Drake

This page describes the implementation details of modeling a stochastic system in Drake and writing algorithms that explicitly leverage the stochastic modeling framework.
A system in Drake can be viewed as having the statespace dynamics
x[n+1] = f(p; n, x[n], u[n], w[n]),
y[n] = g(p; n, x[n], u[n], w[n]),
where n is the time index, x is the state, y is the output, u is the input, and p are the (constant) parameters. This form also calls out w explicitly as a random "disturbance" input. These random inputs are implemented and evaluated using exactly the same methods as the deterministic inputs; any input port can optionally be annotated as "random" when they are being declared (see System<T>::DeclareInputPort() ). Once randomness exists in a system, many signals will throughout the diagram become random variables, but this label is used to denote the "point of entry" for an independent random variable.
Note: For simplicity, I've written only a simple discretetime system form above, but the same model holds for continuoustime systems, and multirate systems, and systems with multiple input/output ports as well, precisely because w is treated exactly as an additional input throughout the system classes. Continuoustime random signals must be treated with some care; see internal::RandomSource for details on how they are treated for the purpopses of simulation.
The rule in Drake is that every method that can be called during the lifetime of a simulation, (e.g. calculating discrete updates, time derivatives, and/or outputs) must be a completely deterministic function. Any randomness must come in through a random input port. The only exception to this rule is the one speciallyimplemented internal::RandomSource system, which goes to some length to store the state of the random number generator in its Context so that all simulation and analysis methods are deterministic given a Context. In almost every application, random input ports will be wired up to internal::RandomSource system blocks; we have provided the AddRandomInputs() method to facilitate this.
Algorithms written for Systems can query the property of the InputPort to find the input ports that are labeled as random, and the random vector distribution type. The list of supported distributions for random input ports is intentionally very limited (to simplify algorithm development); we place the burden on the System author to e.g. transform a Gaussian random input with zero mean and unit covariance into the desired shape inside the update and output methods.
In order to specify distributions over random initial conditions and random parameters, System classes may override the methods System<T>::SetRandomState() and System<T>::SetRandomParameters(). Algorithms written for systems may call System<T>::SetRandomContext() (which calls both of these). The implementations of System<T>::SetRandomState() and System<T>::SetRandomParameters() are expected to call the random number generators in the C++ Standard Library.