How do I simulate a robotic gripper?

Open this page and use the live Cart-Pole Control Design tool below — set your inputs and the simulation runs instantly in your browser using real numerics. No install, no account needed.

Yes. The simulation runs free in your browser. A one-time unlock or a Pro plan adds advanced parameters, saved presets, data import, and clean exports.

Can I use my own numbers?

Absolutely — every input is adjustable, and with data import you can drive a robotic gripper from your own measurements.

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Cart-Pole Control Design for a robotic gripper

Simulate a robotic gripper live in your browser. This runs the real Cart-Pole Control Design solver — adjust the inputs, watch it respond instantly, and export the result. No install, no account.

For:Students Researchers Educators Engineers Hobbyists & Makers K-12 Students First Responders

Cart Pole

open full solver →

Cart-Pole BalanceLive

inverted pendulum control

Controls

Speed1×

Presets

Controller gain

Disturbance

Controller on

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.

▶ Run in Python

Data Inspector

Pole angle8.6°

Cart position0.00

Statusbalanced

Governing equation

F = - K x = 1.0 (55 θ + 12 \dot{θ} + 1.5 x + 3 \overset{x}{˙})

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.

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PID Control

open full solver →

PID ControllerLive

step response tuning

Controls

Presets

Proportional Kp

Integral Ki

Derivative Kd

A PID controller drives a system to its setpoint using three terms: proportional reacts to the current error, integral eliminates steady-state offset, and derivative damps overshoot. Tuning the three gains trades off speed, overshoot, and stability — the workhorse of industrial control.

▶ Run in Python

Data Inspector

Overshoot0%

Settling time0.00 s

Steady-state error0.000

Governing equation

u (t) = 4.0 e (t) + 1.00 \int e d t + 0.5 \frac{d e}{d t}

Reading this result: Balanced gains — proportional for speed, integral to remove offset, derivative to damp overshoot: the textbook well-tuned response.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

or unlock everything with Pro →

About this simulation

The full Cart-Pole Control Design tool models a robotic gripper with the same numerics engineers and scientists use — running entirely client-side. Change any parameter and the result updates in real time, so you can build intuition, check a design, or teach the concept without spreadsheets or installs.

More you can do with Cart-Pole Control Design

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Other ways to simulate a robotic gripper

Forward Kinematics for a robotic gripper Inverse Kinematics for a robotic gripper Differential Drive for a robotic gripper Cart-Pole Balance for a robotic gripper Quadcopter Control for a robotic gripper RRT Path Planning for a robotic gripper

Frequently asked questions

How do I simulate a robotic gripper?: Open this page and use the live Cart-Pole Control Design tool below — set your inputs and the simulation runs instantly in your browser using real numerics. No install, no account needed.
Is it free?: Yes. The simulation runs free in your browser. A one-time unlock or a Pro plan adds advanced parameters, saved presets, data import, and clean exports.
Can I use my own numbers?: Absolutely — every input is adjustable, and with data import you can drive a robotic gripper from your own measurements.