mx::AO::analysis::clGainOpt< _realT > Struct Template Reference

template<typename _realT>
struct mx::AO::analysis::clGainOpt< _realT >

A class to manage optimizing closed-loop gains.

Template Parameters

_realT

the real floating point type in which to do all arithmetic. Is used to define the complex type as well.

Definition at line 63 of file clGainOpt.hpp.

#include <ao/analysis/clGainOpt.hpp>

Public Types
typedef _realT	realT
	The real data type.
typedef std::complex< _realT >	complexT
	The complex data type.

Public Member Functions
	clGainOpt ()
	Default c'tor.
	clGainOpt (realT Ti, realT tau)
	C'tor setting the loop timings.
void	init ()
	Initialize this instance.
int	N ()
	Get the number of integrations in the (optional) moving average.
void	N (int newN)
	Set the number of integrations in the moving average.
realT	Ti ()
	Get the loop sampling interval.
void	Ti (realT newTi)
	Set the loop sampling interval.
realT	tau ()
	Get the loop delay.
void	tau (realT newTau)
	Set the loop delay.
void	b (const std::vector< realT > &newB)
	Set the vector of FIR coefficients.
void	b (const Eigen::Array< realT, -1, -1 > &newB)
	Set the vector of FIR coefficients.
realT	b (size_t i)
	Get a single FIR coefficient.
const std::vector< realT > &	b ()
	Get the vector of FIR coefficients.
void	a (const std::vector< realT > &newA)
	Set the vector of IIR coefficients.
void	a (const Eigen::Array< realT, -1, -1 > &newA)
	Set the vector of IIR coefficients.
realT	a (size_t i)
	Get a single IIR coefficient.
const std::vector< realT > &	a ()
	Get the vector of IIR coefficients.
void	remember (const realT &rem)
	Set the remember factor for a leaky integrator.
realT	remember ()
	Get the remember factor.
void	setLeakyIntegrator (realT remember)
	Set the FIR and IIR coefficients so that the control law is a leaky integrator.
void	f (realT *newF, size_t nF)
	Set the vector of frequencies.
void	f (std::vector< realT > &newF)
	Set the vector of frequencies.
size_t	f_size ()
	Get the size of the frequency vector.
realT	f (size_t i)
	Get the i-th value of frequency.
complexT	olXfer (int fi, complexT &H_dm, complexT &H_del, complexT &H_con)
	Calculate the open-loop transfer function.
complexT	olXfer (int fi)
	Calculate the open-loop transfer function.
complexT	clETF (int fi, realT g)
	Return the closed loop error transfer function (ETF) at frequency f for gain g.
realT	clETFPhase (int fi, realT g)
	Return the closed loop error transfer function (ETF) phase at frequency f for gain g.
realT	clETF2 (int fi, realT g)
	Return the norm of the closed loop error transfer function (ETF) at frequency f for gain g.
complexT	clNTF (int fi, realT g)
	Return the closed loop noise transfer function (NTF) at frequency f for gain g.
realT	clNTF2 (int fi, realT g)
	Return the norm of the closed loop noise transfer function (NTF) at frequency f for gain g.
void	clTF2 (realT &ETF, realT &NTF, int fi, realT g)
	Return the norm of the closed loop transfer functions at frequency f for gain g.
realT	clVariance (realT &varErr, realT &varNoise, const std::vector< realT > &PSDerr, const std::vector< realT > &PSDnoise, realT g)
	Calculate the closed loop variance given open-loop PSDs and gain.
realT	clVariance (const std::vector< realT > &PSDerr, const std::vector< realT > &PSDnoise, realT g)
	Calculate the closed loop variance given open-loop PSDs and gain.
realT	maxStableGain (realT &ll, realT &ul)
	Find the maximum stable gain for the loop parameters.
realT	maxStableGain (const realT &ll, const realT &ul)
	Find the maximum stable gain for the loop parameters.
realT	maxStableGain ()
	Find the maximum stable gain for the loop parameters.
realT	optGainOpenLoop (realT &var, const std::vector< realT > &PSDerr, const std::vector< realT > &PSDnoise, bool gridSearch)
	Return the optimum closed loop gain given an open loop PSD.
realT	optGainOpenLoop (realT &var, const std::vector< realT > &PSDerr, const std::vector< realT > &PSDnoise, realT &gmax, bool gridSearch)
	Return the optimum closed loop gain given an open loop PSD.
int	pseudoOpenLoop (std::vector< realT > &PSD, realT g)
	Calculate the pseudo open-loop PSD given a closed loop PSD.

