Tool 05 · Control
Drag the sliders and watch the response change instantly. The plant is a simulated inertial load (like a robot joint or drive wheel) trying to reach a setpoint — the same problem your PID loop solves.
Plant: inertial load with light friction and a constant disturbance (like gravity on a joint) — that's why P alone never quite reaches the line.
Response metrics
The yellow line is the setpoint (target = 1.0). The white trace is the simulated system's position over 6 seconds after the command steps from 0 to 1. Your PID output drives the load; physics — inertia, friction and a constant disturbance force — pushes back.
Experiments worth trying: raise Kp alone and watch response speed up, then start ringing; note that P-only always settles below the line because of the disturbance — then add Ki and watch the gap close; push Ki too far and see the slow, fat oscillation of integral windup; finally add Kd and watch it shave the overshoot off.
When you're ready to tune a real robot, follow the step-by-step method in How to Tune a PID Controller, and if the concepts are new start with PID Control Explained Simply.
No simulator is — your robot has motor saturation, sensor noise, loop-rate limits and backlash. But the qualitative behavior (what each gain does, what each failure looks like) transfers directly, which is what makes tuning intuition portable.
Kd amplifies high-frequency change — including sensor noise. Real implementations filter the derivative term or compute it from the measurement instead of the error. The simulation here uses clean signals, so Kd looks better-behaved than it will on your encoder.