Tune Your PID Controller Visually

A free browser-based PID controller tuning simulator for students and engineers. Adjust Kp, Ki, and Kd sliders and watch the closed-loop step-response curve update instantly — complete with overshoot, rise time, settling time, and steady-state error metrics. Supports first-order lag and second-order mass-spring-damper plant models with anti-windup and actuator saturation.

How to Use the PID Tuning Simulator

Choose a plant model

Select "First-order lag" for simple thermal or hydraulic systems, or "Second-order" for mechanical mass-spring-damper dynamics.

Adjust the gains

Move the Kp, Ki, and Kd sliders. The step-response curve redraws live. Watch the performance metrics update below the chart.

Try presets or auto-tune

Load a preset to see classic tuning scenarios, or click "Auto-tune" to apply Ziegler–Nichols starting gains based on the selected plant.

Inject a disturbance

Press "Inject disturbance" to add a step load disturbance mid-run and see how the controller rejects it.

Visualize proportional, integral, and derivative control in real time

Setpoint / Output

Setpoint Output

Kp — Proportional / Ki — Integral / Kd — Derivative

Kp — Proportional

Ki — Integral

Kd — Derivative

Plant model

Performance metrics

Overshoot — %

Rise time — s

Settling time (±2%) — s

Steady-state error — %

Presets

Features

Live step-response canvas — redraws on every gain change with DPI-crisp rendering First-order and second-order plant models with numerical Euler integration (dt = 0.01 s) Anti-windup integral clamping and actuator saturation for realistic simulation Instant metrics: overshoot %, rise time, settling time (±2%), and steady-state error One-click Ziegler–Nichols auto-tune and disturbance injection for learning experiments Four tuning presets showing underdamped, well-tuned, sluggish, and aggressive responses

FAQ

What is a PID controller?

A PID (Proportional-Integral-Derivative) controller calculates an error between a desired setpoint and a measured process variable, then applies a correction based on three terms: P reacts to the current error, I eliminates steady-state offset over time, and D damps rapid changes.

What do Kp, Ki, and Kd do?

Kp (proportional gain) sets the strength of the immediate response. Ki (integral gain) drives the error to zero over time but can cause overshoot. Kd (derivative gain) predicts future error and reduces overshoot, but amplifies noise.

What is anti-windup?

When the actuator saturates (reaches its maximum output), the integrator should stop accumulating error — otherwise it "winds up" and causes large overshoot when saturation lifts. This simulator clamps the integral term to prevent windup.

What is Ziegler–Nichols tuning?

Ziegler–Nichols is a classic heuristic tuning method. It estimates the ultimate gain (Ku) and oscillation period (Tu) at the stability boundary, then back-calculates Kp, Ki, Kd. The results are a starting point — fine-tuning is usually required.

Is the simulation numerically accurate?

The simulator uses fixed-step Euler integration with dt = 0.01 s over 10 s, which is sufficient for educational exploration. For precision engineering, use dedicated tools such as MATLAB/Simulink.

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Published 18 May 2026 Updated 21 May 2026