Protected Attributes
int	m_N
	Number of integrations in the (optional) moving average. Default is 1.
realT	m_Ti
	The loop sampling interval.
realT	m_tau
	The loop delay.
realT	m_remember { 1.0 }
	The leaky integrator forget factor.
std::vector< realT >	m_b
	Vector of FIR coefficients.
std::vector< realT >	m_a
	Vector of IIR coefficients.
std::vector< realT >	m_f
	Vector of frequencies.
bool	m_fChanged { true }
	True if frequency or max size of m_a and m_b changes.
bool	m_changed { true }
	True if any of the members which make up the basic transfer functions are changed.

Member Typedef Documentation

complexT

template<typename _realT >

typedef std::complex<_realT> mx::AO::analysis::clGainOpt< _realT >::complexT

The complex data type.

Definition at line 66 of file clGainOpt.hpp.

realT

template<typename _realT >

typedef _realT mx::AO::analysis::clGainOpt< _realT >::realT

The real data type.

Definition at line 65 of file clGainOpt.hpp.

Constructor & Destructor Documentation

clGainOpt() [1/2]

template<typename realT >

mx::AO::analysis::clGainOpt< realT >::clGainOpt

(

)

Default c'tor.

Definition at line 440 of file clGainOpt.hpp.

clGainOpt() [2/2]

template<typename realT >

mx::AO::analysis::clGainOpt< realT >::clGainOpt	(	realT	Ti,
		realT	tau
	)

C'tor setting the loop timings.

Parameters

[in]	Ti	the desired loop sampling interval.
[in]	tau	the desired loop delay.

Definition at line 446 of file clGainOpt.hpp.

Member Function Documentation

a() [1/4]

template<typename _realT >

const std::vector< realT > & mx::AO::analysis::clGainOpt< _realT >::a ( )

inline

Get the vector of IIR coefficients.

Definition at line 212 of file clGainOpt.hpp.

References mx::AO::analysis::clGainOpt< _realT >::m_a.

a() [2/4]

template<typename realT >

void mx::AO::analysis::clGainOpt< realT >::a

(

const Eigen::Array< realT, -1, -1 > &

newA

)

Set the vector of IIR coefficients.

Parameters

[in]

newA

a column-vector Eigen::Array of coefficients, which is copied to m_a.

Definition at line 583 of file clGainOpt.hpp.

a() [3/4]

template<typename realT >

void mx::AO::analysis::clGainOpt< realT >::a

(

const std::vector< realT > &

newA

)

Set the vector of IIR coefficients.

Parameters

[in]

newA

a vector of coefficients, which is copied to m_a.

Definition at line 571 of file clGainOpt.hpp.

Referenced by mx::AO::analysis::clAOLinearPredictor< _realT >::_regularizeCoefficients(), mx::AO::analysis::fourierTemporalPSD< _realT, aosysT >::analyzePSDGrid(), and mx::AO::analysis::clAOLinearPredictor< _realT >::regularizeCoefficients().

a() [4/4]

template<typename _realT >

realT mx::AO::analysis::clGainOpt< _realT >::a ( size_t  i )

inline

Get a single IIR coefficient.

Returns

a single IIR coefficient

Definition at line 204 of file clGainOpt.hpp.

References mx::AO::analysis::clGainOpt< _realT >::m_a.

b() [1/4]

template<typename _realT >

const std::vector< realT > & mx::AO::analysis::clGainOpt< _realT >::b ( )

inline

Get the vector of FIR coefficients.

