BasicController Class Reference

class for robot controller is based on InvertMotorNStep More...

#include <basiccontroller.h>

Inheritance diagram for BasicController:

[legend]Collaboration diagram for BasicController:


Public Member Functions
	BasicController (const BasicControllerConf &conf=getDefaultConf())
virtual void	init (int sensornumber, int motornumber, RandGen *randGen=0)
	initialisation of the controller with the given sensor/ motornumber Must be called before use.
virtual	~BasicController ()
virtual int	getSensorNumber () const
	returns the number of sensors the controller was initialised with or 0 if not initialised
virtual int	getMotorNumber () const
	returns the mumber of motors the controller was initialised with or 0 if not initialised
virtual void	step (const sensor , int number_sensors, motor , int number_motors)
	performs one step (includes learning).
virtual void	stepNoLearning (const sensor , int number_sensors, motor , int number_motors)
	performs one step without learning. Calulates motor commands from sensor inputs.
virtual bool	store (FILE *f) const
	stores the controller values to a given file.
virtual bool	restore (FILE *f)
	loads the controller values from a given file.
virtual iparamkeylist	getInternalParamNames () const
	The list of the names of all internal parameters given by getInternalParams().
virtual iparamvallist	getInternalParams () const
virtual ilayerlist	getStructuralLayers () const
	Specifies which parameter vector forms a structural layer (in terms of a neural network) The ordering is important.
virtual iconnectionlist	getStructuralConnections () const
	Specifies which parameter matrix forms a connection between layers (in terms of a neural network) The orderning is not important.
virtual bool	setParam (const paramkey &key, paramval val)
void	getLastMotors (motor *motors, int len)
virtual double	calcMatrixNorm (const matrix::Matrix &m)
	calculates the city block distance (abs) norm of the matrix. (abs sum of absolutes / size of matrix)
Static Public Member Functions
static BasicControllerConf	getDefaultConf ()
	The given sensor teaching signal (distal learning) is used for this timestep.
Protected Member Functions
virtual void	fillBuffersAndControl (const sensor x_, int number_sensors, motor y_, int number_motors)
	puts the sensors in the ringbuffer, generate controller values and put them in the
virtual void	learnController (int delay)
	learn values H,C This is the implementation uses a better formula for g^-1 using Mittelwertsatz
virtual void	learnModel (int delay)
	learn conf.model, (and S) using motors y and corresponding sensors x
virtual matrix::Matrix	calculateControllerValues (const matrix::Matrix &x_smooth)
	returns controller output for given sensor values
matrix::Matrix	calcDerivatives (const matrix::Matrix *buffer, int delay)
	Calculates first and second derivative and returns both in on matrix (above).
Protected Attributes
unsigned short	number_sensors
unsigned short	number_motors
matrix::Matrix	A
	Model Matrix (motors to sensors).
matrix::Matrix	A_Hat
	Model Matrix (motors to sensors) with input shift.
matrix::Matrix	S
	additional Model Matrix (sensors to sensors)
matrix::Matrix	C
	Controller Matrix.
matrix::Matrix	GSC
	G_Prime times Controller Matrix.
matrix::Matrix	DD
	Noise Matrix.
matrix::Matrix	Dinverse
	Inverse Noise Matrix.
matrix::Matrix	H
	Controller Bias.
matrix::Matrix	B
	Model Bias.
NoiseGenerator *	BNoiseGen
	Noisegenerator for noisy bias.
NoiseGenerator *	YNoiseGen
	Noisegenerator for noisy motor values.
matrix::Matrix	R
	C*A.
matrix::Matrix	RRT_inv
matrix::Matrix	ATA_inv
matrix::Matrix	Rm1
	R^-1.
matrix::Matrix	ID
	identity matrix in the dimension of R
matrix::Matrix	ID_Sensor
	identity matrix in the dimension of sensor space
matrix::Matrix	CST
matrix::Matrix	CCT_inv
matrix::Matrix	xsi
	current output error
double	xsi_norm
	norm of matrix
double	xsi_norm_avg
	average norm of xsi (used to define whether Modell learns)
double	pain
	if the modelling error (xsi) is too high we have a pain signal
matrix::Matrix *	x_buffer
matrix::Matrix *	y_buffer
matrix::Matrix *	eta_buffer
matrix::Matrix	eta
matrix::Matrix	v_smooth
matrix::Matrix	zero_eta
matrix::Matrix	x_smooth
matrix::Matrix	y_smooth
matrix::Matrix	eta_smooth
matrix::Matrix	x_smooth_long
matrix::Matrix	y_teaching
	teaching motor signal
bool	useTeaching
	flag whether there is an actual teachning signal or not
matrix::Matrix	x_intern
	fantasy sensor values
int	fantControl
	interval length for fantasising
int	fantControlLen
	length of fantasy control
int	fantReset
	number of fantasy control events before reseting internal state
int	t_rand
	initial random time to avoid syncronous management of all controllers
int	managementInterval
	interval between subsequent management function calls
paramval	dampS
	damping of S matrix
paramval	dampC
	damping of C matrix
paramval	dampH
	damping of H vector
paramval	weighting
	general weighting fator between update concepts
BasicControllerConf	conf

