AdaptiveGain¶

class AdaptiveGain(*args, **kwargs)¶

Bases: pybind11_object

Adaptive gain computation.

As described in [21] , a varying gain \(\lambda\) could be used in the visual servoing control law

\[{\bf v}_c = -\lambda {\bf L}^{+}_{e} {\bf e}\]

with

\[\lambda (|| {\bf e}||) = (\lambda_0 - \lambda_\infty) e^{ -\frac{ \lambda'_0}{\lambda_0 - \lambda_\infty}||{\bf e}||} + \lambda_\infty \]

where:

\(\lambda_0 = \lambda(0)\) is the gain in 0, that is for very small values of \(||{\bf e}||\)
\(\lambda_\infty = \lambda_{||{\bf e}|| \rightarrow \infty}\lambda(||{\bf e}||)\) is the gain to infinity, that is for very high values of \(||{\bf e}||\)
\(\lambda'_0\) is the slope of \(\lambda\) at \(||{\bf e}|| = 0\)

As described in tutorial-boost-vs, the interest of adaptive_gain is to reduce the time to convergence in order to speed up the servo.

The following example shows how to use this class in order to use an adaptive gain with the following parameters \(\lambda_0 = 4\) , \(\lambda_\infty = 0.4\) and \(\lambda'_0 = 30\) .

#include <visp3/vs/vpAdaptiveGain.h>
#include <visp3/vs/vpServo.h>

int main()
{
  vpAdaptiveGain lambda(4, 0.4, 30);   // lambda(0)=4, lambda(oo)=0.4 and lambda'(0)=30

  vpServo servo;
  servo.setLambda(lambda);

  while(1) {
    vpColVector v = servo.computeControlLaw();
  }
}

This other example shows how to use this class in order to set a constant gain \(\lambda = 0.5\) that will ensure an exponential decrease of the task error.

#include <visp3/vs/vpAdaptiveGain.h>
#include <visp3/vs/vpServo.h>

int main()
{
  vpAdaptiveGain lambda(0.5);

  vpServo servo;
  servo.setLambda(lambda);

  while(1) {
    vpColVector v = servo.computeControlLaw();
  }
}

Overloaded function.

__init__(self: visp._visp.vs.AdaptiveGain) -> None

Basic constructor which initializes all the parameters with their default value:

\(\lambda_0 = 1.666\) using vpAdaptiveGain::DEFAULT_LAMBDA_ZERO
\(\lambda_\infty = 0.1666\) using vpAdaptiveGain::DEFAULT_LAMBDA_INFINITY
\(\lambda'_0 = 1.666\) using vpAdaptiveGain::DEFAULT_LAMBDA_SLOPE

__init__(self: visp._visp.vs.AdaptiveGain, c: float) -> None

Constructor that initializes the gain as constant. In that case \(\lambda(||{\bf e}||) = c\) .

Parameters:

c: Value of the constant gain. A typical value is 0.5.

__init__(self: visp._visp.vs.AdaptiveGain, gain_at_zero: float, gain_at_infinity: float, slope_at_zero: float) -> None

Constructor that initializes the gain as adaptive.

Parameters:

gain_at_zero: the expected gain when \(||{\bf e}||=0\) : \(\lambda_0\) .
gain_at_infinity: the expected gain when \(||{\bf e}||\rightarrow\infty\) : \(\lambda_\infty\) .
slope_at_zero: the expected slope of \(\lambda(||{\bf e}||)\) when \(||{\bf e}||=0\) : \(\lambda'_0\) .

Methods

`__init__`	Overloaded function.
`getLastValue`	Gets the last adaptive gain value which was stored in the class.
`initFromConstant`	Initializes the parameters to have a constant gain.
`initFromVoid`	Initializes the parameters with the default value :
`initStandard`	Set the parameters \(\lambda_0, \lambda_\infty, \lambda'_0\) used to compute \(\lambda(\|\|{\bf e}\|\|)\) .
`limitValue`	Gets the value of the gain at infinity (ie the value of \(\lambda_\infty = c\) ) and stores it as a parameter of the class.
`limitValue_const`	Gets the value of the gain at infinity (ie the value of \(\lambda_\infty = c\) ).
`setConstant`	Sets the internal parameters in order to obtain a constant gain equal to the gain in 0 set through the parameter \(\lambda_0\) .
`value`	Computes the value of the adaptive gain \(\lambda(x)\) using:
`value_const`	Computes the value of the adaptive gain \(\lambda(x)\) using:

Inherited Methods

Operators

`__doc__`
`__init__`	Overloaded function.
`__module__`
`__repr__`

Attributes

`DEFAULT_LAMBDA_INFINITY`
`DEFAULT_LAMBDA_SLOPE`
`DEFAULT_LAMBDA_ZERO`
`__annotations__`

__init__(*args, **kwargs)¶

Overloaded function.