Definition at line 183 of file clGainOpt.hpp.

References mx::AO::analysis::clGainOpt< _realT >::m_b.

b() [2/4]

template<typename realT >

void mx::AO::analysis::clGainOpt< realT >::b

(

const Eigen::Array< realT, -1, -1 > &

newB

)

Set the vector of FIR coefficients.

Parameters

[in]

newB

a column-vector Eigen::Array of coefficients, which is copied to m_b.

Definition at line 542 of file clGainOpt.hpp.

b() [3/4]

template<typename realT >

void mx::AO::analysis::clGainOpt< realT >::b

(

const std::vector< realT > &

newB

)

Set the vector of FIR coefficients.

Parameters

[in]

newB

a vector of coefficients, which is copied to m_b.

Definition at line 530 of file clGainOpt.hpp.

Referenced by mx::AO::analysis::clAOLinearPredictor< _realT >::_regularizeCoefficients(), mx::AO::analysis::fourierTemporalPSD< _realT, aosysT >::analyzePSDGrid(), and mx::AO::analysis::clAOLinearPredictor< _realT >::regularizeCoefficients().

b() [4/4]

template<typename _realT >

realT mx::AO::analysis::clGainOpt< _realT >::b ( size_t  i )

inline

Get a single FIR coefficient.

Returns

a single FIR coefficient

Parameters

[in]

i

the index of the FIR coefficient

Definition at line 175 of file clGainOpt.hpp.

References mx::AO::analysis::clGainOpt< _realT >::m_b.

clETF()

template<typename realT >

std::complex< realT > mx::AO::analysis::clGainOpt< realT >::clETF	(	int	fi,
		realT	g
	)

Return the closed loop error transfer function (ETF) at frequency f for gain g.

Returns

the closed loop ETF at f and g.

Parameters

[in]	fi	the index of the frequency at which to calculate the ETF
[in]	g	the loop gain.

Definition at line 844 of file clGainOpt.hpp.

Referenced by mx::AO::analysis::fourierTemporalPSD< _realT, aosysT >::analyzePSDGrid().

clETF2()

template<typename realT >

realT mx::AO::analysis::clGainOpt< realT >::clETF2	(	int	fi,
		realT	g
	)

Return the norm of the closed loop error transfer function (ETF) at frequency f for gain g.

Returns

the norm of the closed loop ETF at f and g.

Parameters

[in]	fi	the index of the frequency at which to calculate the ETF.
[in]	g	the loop gain.

Definition at line 872 of file clGainOpt.hpp.

clETFPhase()

template<typename realT >

realT mx::AO::analysis::clGainOpt< realT >::clETFPhase	(	int	fi,
		realT	g
	)

Return the closed loop error transfer function (ETF) phase at frequency f for gain g.

Returns

the phase of the closed loop ETF at f and g.

Parameters

[in]	fi	the index of the frequency at which to calculate the ETF
[in]	g	the loop gain.

Definition at line 858 of file clGainOpt.hpp.

clNTF()

template<typename realT >

std::complex< realT > mx::AO::analysis::clGainOpt< realT >::clNTF	(	int	fi,
		realT	g
	)

Return the closed loop noise transfer function (NTF) at frequency f for gain g.

Returns

the closed loop NTF at f and g.

Parameters

[in]	fi	the index of the frequency at which to calculate the NTF
[in]	g	the loop gain.

Definition at line 886 of file clGainOpt.hpp.

Referenced by mx::AO::analysis::fourierTemporalPSD< _realT, aosysT >::analyzePSDGrid().

clNTF2()

template<typename realT >

realT mx::AO::analysis::clGainOpt< realT >::clNTF2	(	int	fi,
		realT	g
	)

Return the norm of the closed loop noise transfer function (NTF) at frequency f for gain g.

Returns

the value of the closed loop NTF at f and g.