Detailed Description

class for robot controller is based on InvertMotorNStep

direct inversion

motor space

multilayer,nonlinear model

Constructor & Destructor Documentation

BasicController ( const BasicControllerConf & conf = getDefaultConf() )

~BasicController ( ) [virtual]

Member Function Documentation

Matrix calcDerivatives	(	const matrix::Matrix *	buffer,
		int	delay
	)			`[protected]`

Calculates first and second derivative and returns both in on matrix (above).

We use simple discrete approximations:

$f'(x) = (f(x) - f(x-1)) / 2$

$f''(x) = f(x) - 2f(x-1) + f(x-2)$

where we have to go into the past because we do not have f(x+1). The scaling can be neglegted.

double calcMatrixNorm ( const matrix::Matrix & m ) [virtual]

calculates the city block distance (abs) norm of the matrix. (abs sum of absolutes / size of matrix)

Matrix calculateControllerValues ( const matrix::Matrix & x_smooth ) [protected, virtual]

returns controller output for given sensor values

Parameters:

x_smooth

smoothed sensors Matrix(number_channels,1)

void fillBuffersAndControl	(	const sensor *	x_,
		int	number_sensors,
		motor *	y_,
		int	number_motors
	)			`[protected, virtual]`

puts the sensors in the ringbuffer, generate controller values and put them in the

static BasicControllerConf getDefaultConf ( ) [inline, static]

The given sensor teaching signal (distal learning) is used for this timestep.

First the belonging motor teachung signal is calculated by the inverse model. See setMotorTeachingSignal

list< Inspectable::iparamkey > getInternalParamNames ( ) const [virtual]

The list of the names of all internal parameters given by getInternalParams().

The naming convention is "v[i]" for vectors and "A[i][j]" for matrices, where i, j start at 0.

Returns:: : list of keys

Reimplemented from Inspectable.

list< Inspectable::iparamval > getInternalParams ( ) const [virtual]

Returns:: : list of values

Reimplemented from Inspectable.

void getLastMotors	(	motor *	motors,
		int	len
	)

virtual int getMotorNumber ( ) const [inline, virtual]

returns the mumber of motors the controller was initialised with or 0 if not initialised

Implements AbstractController.

virtual int getSensorNumber ( ) const [inline, virtual]

returns the number of sensors the controller was initialised with or 0 if not initialised

Implements AbstractController.

list< Inspectable::IConnection > getStructuralConnections ( ) const [virtual]

Specifies which parameter matrix forms a connection between layers (in terms of a neural network) The orderning is not important.

Returns:: : list of layer names with dimension

Reimplemented from Inspectable.

list< Inspectable::ILayer > getStructuralLayers ( ) const [virtual]

Specifies which parameter vector forms a structural layer (in terms of a neural network) The ordering is important.

The first entry is the input layer and so on.

Returns:: : list of layer names with dimension

Reimplemented from Inspectable.

void init	(	int	sensornumber,
		int	motornumber,
		RandGen *	randGen = `0`
	)			`[virtual]`

initialisation of the controller with the given sensor/ motornumber Must be called before use.

