑥̇ 1

= 𝑥2 (−𝐷𝑥2 − 𝐾𝑥1 ) + 𝐾𝑥1 𝑥2

= −𝐷𝑥22
From the above equation, it can be observed that the rate of change of internal stored energy is
negative. So here internal stored energy decreases. So, the system is here a stable system.
So, an energy function is associated with the direct method of the Lyapunov stability criterion and
that energy function should be positive definite. This energy function can be determined easily if
the physical system is known to us. But it is very difficult to determine energy function from the
mathematical equations of the system if the system components are not known to us. In that case
fictious energy function is considered. This fictious energy function is known as Lyapunov
function and the Lyapunov function should be positive definite function.

Lyapunov’s stability theorem (direct method)

Consider an autonomous system described by
𝑋̇ = 𝑓(𝑋) (1)

Suppose that there exists a scalar function 𝑉(𝑥) which for some real number 𝜀 > 0, satisfies the
following properties for all x in the region ‖𝑥‖ ≤ 𝜀:

• V(x) is positive definite function i.e. 𝑉(𝑥) > 0; 𝑥 ≠ 0 and 𝑉(0) = 0.

• V(x) has continuous partial derivatives with respect to all components of x.

The equilibrium state 𝑥𝑒 = 0 of the system given by equation (1) is

• asymptotically stable if 𝑉̇ (𝑥) < 0, 𝑥 ≠ 0, i.e., 𝑉̇ (𝑥) is a negative definite function.

• asymptotically stable in-the-large if 𝑉̇ (𝑥) < 0, 𝑥 ≠ 0 and in addition 𝑉(𝑥) → ∞ as
‖𝑥‖ → ∞.

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Lyapunov’s instability theorem (direct method)

Consider an autonomous system described by
𝑋̇ = 𝑓(𝑋) (2)

Suppose that there exists a scalar function 𝑉(𝑥) which for some real number 𝜀 > 0, satisfies the
following properties for all x in the region ‖𝑥‖ ≤ 𝜀:

• V(x) is positive definite function i.e. 𝑉(𝑥) > 0; 𝑥 ≠ 0 and 𝑉(0) = 0.

• V(x) has continuous partial derivatives with respect to all components of x.

The equilibrium state 𝑥𝑒 = 0 of the system given by equation (2) is unstable if 𝑉̇ (𝑥) > 0, 𝑥 ≠ 0,
i.e., 𝑉̇ (𝑥) is a positive definite function.

Lyapunov function for linear system

Using Lyapunov’s direct method, stability of both linear and non-linear system can be determined.
Consider a linear autonomous system described by
𝑥̇ = 𝐴𝑥 (3)
where A is 𝑛 × 𝑛 real constant matrix.
The linear system described by equ. (3) is globally asymptotically stable at the origin if and only
if, for any given symmetric positive definite matrix Q, there exists a symmetric positive definite
matrix P that satisfies the matrix equation
𝐴𝑇 𝑃 + 𝑃𝐴 = −𝑄
Proof: Consider Lyapunov function 𝑉(𝑥) = 𝑥 𝑇 𝑃𝑥
where 𝑉(𝑥) is positive definite function.

Then 𝑉̇ (𝑥) = 𝑥̇ 𝑇 𝑃𝑥 + 𝑥 𝑇 𝑃𝑥̇

= 𝑥 𝑇 𝐴𝑇 𝑃𝑥 + 𝑥 𝑇 𝑃𝐴𝑥
= 𝑥 𝑇 (𝐴𝑇 𝑃 + 𝑃𝐴)𝑥
= −𝑥 𝑇 𝑄𝑥
∴ 𝐴𝑇 𝑃 + 𝑃𝐴 = −𝑄
The above equation is known as Lyapunov equation for linear system.

As Q is positive definite, 𝑉̇ (𝑥) will be negative definite. So, the system will be stable.
1⁄
Norm of x is defined as ‖𝑥‖ = (𝑥 𝑇 𝑃𝑥) 2

So 𝑉(𝑥) = ‖𝑥‖2
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𝑉(𝑥) → ∞ as ‖𝑥‖ → ∞
So, the system is here globally asymptotically stable at the origin.

Note:
• The system will be asymptotically stable if for positive definite matrix Q, the solution of
Lyapunov equation P is also becomes positive definite.
• The matrix P is here symmetric matrix.
• For convenience the matrix Q is often chosen as identity matrix.

Krasovskii’s method for nonlinear system stability analysis

Consider a nonlinear system described by the dynamic equation
𝑋̇ = 𝑓(𝑋)
The Lyapunov function for the system is
𝑉(𝑋) = 𝑓 𝑇 (𝑋)𝑓(𝑋)
Here V(X) is positive definite function.

Then 𝑉̇ (𝑥) = 𝑓̇ 𝑇 𝑓 + 𝑓 𝑇 𝑓̇
𝜕𝑓 𝜕𝑓 𝜕𝑓
= 𝑋̇ 𝑇 (𝜕𝑋)𝑇 𝑓 + 𝑓 𝑇 𝜕𝑋 𝑓 [here 𝜕𝑋 is the Jacobian matrix]

𝜕𝑓 𝑇 𝜕𝑓
= 𝑓 𝑇 (𝜕𝑋 + 𝜕𝑋) 𝑓

= 𝑓 𝑇 𝐹𝑓

Here if F is negative definite, 𝑉̇ (𝑥) is also will be negative definite and the nonlinear system will
be stable.

Books:

• J. Nagrath, M. Gopal, Control System Engineering, fifth edition, 2007.

• K. R. Verma, Modern Control Theory, CBS Publishers and Distributors Pvt Ltd, 2018.

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