InvertMotorController Class Reference

Abstract class (interface) for robot controller that use direct matrix inversion and a time loop in motor domain. More...

#include <invertmotorcontroller.h>

Inherits AbstractController.

Inherited by BasicController, InvertMotorBigModel, InvertMotorNStep, and InvertMotorSpace.

Inheritance diagram for InvertMotorController:

[legend]Collaboration diagram for InvertMotorController:

[legend]List of all members.

Public Member Functions
	InvertMotorController (unsigned short buffersize, const std::string &name, const std::string &revision)
virtual paramval	getParam (const paramkey &key) const
virtual bool	setParam (const paramkey &key, paramval val)
virtual paramlist	getParamList () const
	The list of all parameters with there value as allocated lists.
Protected Member Functions
void	putInBuffer (matrix::Matrix *buffer, const matrix::Matrix &vec, int delay=0)
virtual matrix::Matrix	calculateDelayedValues (const matrix::Matrix *buffer, int number_steps_of_delay_)
	calculate delayed values
virtual matrix::Matrix	calculateSmoothValues (const matrix::Matrix *buffer, int number_steps_for_averaging_)
	calculate time-smoothed values
virtual double	calcErrorFactor (const matrix::Matrix &e, bool loga, bool root)
	calculates the error_factor for either logarithmic (E=ln(e^T*e)) or square (E=sqrt(e^t*e)) error
Static Protected Member Functions
static double	g (double z)
	neuron transfer function
Protected Attributes
paramval	epsC
	learning rate factor for controller learning
paramval	desens
paramval	s4delay
	number of timesteps of delay in the SML
paramval	s4avg
	number of timesteps used for smoothing the controller output values
paramval	steps
	number of timesteps is used for controller learning
paramval	epsA
	learning rate factor for model learning
paramval	factorB
	additional learning rate factor for model bias
paramval	zetaupdate
paramval	dampA
	damping term for model (0: no damping, 0.1 high damping)
short	logaE
	logarithmic error is used for learning 1: controller 2: model 3: both
short	rootE
	root error is used for learning 1: controller 2: model 3: both
short	relativeE
	if not 0: a relative error signal is used (xsi is normalised in respect to |y|)
paramval	squashSize
	size of the box, where the parameter updates are clipped to
paramval	adaptRate
	adaptation rate for learning rate adaptation
paramval	nomUpdate
	nominal update of controller in respect to matrix norm
paramval	noiseB
	size of the additional noise for model bias B
paramval	noiseY
	size of the additional noise for motor values
paramval	teacher
	factor for teaching signal
int	t
unsigned short	buffersize
bool	initialised

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Detailed Description

Abstract class (interface) for robot controller that use direct matrix inversion and a time loop in motor domain.

Implements standart configureable interface for some useful parameters like epsC, epsA, s4avg ...

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Constructor & Destructor Documentation

InvertMotorController	(	unsigned short	buffersize,
		const std::string &	name,
		const std::string &	revision
	)			[inline]

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Member Function Documentation

virtual double calcErrorFactor	(	const matrix::Matrix &	e,
		bool	loga,
		bool	root
	)			[inline, protected, virtual]

calculates the error_factor for either logarithmic (E=ln(e^T*e)) or square (E=sqrt(e^t*e)) error

virtual matrix::Matrix calculateDelayedValues	(	const matrix::Matrix *	buffer,
		int	number_steps_of_delay_
	)			[inline, protected, virtual]

calculate delayed values

virtual matrix::Matrix calculateSmoothValues	(	const matrix::Matrix *	buffer,
		int	number_steps_for_averaging_
	)			[inline, protected, virtual]

calculate time-smoothed values

static double g

(

double

z

)

[inline, static, protected]

neuron transfer function

virtual paramval getParam

(

const paramkey &

key

)

const [inline, virtual]

Reimplemented in InvertMotorBigModel.

virtual paramlist getParamList

(

)

const [inline, virtual]

The list of all parameters with there value as allocated lists.

Returns:

list of key-value pairs

Reimplemented from Configurable.

Reimplemented in InvertMotorBigModel.

void putInBuffer	(	matrix::Matrix *	buffer,
		const matrix::Matrix &	vec,
		int	delay = 0
	)			[inline, protected]

virtual bool setParam	(	const paramkey &	key,
		paramval	val
	)			[inline, virtual]

Reimplemented in BasicController, and InvertMotorBigModel.

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Member Data Documentation

paramval adaptRate [protected]

adaptation rate for learning rate adaptation

unsigned short buffersize [protected]

paramval dampA [protected]

damping term for model (0: no damping, 0.1 high damping)

paramval desens [protected]

paramval epsA [protected]

learning rate factor for model learning

paramval epsC [protected]

learning rate factor for controller learning

paramval factorB [protected]

additional learning rate factor for model bias

bool initialised [protected]

short logaE [protected]

logarithmic error is used for learning 1: controller 2: model 3: both

paramval noiseB [protected]

size of the additional noise for model bias B

paramval noiseY [protected]

size of the additional noise for motor values

Reimplemented in InvertMotorNStep.

paramval nomUpdate [protected]

nominal update of controller in respect to matrix norm

short relativeE [protected]

if not 0: a relative error signal is used (xsi is normalised in respect to |y|)

short rootE [protected]

root error is used for learning 1: controller 2: model 3: both

paramval s4avg [protected]

number of timesteps used for smoothing the controller output values

paramval s4delay [protected]

number of timesteps of delay in the SML

paramval squashSize [protected]

size of the box, where the parameter updates are clipped to

paramval steps [protected]

number of timesteps is used for controller learning

int t [protected]

paramval teacher [protected]

factor for teaching signal

paramval zetaupdate [protected]

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The documentation for this class was generated from the following file:

• invertmotorcontroller.h

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