P · Respond to error
Proportional gain increases effort when error grows. Low gain is gentle but slow; excessive gain can excite oscillation in a real mechanism with delay or inertia.
Learn error, feedback, proportional control, integral action, derivative damping, and the practical limits of a real robot.
A simplified learning model. Values describe the model, not a connected robot.
An open-loop command asks for effort, such as a PWM duty cycle. A closed-loop controller measures the result, compares it with a target, and adjusts the effort. For wheel speed, an encoder supplies feedback; for a joint, use an appropriate angle sensor. Position, speed, and current are different quantities and need distinct units and limits.
Proportional gain increases effort when error grows. Low gain is gentle but slow; excessive gain can excite oscillation in a real mechanism with delay or inertia.
Integral action accumulates error to compensate for constant loads. Clamp or condition the integral when the actuator saturates so it does not keep winding up.
Derivative action reacts to the rate of change. It is sensitive to noisy measurements, so filtering and derivative-on-measurement are common practical choices.
The experiment uses only proportional control on the normalized first-order plant dy/dt = −y + u, with u = Kp(1 − y). Its exact response is y(t) = Kp/(1 + Kp) × (1 − exp(−(1 + Kp)t)). A gain of 2 settles at 2/3 of the target; a gain of 0 produces no motion. This deliberately simple model has no delay, saturation, noise, or oscillatory dynamics and cannot predict whether your physical robot is stable.
A network connection should not be the only way to stop an actuator. Keep local watchdog and driver-disable behavior independent of high-level planning.
Manufacturer and project documentation. Reviewed 20 September 2026; check your exact board revision and software release.