Tune PID controller gains interactively. Visualize step response, overshoot, settling time, and steady-state error in real time.
An interactive playground for three gains (proportional Kp, integral Ki, derivative Kd) wrapped around a simulated plant. Adjust any gain and the step response redraws immediately, with overshoot, settling time, and steady-state error measured on the curve. The surprise for most first-time users: the gains fight each other. Raising Ki shrinks steady-state error but adds overshoot; raising Kd can calm that overshoot but amplifies measurement noise. There is no single right Kp. The trade-offs only make sense as a triple, and gains that look great on the simulated plant will behave differently on a real process, which is never a clean textbook transfer function.
The tool builds a closed loop around a plant model and a PID controller, applies a unit step at the setpoint, and simulates the response curve. The reported figures are read off that curve: overshoot is the peak rise above the final value, settling time is when the curve enters and stays in a tolerance band, and steady-state error is the gap between setpoint and final value.
Kp acts on the current error: higher Kp means a faster, more aggressive correction, and for plants without a built-in integrator it also shrinks, but never removes, steady-state error. Ki accumulates the error over time, which is what drives steady-state error to zero, and adds overshoot. Kd reacts to how fast the error is changing, adding damping that can reduce overshoot and settling time but magnifies noise.
Not as a finished setting. The tool simulates a plant model, so the gains are a first guess, not a certified result. Real processes add delay, saturation, and noise the simulation may not include, and the model itself may not match your process. Use the values as a starting point, apply them on the real loop with small setpoint steps, and confirm the response before leaving them in place.
The integral term keeps increasing its output as long as any error remains, so it keeps pushing the response past the setpoint before the proportional and derivative terms pull it back. Larger Ki means a bigger push and more overshoot. This is the standard trade-off: Ki is what removes steady-state error, and the overshoot it creates is what a moderate Kd is added to counteract.
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