# 8.3 The view from phase space

A lot of the technical literature on nonlinear systems uses an approach that allows some geometrical insight into the behaviour, at least for systems with smooth nonlinearity. This approach is based on an idea called phase space. We will give an introduction in this section, based on the simplest possible example. Suppose we have a nonlinear oscillator with a single mass, or more generally a single degree of freedom. For any such system, if you specify the position and the velocity at the moment when you set things off, that is sufficient information to determine the subsequent free motion. (The technical reason is that the motion will obey some version of Newton’s law, and so the governing equation will be of second order, requiring two initial conditions to determine a unique solution.)

We can picture this in a two-dimensional plot, with position and velocity as the two axes, as sketched in Fig. 1. Our initial position and velocity, at time $t=0$ say, is represented by a dot in this plane. The subsequent motion of the system, for $t>0$, can then be plotted as a trajectory starting from that point. If we think of running time backwards, we could create the other half of the trajectory, coming from times $t<0$ and reaching our point at $t=0$. But we could have chosen a different position and velocity for our initial spot, and we could have done that anywhere in this plane. So there is a trajectory passing through every point, and there is only one. To put that in different words, the diagram must be filled up with a pattern of trajectories, and they cannot cross. This diagram is called the phase plane, or more generally phase space. The pattern of trajectories is the phase portrait of the particular system we choose to study. It captures in one picture every possible motion of the system.

A good example to illustrate the idea is the pendulum discussed in section 8.2.1 and shown again here in Fig. 2. Its angle of swing is described by an angle $\theta$ (Greek “theta”, remember). We will use this angle for the horizontal axis of our phase portrait, and its rate of change $d\theta / dt$ as the corresponding “velocity” on the vertical axis.

The phase portrait for the pendulum appears in Fig. 3. This plot doesn’t show an arrow on every trajectory, but it is easy to work out where the arrows need to point. When we are above the middle of the plot, that means that the velocity $d\theta / dt$ is positive. In other words, the angle $\theta$ is increasing, so all the arrows point towards the right. In the lower half, $d\theta / dt$ is negative so $\theta$ is decreasing and all the arrows point to the left.

The values marked on the horizontal axis may be confusing if you aren’t used to thinking of angles in radians, but the code is very simple. One complete revolution of the pendulum takes $\theta$ through an angle of $2\pi$ radians. The value $\theta=0$ corresponds to the pendulum hanging vertically downwards. The values $-2\pi$, $2\pi$ and so on are simply further representations of the same position, after a whole number of rotations. The values $\theta = -3\pi,-\pi,\pi,3\pi$ and so on all indicate the vertical-upwards position.

Figure 3 shows trajectories of two different shapes. Along the middle of the plot there are closed-loop trajectories, whereas in the upper and lower portions there are undulating lines that cross the entire plot. These correspond to the two types of motion you should expect for a pendulum. The closed loops show oscillation, back and forth about the position at the bottom of the swing. The undulating lines in the upper part (with “arrows of time” pointing to the right, remember) show the pendulum continuously rotating, in the direction of increasing $\theta$ and so anticlockwise in the view of Fig. 2. The undulating lines in the lower half have $\theta$ decreasing, so they describe clockwise rotation. These three types of motion are summarised in Fig. 4. Figure 4. The three types of qualitatively different motion of the pendulum, indicated in the phase plane. They are separated by curves emanating from saddle points. Such a curve is called a separatrix.

The curves that separate motion of different types are obviously important. These curves are trajectories with a very special property, but to see what that is we should first back off a little and address a more general issue. There are a few points in Fig. 3 which are special, because they correspond to possible equilibrium positions of the pendulum. The pendulum has two equilibrium positions. The obvious one is at $\theta=0$, where the pendulum can hang vertically downwards without any motion. But there is another, where the pendulum sits vertically upwards. In principle the pendulum could balance in that position without moving, but in practice you don’t see it doing so because this equilibrium position is unstable: you only have to tweak the position away from the perfect vertical by the smallest amount, and it will topple and start to swing. The mathematics behind stability and instability of the pendulum is explained in the next link.

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These equilibrium positions feature multiple times in Fig. 3, because we have “unwrapped” the angle $\theta$ to allow for the possibility of rotating motion. The stable equilibrium corresponds to the point at the centre of each of the sets of closed loops: at $\theta=0,2\pi,4\pi$ and so on, on the mid-line of the plot where $d\theta/dt=0$ because the pendulum is stationary. The unstable equilibrium also occurs on that mid-line, at the positions $\theta=-\pi,\pi$ and so on. These are the only points in the plot where we seem to have trajectories that cross. But I said earlier that this couldn’t happen, so what is going on? We will see in a moment that they don’t in fact cross, but they are important for another reason.

The behaviour of the phase portrait in the vicinity of an equilibrium point can be analysed — at least for a system like this one where the nonlinearity is smooth. The details are explained in the next link, but the main result can be shown in pictures. For an undamped system like our pendulum, there are only two types of behaviour that can occur near an equilibrium point (also called a singular point by the mathematicians). The two corresponding phase portraits are shown, in a rather general form, in Fig. 5. On the left, we see what is called a centre: it features a set of closed loops, and describes oscillation around the equilibrium point. Behaviour roughly like this will always occur near a stable equilibrium point of any undamped system. On the right we see a saddle point, which is the generic behaviour near any unstable equilibrium point.

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We can immediately recognise these two patterns in Fig. 3. Now look closely at the right-hand plot of Fig. 5, for the saddle point, and pay close attention to the pattern of arrows. The plot does indeed seem to show a pair of trajectories crossing in the middle, but the arrows tell a different story. One of these lines has inward-pointing arrows either side of the “crossing”, while the other has outward-pointing arrows. In the context of our pendulum, we can describe what is happening. The two inward-point arrows indicate the trajectories in which you propel the pendulum upwards with just enough energy that it approaches the unstable equilibrium, but never quite gets there. You could do that either from the left-hand side or from the right-hand side: these are the two inward-pointing segments. The outward-pointing pair show the converse behaviour: the pendulum toppling either to the right or to the left, after an infinitesimally small nudge away from the unstable equilibrium. So what we have is four trajectories, not two, and there is no crossing. The equilibrium point at the centre is a trajectory all by itself.

Now look back at Fig. 4: the curves dividing the different types of behaviour are precisely the trajectories we have just been talking about, coming into and out of the saddle points marking the unstable equilibrium of the pendulum. Such a trajectory is known in the jargon of the subject as a separatrix, precisely because they always have this property of separating behaviour with different qualitative descriptions. We begin to see the power of the phase portrait: it gives a way to encapsulate everything the system is capable of doing, and it has a built-in method for sorting out regions with interestingly different behaviour.

We can take our pendulum example a little further. What happens if we introduce some damping to our pendulum, so that the motion will eventually die away? The phase portrait must change to reflect this fact. Nothing much happens near the saddle points: they are still unstable, and the qualitative behaviour stays the same. But the centre that previously described the behaviour near the stable equilibrium turns into a stable spiral. The corresponding phase portrait is illustrated in general form in Fig. 6: it should be pretty much what you were expecting, showing trajectories spiralling in towards the equilibrium point.