%% Phase portraits with chebop/quiver
% Asgeir Birkisson, November 2015
%%
% (Chebfun example ode-nonlin/ChebopQuiver.m)
% [Tags: #nonlinearODE, #PhasePortraits]
%% Phase portraits
% Phase portrait are geometric representations of the trajectories of a
% dynamical system in the phase plane, and are an important tool in the study of
% dynamical systems [1]. They consist of plots of trajectories in the state
% space, which frequently corresponds to plotting the derivative of a solution
% against the solution (in the case of second order ODEs), or the plot of one
% solution variable against another (in the case of coupled first order
% systems).
%
% When drawing phase portraits, it's useful to draw vector fields to see the
% rate of change of solutions at a particular point in the phase plane. The
% chebop class now has a $\mathtt{quiver}$ method that draws such vector fields.
% It works for both coupled first order systems with two unknown functions
% (where the second function gets plotted against the first), and second order
% scalar problems (which get automatically reformulated to first order systems,
% and where the phase plane plotted corresponds to plotting the derivative of
% the solution against the solution). Here we show a few examples of how the
% quiver method of chebop can be called.
%% The van der Pol equation
%
% The first ODE we consider is the van der Pol equation [2], a second order
% nonlinear ODE, given by
%
% $$ u'' - \mu(1-u^2)u' + u = 0. $$
%
% We start by defining a chebop defining the differential equation (selecting
% $\mu = 3$). Note that we specify the domain of the chebop as well. This
% is not necessary for the quiver plot itself, but it will allow us to
% overlay particular solutions on the quiver plot.
N = chebop(0,100);
N.op = @(t,u) diff(u, 2) - 3*(1-u^2)*diff(u) + u;
%%
% We call |quiver| with N as an argument, along with a vector argument that
% specifies the lower and upper limits on the $x$ and $y$ axes. Furthermore, we
% give further arguments for customizing the plot, which are described in
% detail in the help text of |chebop/quiver|.
%
% Once we have called |quiver|, we overlay the phase plane portraits of solutions
% obtained by specifying different initial conditions -- notice how the
% solutions follow the arrows of the quiver plot, then get attracted to the same
% limit cycle (regardless of whether we start inside or outside of the cycle).
quiver(N, [-2.75 2.75 -5.5 5.5],'xpts', 40, 'ypts', 40, 'scale', .5, ...
'normalize', true)
hold on
for init = 0.2:0.4:0.2
N.lbc = [init; 1];
u = N\0;
arrowplot(u, diff(u))
end
title('Phase portrait of the van der Pol oscillator')
IN = 'interpreter'; LT = 'latex';
xlabel('$u$',IN,LT), ylabel('$u''$',IN,LT)
hold off
%% A mathematical pendulum
%
% The next ODE we consider is that controlling the trajectory of a nonlinear
% pendulum,
%
% $$ u'' + \sin(u) = 0. $$
%
% Again we define a chebop, call |quiver| and overlay solutions on the
% plot for trajectories
% starting from the stable equilibrium $u=0$ but with different initial
% velocities.
N = chebop(0, 50);
N.op = @(t,u) diff(u,2) + sin(u);
quiver(N, [-2.5 25 -2 5.5],'xpts',30)
hold on
for init = 0:0.5:5
N.lbc = [0, init];
u = N\0;
arrowplot(u, diff(u))
end
hold off
xlim([-2.5 25])
title('Phase portrait for an undamped nonlinear pendulum')
xlabel('$u$',IN,LT), ylabel('$u''$',IN,LT)
%%
% We see that for small enough initial velocities, the pendulum swings back and
% forth around the equilibrium $u=0$, while for larger initial velocities, it
% swings over and over the top position. However, if we introduce damping, all
% trajectories will eventually end up at rest:
N.op = @(t,u) diff(u,2) + 0.25*diff(u) + sin(u);
quiver(N, [-2.5 25 -2 5.5],'xpts',30)
hold on
for init = 0:0.5:5
N.lbc = [0, init];
u = N\0;
plot(u, diff(u))
end
hold off
title('Phase portrait for a damped nonlinear pendulum')
xlabel('$u$',IN,LT), ylabel('$u''$',IN,LT)
%% Lotka-Volterra predator-prey model
% The final equations we consider are the The Lotka-Volterra equations, which
% model the populations of predators (say foxes) and prey (say rabbits) [3].
% These are a pair of nonlinear, first order differential equations, and exhibit
% the behaviour that in the absence of predators, the prey population grows
% exponentially, while the predator population shrinks if the prey population is
% too small. The equations are given by
%
% $$ u' = au - buv, \quad v' = -cv + duv $$
%
% where $a, b, c$ and $d$ are real parameters that describe the interaction of
% the two species.
%
% We begin by setting all the parameters equal to 1, and draw solutions in the
% phase plane obtained from different initial populations of rabbits (keeping
% the initial population of foxes fixed).
N = chebop(@(t,u,v) [diff(u)-u+u.*v; diff(v)+v-u.*v], [0 10]);
quiver(N, [0 5 0 5],'xpts',30,'ypts',30,'normalize',true,'scale',.4)
hold on
for rabbits = 0.1:.2:1.9
N.lbc = @(u,v) [u - rabbits; v - 1]; % Initial populations
[u, v] = N\0;
arrowplot(u, v)
end
hold off
title('Phase portrait for Lotka-Volterra equations')
xlabel('Rabbits'), ylabel('Foxes')
%%
% The cyclical behaviour of the populations is evident. What happens if we
% increase the reproduction rate of the rabbits by 50%?
N = chebop(@(t,u,v) [diff(u)-1.5*u+u.*v; diff(v)+v-u.*v], [0 10]);
quiver(N, [0 5 0 5],'xpts',30,'ypts',30,'normalize',true,'scale',.4)
hold on
for rabbits = 0.1:.2:1.9
N.lbc = @(u,v) [u - rabbits; v - 1]; % Initial populations
[u, v] = N\0;
arrowplot(u, v)
end
xlim([0 5]), ylim([0 5])
hold off
title('Phase portrait for L-V eqns., increased rabbit reproduction')
xlabel('Rabbits'), ylabel('Foxes')
%%
% Comparing the phase portraits, we observe that while the maximum rabbit
% population increased, it was by much less than 50%. In fact, the maximum
% population of foxes grew more than that of rabbits.
%% References
% 1. http://en.wikipedia.org/wiki/Phase_portrait
%
% 2. http://en.wikipedia.org/wiki/Van_der_Pol_oscillator
%
% 3. http://en.wikipedia.org/wiki/Lotka%E2%80%93Volterra_equations