• PID Control | ACE-Lab CORE
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    PID Control

    Understand how proportional, integral and derivative action change closed-loop behaviour, then apply that understanding to tune a practical motor-speed controller.

    Estimated study time: 60–90 minutes ACE-Lab: Base + Sense + Actuate Prior topic: Closed-loop control
    1

    What will you learn?

    PID control is one of the most widely used feedback-control approaches. This page moves from the practical effect of each controller term to testing and refining a controller on the ACE-Lab motor.

    Explain proportional, integral and derivative action.
    Relate gain changes to rise time, overshoot and error.
    Tune a PI or PID controller against requirements.
    Recognise saturation, noise and windup effects.
    2

    Start with the application

    The application is a DC motor whose speed must follow a demanded value even when the mechanical load changes.

    30 RPM

    Demanded motor speed

    ⚙
    Control requirements
    Reference speed30 RPM
    Rise time< 3 s
    Overshoot0%
    Steady-state error0 RPM
    Load disturbanceRecover safely
    3

    The core idea

    The controller uses the error between the demanded speed and the measured speed. Each PID term responds to the error in a different way.

    u(t) = Kpe(t) + Ki∫e(t)dt + Kdde(t)/dt
    Proportional Responds to the current error. Increasing it usually makes the system react faster.
    Integral Responds to accumulated error. It is useful for removing steady-state error.
    Derivative Responds to the rate of change of error. It can add damping but may amplify noise.
    4

    Worked tuning example

    Begin with proportional control, then add integral action only when the behaviour of the first design is understood.

    1
    Apply proportional control

    Increase Kp gradually and record the rise time, overshoot and final speed.

    2
    Observe the remaining error

    Check whether the motor settles below the 30 RPM reference under load.

    3
    Add integral action

    Increase Ki carefully until the steady-state error is removed.

    4
    Retest against every requirement

    A faster response is not automatically a better response if overshoot or saturation appears.

    Controller Rise time Overshoot Final error Decision
    P only 2.4 s 0% 3.2 RPM Refine
    PI attempt 1 1.7 s 14% 0 RPM Refine
    PI final 2.2 s 0% 0 RPM Meets requirements
    5

    ACE-Lab practical activity

    Motor-speed control challenge

    Implement and tune a PI controller so that the motor satisfies the stated speed-control requirements.

    Equipment ACE-Lab Base + Sense and Actuate rig
    Software MATLAB and Simulink
    Evidence Speed response, control effort and requirement table

    Suggested activity sequence

    1
    Open the supplied model

    Identify the reference, controller, motor, encoder and feedback path.

    2
    Run the P controller

    Record the response and explain the remaining steady-state error.

    3
    Add integral action

    Tune Ki while watching overshoot and actuator demand.

    4
    Apply a load disturbance

    Measure how quickly the controller restores the demanded speed.

    5
    Validate the final design

    Complete the requirement table using measured results.

    6

    What could go wrong?

    A controller may look successful under one test while hiding practical weaknesses. Refinement means deliberately looking for them.

    Saturation The requested motor command may exceed the available supply or driver limit.
    Integral windup The integral term may continue growing while the actuator is saturated.
    Measurement noise Encoder noise or quantisation may make derivative action unsuitable.
    7

    Check your understanding

    Why can proportional control leave a steady-state speed error?
    What benefit does integral action add, and what new problem can it introduce?
    Why should controller gains be tested against actuator limits?
    Which measured results prove that the final design meets the requirements?
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