Parameters

[in]	fi	the index of the frequency at which to calculate the NTF
[in]	g	the loop gain.

Definition at line 904 of file clGainOpt.hpp.

clTF2()

template<typename realT >

void mx::AO::analysis::clGainOpt< realT >::clTF2	(	realT &	ETF,
		realT &	NTF,
		int	fi,
		realT	g
	)

Return the norm of the closed loop transfer functions at frequency f for gain g.

Calculates both the error transfer function (ETF) and the noise transfer function (NTF). This minimizes the various complex number operations, compared to calling both clETF2 and clNTF2.

Parameters

[out]	ETF	is set to the ETF at f and g
[out]	NTF	is set to the NTF at f and g
[in]	fi	is the index of the frequency at which to calculate the ETF
[in]	g	is the loop gain.

Definition at line 924 of file clGainOpt.hpp.

Referenced by mx::AO::analysis::fourierTemporalPSD< _realT, aosysT >::analyzePSDGrid().

clVariance() [1/2]

template<typename realT >

realT mx::AO::analysis::clGainOpt< realT >::clVariance	(	const std::vector< realT > &	PSDerr,
		const std::vector< realT > &	PSDnoise,
		realT	g
	)

Calculate the closed loop variance given open-loop PSDs and gain.

Overload of clVariance without the varErr and varNoise output parameters.

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Returns

the total variance (error + noise) in closed loop

Parameters

[in]	PSDerr	the open-loop process error PSD.
[in]	PSDnoise	the open-loop measurement noise PSD.
[in]	g	the gain.

Definition at line 990 of file clGainOpt.hpp.

clVariance() [2/2]

template<typename realT >

realT mx::AO::analysis::clGainOpt< realT >::clVariance	(	realT &	varErr,
		realT &	varNoise,
		const std::vector< realT > &	PSDerr,
		const std::vector< realT > &	PSDnoise,
		realT	g
	)

Calculate the closed loop variance given open-loop PSDs and gain.

Calculates the following quantities.

\[ \sigma_{err}^2 = \sum_i \left| ETF(f_i) \right|^2 PSD_{err}(fi) \Delta f\\ \sigma_{noise}^2 = \sum_i \left| NTF(f_i) \right|^2 PSD_{noise}(fi) \Delta f\\ \sigma^2 = \sigma_{err}^2 + \sigma_{noise}^2 \]

\( \sigma^2 \) is returned, and \( \sigma_{err}^2 \) and \( \sigma_{noise}^2 \) are available as the optional arguments varErr and varNoise.

Returns

the total variance (error + noise) in closed loop

Parameters

[out]	varErr	the variance in the residual process error.
[out]	varNoise	the variance in the residual measurement noise.
[in]	PSDerr	the open-loop process error PSD.
[in]	PSDnoise	the open-loop measurement noise PSD.
[in]	g	the gain.

Definition at line 955 of file clGainOpt.hpp.

Referenced by mx::AO::analysis::fourierTemporalPSD< _realT, aosysT >::analyzePSDGrid().

f() [1/3]

template<typename realT >

void mx::AO::analysis::clGainOpt< realT >::f	(	realT *	newF,
		size_t	nF
	)

Set the vector of frequencies.

Parameters

[in]	newF	a pointer to an array containing the new frequencies
[in]	nF	the number of elements of size(realT) in newF.

Definition at line 658 of file clGainOpt.hpp.

Referenced by mx::AO::analysis::fourierTemporalPSD< _realT, aosysT >::analyzePSDGrid().

f() [2/3]

template<typename realT >

realT mx::AO::analysis::clGainOpt< realT >::f

(

size_t

i

)

Get the i-th value of frequency.

No range checks are conducted.

Returns

the value of m_f[i]

Parameters

[in]

i

the index of the frequency to return

Definition at line 680 of file clGainOpt.hpp.

f() [3/3]

template<typename realT >

void mx::AO::analysis::clGainOpt< realT >::f

(

std::vector< realT > &

newF

)

Set the vector of frequencies.

