Dashboard Tuning Workflow

Tune with graphs and notes instead of guesses.

Module 9: PID and Feedforward ControlControl

In this lesson, you will:

01Use @Config safely.
02Graph target vs measurement.
03Record tuning context.

Concept narrative

FTC Dashboard shortens the tuning loop by letting teams adjust constants and graph behavior live. It is powerful only if the test procedure is repeatable.

Robot mental model

Tuning is an experiment. The robot setup, battery voltage, target move, and mechanical condition are part of the data.

Implementation walkthrough

Make constants configurable, graph target/actual/error/output, change one value at a time, and copy final reviewed values back into code.

TuningConstants.javaJava

@Config
public class LiftTuning {
    public static double kP = 0.004;
    public static int targetTicks = 1200;
}

Common mistakes and debugging

Dashboard can encourage random knob turning. If students change multiple constants at once, they cannot learn what mattered.

Checkpoint

Only one value changes at a time.
Graphs show behavior.
Final values are documented.
The test record includes the setup, prediction, and observed result.
A teammate can repeat the check from the saved evidence without guessing.

Reflection check

Check your understanding before moving on.

Which result best demonstrates completion of “Dashboard Tuning Workflow”?

Why record the test setup, prediction, and observed result?

0 of 2 answered

References

FTC DashboardLive telemetry, graphs, config variables, and camera streaming.Game Manual 0FTC community reference for programming, controls, and robot design.