

In linear dynamics, one seeks the fundamental solutions from which one can build all other solutions. In nonlinear dynamics, the main questions are: What is the qualitative behavior of the system? Which and how many nonwandering sets (i.e. a fixed point, a limit cycle, a quasiperiodic or chaotic orbit) occur? Which of them are stable? How does the number of nonwandering sets change while changing a parameter of the system (called control parameter)? The appearance and disappearance of a nonwandering set is called a bifurcation. Change of stability and bifurcation always coincide. There are the two different sides of the same medal!
A nonwandering set may be stable or unstable. There are two types of stability, a weaker and a stronger one:
Thus, a nonwandering set is either asymptotically stable, marginally stable (i.e. only Lyapunov stable), or unstable. Asymptotically stable nonwandering sets are also called attractors. The basin of attraction is the set of all initial states approaching the attractor in the long time limit.
How can we check the stability of a nonwandering set? There is a scheme called linear
stability analysis: Assuming u_{0}(t) is the orbit
you want to check whether it is stable or not.
It is asymptotically stable if any infinitesimal small perturbations
u
decays. Thus, make the ansatz
and
respectively. These linear equations of motion are justified as long as the orbit doesn't go
too far away from
Calculating the roots of a polynomial analytically is impossible if its order is greater than five, and tedious if the order is greater than two. For timecontinuous system, the question on stability can be answered without explicitely calculating the eigenvalues, thanks to the theorem of Routh and Hurwitz. This theorem says that the real parts of all roots of a polynomial are negative if and only if certain conditions are fulfilled which can be easily calculated. This theorem is useful even in the case of a twodimensional phase space where the characteristic polynomial is quadratic: Both eigenvalues have negative real part if and only if the determinant DF(u_{0}) is positive and the trace of DF(u_{0}) is negative. The figure below shows a classification scheme of the fixed points of twodimensional phase spaces.
The notion spiral and node are inspired by the flow near the fixed point. A pair of conjugated complex eigenvalues lead to a spiral, whereas a node is causes by two real eigenvalues of the same sign. Real eigenvalues of different sign lead to a saddle. In general, a saddle is a fixed point where at least one eigenvalue has a positive real part but also at least one eigenvalue has a negative real part. Near a saddle, an orbit is usually attracted at first but repelled later on. There are points in the phase space which approach the fixed point for t . They form the stable manifold. The eigenspace for the eigenvalues with negative real part is tangential to the stable manifold. The unstable manifold are built by all points approaching the fixed point for t  . Saddles and their stable manifolds are usually the boundaries of a basin of attraction. The undriven pendulum has a saddle point (corresponding to the upsidedown equilibrium position) and a spiral if < 2_{0 }or a node if > 2_{0} (see the flow on the previous page).
The number of attractors in a nonlinear dynamical system can change when a system parameter is changed. This change is called bifurcation. It is accompanied by a change of the stability of an attractor. In a bifurcation point, at least one eigenvalue of the Jacobian gets a zero real part. There are three generic types of socalled codimensionone bifurcations (the term codimension counts the number of control parameters for which fine tuning is necessary to get such a bifurcation):
In a stationary bifurcation, a single real eigenvalue crosses the boundary of stability. In the Pendulum Lab, stationary bifurcations of limit cycles occur quit often. They are stationary bifuractions of fixed points in a discrete dynamical system because limit cycles are fixed points in the Poincaré map. 
Hopf bifurcations occur when a conjugated complex pair crosses the boundary of stability. In the timecontinuous case, a limit cycle bifurcates. It has an angular frequency which is given by the imaginary part of the crossing pair. In the discrete case, the bifurcating orbit is generally quasiperiodic, except that the argument of the crossing pair times an integer gives just 2. In the Pendulum Lab, Hopf bifurcations seem to be absent. 
In discrete dynamical systems, a certain bifurcation is possible which is absent in continuous systems: The period doubling bifurcation. Here, a real eigenvalue crosses the boundary of stability at 1. In a perioddoubling bifurcation point, a period2 orbit bifurcates. You can find this type of bifurcation quite often in the Pendulum Lab. 