Parameters

[in]

newF

a vector containing the new frequencies

Definition at line 671 of file clGainOpt.hpp.

f_size()

template<typename _realT >

size_t mx::AO::analysis::clGainOpt< _realT >::f_size ( )

inline

Get the size of the frequency vector.

Returns

m_f.size()

Definition at line 248 of file clGainOpt.hpp.

References mx::AO::analysis::clGainOpt< _realT >::m_f.

init()

template<typename realT >

void mx::AO::analysis::clGainOpt< realT >::init

(

)

Initialize this instance.

Definition at line 455 of file clGainOpt.hpp.

maxStableGain() [1/3]

template<typename realT >

realT mx::AO::analysis::clGainOpt< realT >::maxStableGain

(

)

Find the maximum stable gain for the loop parameters.

Conducts a search along the Nyquist contour of the open-loop transfer function to find the most-negative crossing of the real axis.

This version uses m_maxFindMin for the lower limit and no upper limit.

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Returns

the maximum stable gain for the loop parameters

Definition at line 1038 of file clGainOpt.hpp.

maxStableGain() [2/3]

template<typename _realT >

realT mx::AO::analysis::clGainOpt< _realT >::maxStableGain	(	const realT &	ll,
		const realT &	ul
	)

Find the maximum stable gain for the loop parameters.

Conducts a search along the Nyquist contour of the open-loop transfer function to find the most-negative crossing of the real axis.

This version allows constant arguments. This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Returns

the maximum stable gain for the loop parameters

Parameters

[in]	ll	the lower limit used for the search
[in]	ul	the upper limit used for hte search

maxStableGain() [3/3]

template<typename realT >

realT mx::AO::analysis::clGainOpt< realT >::maxStableGain	(	realT &	ll,
		realT &	ul
	)

Find the maximum stable gain for the loop parameters.

Returns

the maximum stable gain for the loop parameters Find the maximum stable gain for the loop parameters Conducts a search along the Nyquist contour of the open-loop transfer function to find the most-negative crossing of the real axis.

the maximum stable gain for the loop parameters

Parameters

ll	[in.out] the lower limit used for the search
ul	[in.out] the upper limit used for hte search

Definition at line 999 of file clGainOpt.hpp.

Referenced by mx::AO::analysis::clAOLinearPredictor< _realT >::_regularizeCoefficients(), and mx::AO::analysis::clAOLinearPredictor< _realT >::regularizeCoefficients().

N() [1/2]

template<typename realT >

int mx::AO::analysis::clGainOpt< realT >::N

(

)

Get the number of integrations in the (optional) moving average.

Returns

the current value of m_N.

Definition at line 476 of file clGainOpt.hpp.

N() [2/2]

template<typename realT >

void mx::AO::analysis::clGainOpt< realT >::N

(

int

newN

)

Set the number of integrations in the moving average.

Parameters

[in]

newN

the value of m_N.

Definition at line 482 of file clGainOpt.hpp.

olXfer() [1/2]

template<typename realT >

std::complex< realT > mx::AO::analysis::clGainOpt< realT >::olXfer

(

int

fi

)

Calculate the open-loop transfer function.

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Returns

the complex value of the open-loop transfer function at f[fi].

Parameters

[in]

fi

the index of the frequency

Definition at line 687 of file clGainOpt.hpp.

olXfer() [2/2]

template<typename realT >

std::complex< realT > mx::AO::analysis::clGainOpt< realT >::olXfer	(	int	fi,
		complexT &	H_dm,
		complexT &	H_del,
		complexT &	H_con
	)

Calculate the open-loop transfer function.

Returns

the complex value of the open-loop transfer function at f.

Parameters

[in]	fi	the index of the frequency
[out]	H_dm	the transfer function of the DM
[out]	H_del	the delay transfer function
[out]	H_con	the controller transfer function.

Definition at line 701 of file clGainOpt.hpp.

