com.acmerobotics.roadrunner.tuning

Class RampRegression

com.acmerobotics.roadrunner.tuning.RampRegression

public class RampRegression

Container for ramp feedforward regression data.

Here's the general procedure for gathering the requisite data:

1. Slowly ramp the motor power/voltage and record encoder values along the way.

2. Run a linear regression on the encoder velocity vs. motor power plot to obtain a slope (kV) and an optional intercept (kStatic).

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	RampRegression.RampResult Feedforward parameter estimates from the ramp regression and additional summary statistics

Constructor Summary

Constructors

Constructor and Description
RampRegression(java.util.List<java.lang.Double> timeSamples, java.util.List<java.lang.Double> positionSamples, java.util.List<java.lang.Double> powerSamples) Container for ramp feedforward regression data.
RampRegression(java.util.List<java.lang.Double> timeSamples, java.util.List<java.lang.Double> positionSamples) Container for ramp feedforward regression data.
RampRegression(java.util.List<java.lang.Double> timeSamples) Container for ramp feedforward regression data.
RampRegression() Container for ramp feedforward regression data.

Method Summary

Modifier and Type	Method and Description
void	add(double time, double position, double power) Add a sample to the regression.
RampRegression.RampResult	fit(boolean fitStatic) Fit data from a "ramp" test.
RampRegression.RampResult	fit() Fit data from a "ramp" test.
void	save(java.io.File file) Save the data to a CSV file for debugging or additional analysis.

Constructor Detail

RampRegression

@JvmOverloads
public RampRegression(@NotNull
                                    java.util.List<java.lang.Double> timeSamples,
                                    @NotNull
                                    java.util.List<java.lang.Double> positionSamples,
                                    @NotNull
                                    java.util.List<java.lang.Double> powerSamples)

Container for ramp feedforward regression data.

Here's the general procedure for gathering the requisite data:

1. Slowly ramp the motor power/voltage and record encoder values along the way.

2. Run a linear regression on the encoder velocity vs. motor power plot to obtain a slope (kV) and an optional intercept (kStatic).

Parameters:

timeSamples - time samples

positionSamples - position samples

powerSamples - power (scaled voltage) samples

RampRegression

@JvmOverloads
public RampRegression(@NotNull
                                    java.util.List<java.lang.Double> timeSamples,
                                    @NotNull
                                    java.util.List<java.lang.Double> positionSamples)

Container for ramp feedforward regression data.

Here's the general procedure for gathering the requisite data:

1. Slowly ramp the motor power/voltage and record encoder values along the way.

2. Run a linear regression on the encoder velocity vs. motor power plot to obtain a slope (kV) and an optional intercept (kStatic).

Parameters:

timeSamples - time samples

positionSamples - position samples

RampRegression

@JvmOverloads
public RampRegression(@NotNull
                                    java.util.List<java.lang.Double> timeSamples)

Container for ramp feedforward regression data.

Here's the general procedure for gathering the requisite data:

1. Slowly ramp the motor power/voltage and record encoder values along the way.

2. Run a linear regression on the encoder velocity vs. motor power plot to obtain a slope (kV) and an optional intercept (kStatic).

Parameters:

timeSamples - time samples

RampRegression

@JvmOverloads
public RampRegression()

Container for ramp feedforward regression data.

Here's the general procedure for gathering the requisite data:

1. Slowly ramp the motor power/voltage and record encoder values along the way.

2. Run a linear regression on the encoder velocity vs. motor power plot to obtain a slope (kV) and an optional intercept (kStatic).

Method Detail

add

public void add(double time,
                double position,
                double power)

Add a sample to the regression.

fit

@JvmOverloads
 @NotNull
public RampRegression.RampResult fit(boolean fitStatic)

Fit data from a "ramp" test.

Parameters:

fitStatic - true if kStatic should be fit (note: this affects the kV computation)

fit

@JvmOverloads
 @NotNull
public RampRegression.RampResult fit()

Fit data from a "ramp" test.

save

public void save(@NotNull
                 java.io.File file)

Save the data to a CSV file for debugging or additional analysis.