Motor Model Dynamics logo

Motor Model Dynamics

OrganizationPopular
benchflow-ai
motor-model-dynamics

Use this skill when simulating quadrotor physical dynamics — mapping desired thrust/moments to individual motor RPMs via a propeller allocation matrix, applying first-order motor lag, and integrating the nonlinear equations of motion (translational and rotational) using RK45.

Overview

Publisherbenchflow-ai
Repositoryskillsbench
Skill namemotor-model-dynamics
Stars
1.8K
Forks
371
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by benchflow-ai on GitHub. Read the source before you install it.

Installation

Install the Motor Model Dynamics AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/benchflow-ai/skillsbench/tree/main/tasks/drone-planning-control/environment/skills/motor-model-dynamics
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git /tmp/skillsbench
mkdir -p .claude/skills
cp -r /tmp/skillsbench/tasks/drone-planning-control/environment/skills/motor-model-dynamics .claude/skills/motor-model-dynamics
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Motor Model Dynamics in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Motor Model Dynamics on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Motor Model Dynamics is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Motor Model and Dynamics Simulation

Overview

Two modules form the physics layer of the simulator:

  1. Motor model — maps desired [F, Mx, My, Mz] → desired motor RPMs → applies first-order lag → returns actual thrust and moments
  2. Dynamics — nonlinear equations of motion; given forces and moments, returns the 16-element state derivative for ODE integration

Motor Model

Propeller Allocation Matrix (X-frame quadrotor)

The 4×4 allocation matrix maps [F, Mx, My, Mz] to squared motor RPMs. For an X-frame with arm length d, thrust coefficient cT, and torque coefficient cQ:

         motor:  1      2      3      4
Thrust:         +cT    +cT    +cT    +cT
Roll  (Mx):      0    +d·cT    0    -d·cT
Pitch (My):    -d·cT    0    +d·cT    0
Yaw   (Mz):    -cQ    +cQ    -cQ    +cQ

Implementation Logic

  1. Build the 4×4 prop_matrix from cT, cQ, and d.
  2. Solve prop_matrix @ rpm_sq = [F, Mx, My, Mz] for rpm_sq (desired squared RPMs).
  3. Take sqrt of each element (clamp negatives to 0 first), then clip to [rpm_min, rpm_max].
  4. Apply first-order motor lag: rpm_dot = km * (rpm_desired - rpm_current).
  5. Compute actual force/moment using prop_matrix @ (motor_rpm ** 2) and return (F_actual, M_actual, rpm_dot).

Dynamics (Equations of Motion)

State vector (16,)

IndicesMeaning
0:3Position [x, y, z]
3:6Velocity [vẋ, vẏ, vż]
6:9Euler angles [φ, θ, ψ]
9:12Angular velocity [p, q, r]
12:16Motor RPM [ω₁, ω₂, ω₃, ω₄]

Implementation Logic

Compute state_dot (16,) from current state and applied forces/moments:

  1. Position derivative = current velocity (state[3:6]).
  2. Velocity derivative = gravity + thrust projected into world frame via ZYX Euler rotation. The x/y accelerations depend on F/m, sin/cos of all three Euler angles. The z acceleration is −g + (F/m)·cos(φ)·cos(θ).
  3. Euler angle derivative = current angular velocity (state[9:12]).
  4. Angular velocity derivative = I⁻¹ M (solve inertia matrix against moment vector).
  5. Motor RPM derivative = rpm_motor_dot from the motor model.

RK45 Integration

Use scipy.integrate.solve_ivp with method='RK45' over each timestep [t_k, t_{k+1}]. The ODE function wraps dynamics(params, s, F_actual, M_actual, rpm_motor_dot) with F_actual and M_actual held constant for the step. After solving, take the last column of sol.y as the new state.

Motor Physical Limits

ParameterValueMeaning
rpm_min3000Minimum motor speed (motors always spinning)
rpm_max20000Maximum motor speed
motor_constant km36.5 s⁻¹First-order time constant; τ = 1/km ≈ 27 ms

Max Thrust Calculation

Derived from motor limits:

  • T_max = 4 * cT * rpm_max² (all 4 motors at max RPM)
  • T_min = 4 * cT * rpm_min²
  • Thrust-to-weight ratio: twr = T_max / (mass * gravity)
  • Max upward acceleration: (T_max − mass·g) / mass
  • Max downward acceleration: (mass·g − T_min) / mass

Frequently asked questions

What does the Motor Model Dynamics AI skill do?

Use this skill when simulating quadrotor physical dynamics — mapping desired thrust/moments to individual motor RPMs via a propeller allocation matrix, applying first-order motor lag, and integrating the nonlinear equations of motion (translational and rotational) using RK45.

Why use Motor Model Dynamics on TypingMind?

Because you install it once and use it with any model. Motor Model Dynamics is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Motor Model Dynamics in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/benchflow-ai/skillsbench/tree/main/tasks/drone-planning-control/environment/skills/motor-model-dynamics. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Motor Model Dynamics?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Motor Model Dynamics?

As many as you like. As long as a model supports skills, you can use Motor Model Dynamics with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Motor Model Dynamics AI skill free?

Yes. It is published on GitHub by benchflow-ai under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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