References mx::math::six_fifths().

optGainOpenLoop() [1/2]

template<typename realT >

realT mx::AO::analysis::clGainOpt< realT >::optGainOpenLoop	(	realT &	var,
		const std::vector< realT > &	PSDerr,
		const std::vector< realT > &	PSDnoise,
		bool	gridSearch
	)

Return the optimum closed loop gain given an open loop PSD.

Uses _gmax for the upper limit.

Returns

the optimum gain

Parameters

[out]	var	the variance at the optimum gain
[in]	PSDerr	open loop error PSD
[in]	PSDnoise	open loop measurement noise PSD
[in]	gridSearch	flag controlling whether an intial grid search is done to find the global minimum

Definition at line 1136 of file clGainOpt.hpp.

References gmax.

Referenced by mx::AO::analysis::clAOLinearPredictor< _realT >::_regularizeCoefficients(), mx::AO::analysis::fourierTemporalPSD< _realT, aosysT >::analyzePSDGrid(), and mx::AO::analysis::clAOLinearPredictor< _realT >::regularizeCoefficients().

optGainOpenLoop() [2/2]

template<typename realT >

realT mx::AO::analysis::clGainOpt< realT >::optGainOpenLoop	(	realT &	var,
		const std::vector< realT > &	PSDerr,
		const std::vector< realT > &	PSDnoise,
		realT &	gmax,
		bool	gridSearch
	)

Return the optimum closed loop gain given an open loop PSD.

Returns

the optimum gain.

Parameters

[out]	var	the variance at the optimum gain
[in]	PSDerr	open loop error PSD
[in]	PSDnoise	open loop measurement noise PSD
[in]	gmax	maximum gain to consider. If 0, then _gmax is used.
[in]	gridSearch	flag controlling whether an intial grid search is done to find the global minimum

Definition at line 1146 of file clGainOpt.hpp.

References gmax.

pseudoOpenLoop()

template<typename realT >

int mx::AO::analysis::clGainOpt< realT >::pseudoOpenLoop	(	std::vector< realT > &	PSD,
		realT	g
	)

Calculate the pseudo open-loop PSD given a closed loop PSD.

Returns

0 on success

Parameters

	PSD	[in.out] input closed loop PSD, on output contains the pseudo open loop error PSD
[in]	g	the loop gain when PSD was measured.

Definition at line 1204 of file clGainOpt.hpp.

remember() [1/2]

template<typename realT >

realT mx::AO::analysis::clGainOpt< realT >::remember

(

)

Get the remember factor.

Definition at line 625 of file clGainOpt.hpp.

remember() [2/2]

template<typename realT >

void mx::AO::analysis::clGainOpt< realT >::remember

(

const realT &

rem

)

Set the remember factor for a leaky integrator.

Definition at line 612 of file clGainOpt.hpp.

setLeakyIntegrator()

template<typename realT >

void mx::AO::analysis::clGainOpt< realT >::setLeakyIntegrator

(

realT

remember

)

Set the FIR and IIR coefficients so that the control law is a leaky integrator.

Set remember to 1.0 for a pure integrator control law.

Parameters

[in]

remember

a number usually close to 1 setting the amount "remembered" from previous iterations.

Definition at line 631 of file clGainOpt.hpp.

tau() [1/2]

template<typename realT >

realT mx::AO::analysis::clGainOpt< realT >::tau

(

)

Get the loop delay.

Returns

the current value of m_tau.

Definition at line 512 of file clGainOpt.hpp.

tau() [2/2]

template<typename realT >

void mx::AO::analysis::clGainOpt< realT >::tau

(

realT

newTau

)

Set the loop delay.

Parameters

[in]

newTau

the new value of m_tau.

Definition at line 518 of file clGainOpt.hpp.

Ti() [1/2]

template<typename realT >

realT mx::AO::analysis::clGainOpt< realT >::Ti

(

)

Get the loop sampling interval.

Returns

the current value of m_Ti.

Definition at line 494 of file clGainOpt.hpp.

