Equilibrium point analysis linearization technique

Equilibrium Point Analysis: Linearization Technique
TARUN GEHLOT (B.E, CIVIL, HONOURS)
Recall that only the solutions of linear systems may be found explicitly. The
problem is that in general real life problems may only be modeled by
nonlinear systems. In this case, we only know how to describe the solutions
globally (via nullclines). What happens around an equilibrium point
remains a mystery so far. Here we propose the to discuss this problem. The
main idea is to approximate a nonlinear system by a linear one (around the
equilibrium point). Of course, we do hope that the behavior of the
solutions of the linear system will be the same as the nonlinear one. This is
the case most of the time (not all the time!).
Example. Consider the Van der Pol equation
This is a nonlinear equation. Let us translate this equation into a system.
Set
. Then we have
The equilibrium points reduce to the only point (0,0). Let us find the
nullclines and the direction of the velocity vectors along them.
The x-nullcline is given by
Hence the x-nullcline is the x-axis.
The y-nullcline is given by

Hence the y-nullcline is the curve
.
In the picture below we draw the nullclines and direction of the velocity
vectors along them.
Note that the arrangement of these curves tell us that the solutions
``circles'' around the origin. But it is not clear whether the solutions circle
and dye at the origin, circle away from the origin, or keep on circling
periodically. A very rough approach to this problem suggests that if we
rewrite the term
as
, then when (x,y) is close to (0,0), the term
is very small compared to -x+y. Hence a close system to the original
nonlinear system is

which happens to be a linear system. The eigenvalues of this system are
. Hence the solutions of the linear system spiral away from the origin (since
the real part
is positive). So we suggest that the solutions of nonlinear system spiral
away from the origin (look at the picture below)
The solution started close to the equilibrium point, then it moved away.
Notice that in this case, the trajectory is getting close to what looks like a
cycle. To better see this, let us consider the graphs of the
function x(t) andy(t):

and
So what if we want to generalize this to different systems. Is there a
technique that mimic what we did? The answer is yes. It is
called linearization.
Linearization Technique.
Consider the autonomous system

And assume that
is an equilibrium point. So we would like to find the closest linear system
when (x,y) is close to
. In order to do that we need to approximate the functions f(x,y) and g(x,y)
. This is a similar problem to approximating a real valued function by its
tangent (around a point of course). From multivariable calculus, we get
and
. Then the nonlinear system may be approximated by the system
But since
is an equilibrium point, then we have
. Hence we have

This is a linear system. Its coefficient matrix is
This matrix is called the Jacobian matrix of the system at the point
.
Summary of the linearization technique.
Consider the autonomous system
and
an equilibrium point.
Find the partial derivatives
Write down the Jacobian matrix

Find the eigenvalues of the Jacobian matrix.
Deduce the fate of the solutions around the equilibrium point from the
eigenvalues. For example,
if the eigenvalues are negative or complex with negative real part, then the
equilibrium point is a sink (that is all the solutions will dye at the
equilibrium point). Note that if the eigenvalues are complex, then the
solutions will spiral around the equilibrium point.
If the eigenvalues are positive or complex with positive real part, then the
equilibrium point is a source (that is all the solutions will move away from
the equilibrium point). Note that if the eigenvalues are complex, then the
solutions will spiral away from the equilibrium point.
If the eigenvalues are real number with different sign (one positive and one
negative), then the the equilibrium point is a saddle. In fact, there will be
two solutions which approach the equilibrium point as
, and two more solutions which approach the equilibrium point as
. For the linear system theses solutions are lines, but for the nonlinear
system they are not in general. These four solutions are
called separatrix.Remark. When dealing with an autonomous system
without prior knowledge of the equilibrium point, then we advice to first
find the Jacobian matrix and plug the values for every equilibrium point.
This way you don't repeat the calculations over and over again.
Example. Consider the equation of the pendulum

where
is the damping coefficient. See the picture below.
The equivalent system is
The equilibrium points are
, where
. The angles
, for

, correspond to the pendulum at its lowest position, while
, for
, correspond to the pendulum at its highest position. The Jacobian matrix of
the system
Let us concentrate on the equilibrium positions (0,0) and
.
For (0,0), the Jacobian matrix is
For the sake of illustration let us fix the parameters. For example,
if we take
(undamped pendulum), then the eigenvalues are
which implies that the mass will oscillate around the lowest position in a
periodic fashion.
If
(dumped pendulum), m=1, and l=1. Then the eigenvalues are

Since the real part is negative, the solutions will sink (dye) while oscillating
around the equilibrium point. Here we have the same behavior for the
linear and nonlinear system.
For
, the Jacobian matrix is
The eigenvalues are
Clearly we have two real eigenvalues with one positive and one negative.
So the solutions will always get away from the equilibrium position except
along one curve (the separatrix).

Equilibrium point analysis linearization technique

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