The hello-world of control theory. A controller nudges a cart back and forth to keep a pole balanced upright — switch it off and gravity wins instantly.
Cart-Pole BalanceLive
inverted pendulum control
Controls
Presets
Balancing a pole on a moving cart is the classic control benchmark. A state-feedback controller senses the pole angle and cart position and pushes the cart to keep the pole upright. Turn the controller off and it topples instantly; add disturbance to test how hard it can fight back.
Reading this result: The state-feedback law F = -K·state pushes the cart under the falling pole; with gain above the disturbance it recovers from each nudge.
Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.
★ Sign in to save this setupSave your tuned setup, or drop this simulation into your own site, docs, or course page.
How it works
The cart-pole, or inverted pendulum, is an unstable system stabilized by feedback. A state-feedback controller measures the pole angle and cart position and applies a force to keep the pole vertical. It is the standard benchmark for control and reinforcement learning. Turn the controller off, or crank up the disturbance, to watch it fail.
✦
Ask the AI about this model
The math, the assumptions, real-world uses, or a code translation — explained for this exact simulation.
Is this cart pole inverted pendulum tool really free?▾
Yes. Cart-Pole Balance runs entirely in your browser using your device's own compute, so local use is free forever. You only pay Compute Tokens if you scale a job to the cloud.
Do I need to install anything?▾
No. Everything runs client-side in a modern browser — no downloads, no license, no account required to start.
Can I save or share my simulation?▾
Create a free account to save projects, and use a shareable embed or minted DOI to publish a live, interactive version anywhere.
How accurate are the results?▾
The solver uses established numerical methods, but results are for research and educational purposes and should be validated against experiment or professional review before you rely on them.