Ti() [2/2]

template<typename realT >

void mx::AO::analysis::clGainOpt< realT >::Ti

(

realT

newTi

)

Set the loop sampling interval.

Parameters

[in]

newTi

the new value of m_Ti.

Definition at line 500 of file clGainOpt.hpp.

Member Data Documentation

m_a

template<typename _realT >

std::vector<realT> mx::AO::analysis::clGainOpt< _realT >::m_a

protected

Vector of IIR coefficients.

Definition at line 75 of file clGainOpt.hpp.

Referenced by mx::AO::analysis::clGainOpt< _realT >::a(), and mx::AO::analysis::clGainOpt< _realT >::a().

m_b

template<typename _realT >

std::vector<realT> mx::AO::analysis::clGainOpt< _realT >::m_b

protected

Vector of FIR coefficients.

Definition at line 74 of file clGainOpt.hpp.

Referenced by mx::AO::analysis::clGainOpt< _realT >::b(), and mx::AO::analysis::clGainOpt< _realT >::b().

m_changed

template<typename _realT >

bool mx::AO::analysis::clGainOpt< _realT >::m_changed { true }

protected

True if any of the members which make up the basic transfer functions are changed.

Definition at line 81 of file clGainOpt.hpp.

m_f

template<typename _realT >

std::vector<realT> mx::AO::analysis::clGainOpt< _realT >::m_f

protected

Vector of frequencies.

Definition at line 77 of file clGainOpt.hpp.

Referenced by mx::AO::analysis::clGainOpt< _realT >::f_size().

m_fChanged

template<typename _realT >

bool mx::AO::analysis::clGainOpt< _realT >::m_fChanged { true }

protected

True if frequency or max size of m_a and m_b changes.

Definition at line 79 of file clGainOpt.hpp.

m_maxFindMin

template<typename _realT >

realT mx::AO::analysis::clGainOpt< _realT >::m_maxFindMin

The Minimum value for the maximum stable gain finding algorithm.

Parameters for stability analysis

Definition at line 97 of file clGainOpt.hpp.

m_minFindBits

template<typename _realT >

int mx::AO::analysis::clGainOpt< _realT >::m_minFindBits

The bits of precision to use for minimum finding. Defaults to std::numeric_limits<realT>::digits.

Definition at line 107 of file clGainOpt.hpp.

m_minFindMaxFact

template<typename _realT >

realT mx::AO::analysis::clGainOpt< _realT >::m_minFindMaxFact

The maximum value, as a multiplicative factor of maximum gain.

Definition at line 106 of file clGainOpt.hpp.

m_minFindMaxIter

template<typename _realT >

uintmax_t mx::AO::analysis::clGainOpt< _realT >::m_minFindMaxIter

The maximum iterations allowed for minimization.

Definition at line 109 of file clGainOpt.hpp.

m_minFindMin

template<typename _realT >

realT mx::AO::analysis::clGainOpt< _realT >::m_minFindMin

The Minimum value for the minimum finding algorithm.

Parameters for minimization finding

Definition at line 105 of file clGainOpt.hpp.

m_N

template<typename _realT >

int mx::AO::analysis::clGainOpt< _realT >::m_N

protected

Number of integrations in the (optional) moving average. Default is 1.

Definition at line 69 of file clGainOpt.hpp.

m_remember

template<typename _realT >

realT mx::AO::analysis::clGainOpt< _realT >::m_remember { 1.0 }

protected

The leaky integrator forget factor.

Definition at line 73 of file clGainOpt.hpp.

m_tau

template<typename _realT >

realT mx::AO::analysis::clGainOpt< _realT >::m_tau

protected

The loop delay.

Definition at line 71 of file clGainOpt.hpp.

m_Ti

template<typename _realT >

realT mx::AO::analysis::clGainOpt< _realT >::m_Ti

protected

The loop sampling interval.

Definition at line 70 of file clGainOpt.hpp.

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

• ao/analysis/clGainOpt.hpp