Getting Started with PID Tuning

January 4, 2025 Update -

Documentation for REVLib 2025 can now be found at: https://docs.revrobotics.com/revlib

Please bookmark our new page as we transition over and add more information there!

For a detailed technical and mathematical description of each term and its effect, the WPILib docs page on PID is a good resource.

FRC Usage

In FRC, PID loops are used in many types of mechanisms, from flywheel shooters to vertical arms. These need to be tuned to different constants, depending on the units they use and the physical design of the mechanism, however the process to find these constants is roughly the same.

Most teams find success using controllers tuned primarily with and , using a to account for .

The Constants

P - Proportional Gain

P, the proportional gain, is the primary factor of the control loop. This is multiplied by the error and that gain is added to the output. This does the heavy lifting of the motion, pushing the motor in the direction it needs to go.

I - Integral Gain

I, the integral gain, is not often recommended in FRC. It is useful for eliminating steady-state error, or error that the other gains leave behind and cannot address. It accumulates the error over time and multiplies it by the I gain, gradually increasing the power it supplies until that has evened out. If it is needed, it's recommended to use a limited to prevent .

D - Derivative Gain

The derivative gain, D, is used to tune out oscillation and dampen the motion. It resists motion, decreasing power when the mechanism is moving. A good balance of P and D is needed to make a smooth motion with no oscillation.

Tuning

Several guides for PID tuning are available, such as this one on the WPILib docs. It may be useful to consult multiple, especially those available that reference your specific mechanism.

Any method for PID tuning will start with the same concept, however, regardless of mechanism. Before you can tune your mechanism, you should setup a graph of the setpoint and that measured value, either through the REV Hardware Client or a similar utility. This will allow you to analyze each test and properly evaluate the changes to make.

To then tune a basic PID loop, follow the steps below:

1. Set all constants (P, I, D, F, arb Feed Forward, etc) to 0

2. If you are operating in Velocity Control Mode (or a velocity-based control mode), set F to the motor's value as discussed in F Parameter
3. Ensure the mechanism is safe to actuate. This process will spin the motor, potentially at unexpected speeds and in unexpected directions

4. Check the direction of the motor, and invert it if needed so that positive output is in the desired direction

5. Set P to a very small number, relative to the units you are working in

6. Set a target for the motor to move to. Ensure this is within the range of your mechanism.

7. Gradually increase P until you see movement, by small increments

8. Once you see motion, increase P by small increments until it reaches the target at the desired speed

9. If you see oscillation, decrease P or begin to increment D by a small amount

10. Continue to adjust these parameters until the motion is quick, precise, and repeatable

Arbitrary Feed Forward

The SPARK Flex and SPARK MAX do not have any support for more complex physics-based Feed Forward models, but do include a means of applying an arbitrary voltage which can be calculated in your team code and passed to the API.

It can be applied with the setpoint as seen below:

Java

// Set the setpoint of the PID controller in raw position mode, with a feedforward
m_pidController.setReference(
    setPoint, 
    CANSparkBase.ControlType.kPosition, 
    feedForward
);

API Docs: SparkPIDController, setReference

C++

// Set the setpoint of the PID controller in raw position mode, with a feedforward
m_pidController.SetReference(
    SetPoint, 
    rev::CANSparkBase::ControlType::kPosition,
    feedForward
);

API Docs: SparkPIDController, SetReference

WPILib offers several basic feed forward calculation classes