__init__(self: visp._visp.vs.AdaptiveGain) -> None

Basic constructor which initializes all the parameters with their default value:

\(\lambda_0 = 1.666\) using vpAdaptiveGain::DEFAULT_LAMBDA_ZERO
\(\lambda_\infty = 0.1666\) using vpAdaptiveGain::DEFAULT_LAMBDA_INFINITY
\(\lambda'_0 = 1.666\) using vpAdaptiveGain::DEFAULT_LAMBDA_SLOPE

__init__(self: visp._visp.vs.AdaptiveGain, c: float) -> None

Constructor that initializes the gain as constant. In that case \(\lambda(||{\bf e}||) = c\) .

Parameters:

c: Value of the constant gain. A typical value is 0.5.

__init__(self: visp._visp.vs.AdaptiveGain, gain_at_zero: float, gain_at_infinity: float, slope_at_zero: float) -> None

Constructor that initializes the gain as adaptive.

Parameters:

gain_at_zero: the expected gain when \(||{\bf e}||=0\) : \(\lambda_0\) .
gain_at_infinity: the expected gain when \(||{\bf e}||\rightarrow\infty\) : \(\lambda_\infty\) .
slope_at_zero: the expected slope of \(\lambda(||{\bf e}||)\) when \(||{\bf e}||=0\) : \(\lambda'_0\) .

getLastValue(self) → float¶

Gets the last adaptive gain value which was stored in the class.

Returns:: It returns the last adaptive gain value which was stored in the class.

initFromConstant(self, c: float) → None¶

Initializes the parameters to have a constant gain. In that case \(\lambda(||{\bf e}||) = c\) .

Parameters:

c: float¶: Value of the constant gain. A typical value is 0.5.

initFromVoid(self) → None¶

Initializes the parameters with the default value :

\(\lambda_0 = 1.666\) using vpAdaptiveGain::DEFAULT_LAMBDA_ZERO
\(\lambda_\infty = 0.1666\) using vpAdaptiveGain::DEFAULT_LAMBDA_INFINITY
\(\lambda'_0 = 1.666\) using vpAdaptiveGain::DEFAULT_LAMBDA_SLOPE

initStandard(self, gain_at_zero: float, gain_at_infinity: float, slope_at_zero: float) → None¶

Set the parameters \(\lambda_0, \lambda_\infty, \lambda'_0\) used to compute \(\lambda(||{\bf e}||)\) .

Parameters:

gain_at_zero: float¶: the expected gain when \(||{\bf e}||=0\) : \(\lambda_0\) .
gain_at_infinity: float¶: the expected gain when \(||{\bf e}||\rightarrow\infty\) : \(\lambda_\infty\) .
slope_at_zero: float¶: the expected slope of \(\lambda(||{\bf e}||)\) when \(||{\bf e}||=0\) : \(\lambda'_0\) .

limitValue(self) → float¶

Gets the value of the gain at infinity (ie the value of \(\lambda_\infty = c\) ) and stores it as a parameter of the class.

Returns:: It returns the value of the gain at infinity.

limitValue_const(self) → float¶

Gets the value of the gain at infinity (ie the value of \(\lambda_\infty = c\) ). This function is similar to limitValue() except that here the value is not stored as a parameter of the class.

Returns:: It returns the value of the gain at infinity.

setConstant(self) → float¶

Sets the internal parameters in order to obtain a constant gain equal to the gain in 0 set through the parameter \(\lambda_0\) .

Returns:: It returns the value of the constant gain \(\lambda_0\) .

value(self, x: float) → float¶

Computes the value of the adaptive gain \(\lambda(x)\) using:

\[\lambda (x) = (\lambda_0 - \lambda_\infty) e^{ -\frac{ \lambda'_0}{\lambda_0 - \lambda_\infty}x} + \lambda_\infty \]

This value is stored as a parameter of the class.

Parameters:

x: float¶: Input value to consider. During a visual servo this value can be the Euclidean norm \(||{\bf e}||\) or the infinity norm \(||{\bf e}||_{\infty}\) of the task function.

Returns:

It returns the value of the computed gain.

value_const(self, x: float) → float¶

Computes the value of the adaptive gain \(\lambda(x)\) using:

\[\lambda (x) = (\lambda_0 - \lambda_\infty) e^{ -\frac{ \lambda'_0}{\lambda_0 - \lambda_\infty}x} + \lambda_\infty \]

Parameters:

x: float¶: Input value to consider. During a visual servo this value can be the Euclidean norm \(||{\bf e}||\) or the infinity norm \(||{\bf e}||_{\infty}\) of the task function.

Returns:

It returns the value of the computed gain.