Module: stateData

Object that is to be used by an integrator. It’s basically an interface with only one method: the \(F\) function describing a dynamic model \(\dot X = F(X,t)\).

class StateData

#include <stateData.h>

Represents a physical state, which has a name, a value, and a derivative.

Public Functions

StateData(std::string inName, const Eigen::MatrixXd &newState)

Creates a new state with the given name and set’s the initial state.

The state derivative will be resized to the same size as the state and zero’d.

virtual std::unique_ptr<StateData> clone() const: Clone constructor for polymorphic class

virtual ~StateData() = default: Destructor

void setNumNoiseSources(size_t numSources)

Sets the number of noise sources for this state.

This is used for stochastic dynamics, where the evolutions of the state are driven by a set of independent noise sources:

\[ dx = f(t,x)\,dt + g_0(t,x)\,dW_0 + g_1(t,x)\,dW_1 + \cdots + g_{n-1}(t,x)\,dW_{n-1} \]

where \(dW_i\) are independent Wiener processes. The number of noise sources is equal to the number of diffusion matrices that are used to drive the stochastic dynamics (n above).

Parameters:: numSources – The number of noise sources

size_t getNumNoiseSources() const

Get how many independent sources of noise drive the dynamics of this state.

Any number greater than zero indicates that this state is driven by a stochastic differential equation.

void setState(const Eigen::MatrixXd &newState): Updates the value of the state

void setDerivative(const Eigen::MatrixXd &newDeriv): Updates the derivative of the value of the state

void setDiffusion(const Eigen::MatrixXd &newDiffusion, size_t index)

Updates the diffusion of the value of the state.

This is used for stochastic dynamics, where the evolutions of the state are driven by a set of independent noise sources:

\[ dx = f(t,x)\,dt + g_0(t,x)\,dW_0 + g_1(t,x)\,dW_1 + \cdots + g_{n-1}(t,x)\,dW_{n-1} \]

where \(dW_i\) are independent Wiener processes. The diffusion matrices are used to drive the stochastic dynamics ( \(g_i\) above).

Parameters:

newDiffusion – The new diffusion matrix
index – The index of the diffusion matrix to update. This must be less than the number of noise sources.

inline Eigen::MatrixXd getState() const: Retrieves a copy of the current state

inline Eigen::MatrixXd getStateDeriv() const: Retrieves a copy of the current state derivative

inline Eigen::MatrixXd getStateDiffusion(size_t index) const

Retrieves a copy of the current state diffusion

Parameters:: index – The index of the diffusion matrix to retrieve. This must be less than the number of noise sources.

inline std::string getName() const: Returns the name of the state

inline uint32_t getRowSize() const: Returns the row-size of the state

inline uint32_t getColumnSize() const: Returns the column-size of the state

inline uint32_t getDerivativeRowSize() const: Returns the row-size of the derivative of the state

inline uint32_t getDerivativeColumnSize() const: Returns the column-size of the derivative of the state

void scaleState(double scaleFactor): Multiples the state by a scalar

void addState(const StateData &other): Adds the values of the other state to this state

virtual void propagateState(double h, std::vector<double> pseudoStep = {})

Propagates the state over a time step.

This method integrates the position state using the state derivative over the given time step::

\[ x \mathrel{+}= f(t,x)\,h + g_0(t,x)\,\mathrm{pseudoStep}[0] + g_1(t,x)\,\mathrm{pseudoStep}[1] + \cdots \]

Parameters:

h – The time step for propagation.
pseudoStep – For states driven by stochastic dynamics, this represents the random pseudotimestep. The length of this input must match the number of noise sources of this state (getNumNoiseSources())

Public Members

Eigen::MatrixXd state: [-] State value storage

Eigen::MatrixXd stateDeriv: [-] State derivative value storage

std::vector<Eigen::MatrixXd> stateDiffusion: [-] State diffusion value storage

const std::string stateName: [-] Name of the state

BSKLogger bskLogger: — BSK Logging