Crossing the boundary of stability only indicates a bifurcation point and the type of the bifurcating solutions. But it doesn't tell you how and how many new solutions bifurcate or disappear in a bifurcation point. To answer this question one has to take into account the leading nonlinear terms. This might appear to be a difficult task. But fortunately, a theorem and a transformation help us simplifying this analysis:
Below, the main normal forms (for continuous dynamical systems) of stationary codimensionone bifurcations are shown. The phase space variable is u. The control parameter is . The bifurcation point is at = 0. The direction of motion in the onedimensional phase space is shown by arrows. Stable (unstable) fixed points are drawn as red solid (dotted) lines. The normal forms are shown in blue.
The generic case of stationary codimensionone bifurcation is a saddlenode bifurcation. It is generic (that is, the probability is finite to pick a dynamical system having a saddlenode bifurcation) because fixed points lie on a smooth onedimensional manifold in the combined space of phase space and control parameter. The minima and maxima of as a function of the curve length denote saddlenode bifurcations where a stable fixed point (a node) annihilates with an unstable one (a saddle in more than one dimension). A combination of a minimum and a maximum lead to the phenomenon of bistability where in a certain interval of the control parameter, two stable attractors exist with an unstable one inbetween. Bistability is responsible for hysteresis in many physical and technical systems, as for example the button on your computer mouse. This phenomenon occurs if one sweeps the control parameter relatively slowly beyond the interval of bistability. When the interval of bistability is left the attractor disappears in a saddlenode bifurcation, and the system suddenly jumps to another attractor. For the example of a mouse button, this means that the button makes click even if pressing the button extremely slowly (try it!). Now sweeping the control parameter in the other direction has no effect when the bistability interval is reentered. Thus, the mouse button remains in the pressed state even if the pressure is diminished below the pressure necessary for the previous click. Leaving the bistability interval at its other end, the system has to jump towards the former attractor. And the mouse button makes again click (maybe it sounds different than the first click). In the Pendulum Lab, bistability occurs e.g. in the foldover effect. 
The transcritical bifurcation occurs when in the combined space of phase space and controlparameter space two different manifolds of fixed points cross each other. At the crossing point the fixed points exchange there stability property. That is, the unstable fixed point becomes stable and vice versa. Note, that beyond the bifurcation point the number of fixed points has not changed contrary to saddlenode bifurcation where two fixed points appear or disappear. A transcritical bifurcation is not a generic bifurcation in a phase space with more than one dimension because it is unlikely that two lines cross each other in a space with more than two dimensions. 
Pitchfork bifurcations are possible in dynamical
systems with an inversion or reflection symmetry.
That is, an equation of motion that remains
unchanged if one changes the sign of all phase space variables (or at least for one). An example is the undriven
pendulum. Usually, such a system has a symmetric fixed point (or limit cycle). Pitchfork bifurcations are the
generic bifurcations when such a symmetric solution changes its stability. The bifurcating solution does not have
the full symmetry of the equation of motion. This phenomenon is called broken symmetry. A solution with a broken
symmetry does not occur in isolation because the broken symmetry applied onto such a solution generates a new solution where
the same symmetry is broken. All such solutions build a family. Therefore, always two fixed
points with a broken symmetry bifurcate at once in a pitchfork bifurcation. Both are either stable (supercritical pitchfork
bifurcation) or unstable (subcritical pitchfork bifurcation). In the Pendulum Lab, pitchfork bifurcations of limit cycles occur quite often. As an example, try the following experiment: Shake a horizontally driven pendulum faster than its eigen frequency. It will show a tiny oscillation around the equilibrium value = 0. This limit cycle becomes unstable if the driving frequency is much larger than the eigenfrequency. At the bifurcation point, two new stable limit cycles bifurcate supercritically. Their centers are on the right and on the left hand of the unstable symmetric limit cycle. 
The bifurcation diagrams of a Hopf and a period doubling bifurcation are similar to the diagram of a pitchfork bifurcation. That is, the bifurcating periodic or quasiperiodic solution is either stable (supercritical bifurcation) or unstable (subcritical bifurcation). Again, a broken symmetry is responsible for this similarity. Here, it is the invariance of the dynamical system against translations in time.
QUESTIONS worth to think about: 