The random generator is optional.

Implements AbstractController.

void learnController ( int delay ) [protected, virtual]

learn values H,C This is the implementation uses a better formula for g^-1 using Mittelwertsatz

Parameters:

delay

0 for no delay and n>0 for n timesteps delay in the SML (s4delay)

void learnModel ( int delay ) [protected, virtual]

learn conf.model, (and S) using motors y and corresponding sensors x

bool restore ( FILE * f ) [virtual]

loads the controller values from a given file.

Implements Storeable.

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

Reimplemented from InvertMotorController.

void step	(	const sensor *	,
		int	number_sensors,
		motor *	,
		int	number_motors
	)			`[virtual]`

performs one step (includes learning).

Calulates motor commands from sensor inputs.

Implements AbstractController.

void stepNoLearning	(	const sensor *	,
		int	number_sensors,
		motor *	,
		int	number_motors
	)			`[virtual]`

performs one step without learning. Calulates motor commands from sensor inputs.

Implements AbstractController.

bool store ( FILE * f ) const [virtual]

stores the controller values to a given file.

Implements Storeable.

Member Data Documentation

matrix::Matrix A [protected]

Model Matrix (motors to sensors).

matrix::Matrix A_Hat [protected]

Model Matrix (motors to sensors) with input shift.

matrix::Matrix ATA_inv [protected]

matrix::Matrix B [protected]

Model Bias.

NoiseGenerator* BNoiseGen [protected]

Noisegenerator for noisy bias.

matrix::Matrix C [protected]

Controller Matrix.

matrix::Matrix CCT_inv [protected]

BasicControllerConf conf [protected]

matrix::Matrix CST [protected]

paramval dampC [protected]

damping of C matrix

paramval dampH [protected]

damping of H vector

paramval dampS [protected]

damping of S matrix

matrix::Matrix DD [protected]

Noise Matrix.

matrix::Matrix Dinverse [protected]

Inverse Noise Matrix.

matrix::Matrix eta [protected]

matrix::Matrix* eta_buffer [protected]

matrix::Matrix eta_smooth [protected]

int fantControl [protected]

interval length for fantasising

int fantControlLen [protected]

length of fantasy control

int fantReset [protected]

number of fantasy control events before reseting internal state

matrix::Matrix GSC [protected]

G_Prime times Controller Matrix.

matrix::Matrix H [protected]

Controller Bias.

matrix::Matrix ID [protected]

identity matrix in the dimension of R

matrix::Matrix ID_Sensor [protected]

identity matrix in the dimension of sensor space

int managementInterval [protected]

interval between subsequent management function calls

unsigned short number_motors [protected]

unsigned short number_sensors [protected]

double pain [protected]

if the modelling error (xsi) is too high we have a pain signal

matrix::Matrix R [protected]

C*A.

matrix::Matrix Rm1 [protected]

R^-1.

matrix::Matrix RRT_inv [protected]

matrix::Matrix S [protected]

additional Model Matrix (sensors to sensors)

int t_rand [protected]

initial random time to avoid syncronous management of all controllers

bool useTeaching [protected]

flag whether there is an actual teachning signal or not

matrix::Matrix v_smooth [protected]

paramval weighting [protected]

general weighting fator between update concepts

matrix::Matrix* x_buffer [protected]

matrix::Matrix x_intern [protected]

fantasy sensor values

matrix::Matrix x_smooth [protected]

matrix::Matrix x_smooth_long [protected]

matrix::Matrix xsi [protected]

current output error

double xsi_norm [protected]

norm of matrix

double xsi_norm_avg [protected]

average norm of xsi (used to define whether Modell learns)

matrix::Matrix* y_buffer [protected]

matrix::Matrix y_smooth [protected]

matrix::Matrix y_teaching [protected]

teaching motor signal

NoiseGenerator* YNoiseGen [protected]

Noisegenerator for noisy motor values.

matrix::Matrix zero_eta [protected]

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

Generated on Tue Sep 16 22:01:21 2008 for Robotsystem of the Robot Group Leipzig by

1.4.7

BasicController Class Reference

Public Member Functions

Static Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation