This paper illustrates the application of recent research in
region-of-attraction analysis for nonlinear hybrid limit cycles. Three example
systems are analyzed in detail: the van der Pol oscillator, the "rimless
wheel", and the "compass gait", the latter two being simplified models of
underactuated walking robots. The method used involves decomposition of the
dynamics about the target cycle into tangential and transverse components, and
a search for a Lyapunov function in the transverse dynamics using
sum-of-squares analysis (semidefinite programming).
A new framework for nonlinear system identification is presented in terms of
optimal fitting of stable nonlinear state space equations to input/output/state
data, with a performance objective defined as a measure of robustness of the
simulation error with respect to equation errors. Basic definitions and
analytical results are presented. The utility of the method is illustrated on a
simple simulation example as well as experimental recordings from a live
neuron.