A specialized technical skill for fitting first-order dynamic models to experimental step response data to extract critical system parameters like process gain and time constants.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install hvac-control-first-order-model-fitting
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install hvac-control-first-order-model-fitting using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is First-Order Model Fitting?
The First-Order Model Fitting skill provides a mathematical and computational framework for analyzing physical systems that exhibit first-order dynamics, such as thermal, electrical, or mechanical processes. By leveraging Openclaw Skills, developers and engineers can automate the extraction of the process gain (K) and the time constant (tau) from raw experimental data.
This utility focuses on the step response formula, enabling users to transform noisy time-series measurements into actionable system models. It is an essential tool for anyone performing system identification or control loop tuning within the Openclaw Skills ecosystem.
First-Order Model Fitting Use Cases
- Characterizing the heating and cooling rates of thermal chambers or industrial ovens.
- Analyzing electrical RC circuit responses to determine capacitance or resistance characteristics.
- Modeling mechanical damping and velocity profiles in response to constant force inputs.
- Automating system identification for PID controller tuning in automation workflows.
How First-Order Model Fitting Works
- Define the baseline y_ambient and the known step input change u from the experimental setup.
- Identify the transient phase of the data, starting from the exact moment the input step occurs.
- Apply the step response equation y(t) = y_ambient + K * u * (1 - exp(-t/tau)) to the dataset.
- Calculate the process gain K by measuring the steady-state change relative to the input magnitude.
- Determine the time constant tau by identifying the time elapsed to reach 63.2% of the total output change.
- Validate the model fit using R-squared and Root Mean Square (RMS) error metrics.
First-Order Model Fitting Setup
To implement this skill within your environment, ensure you have a Python environment with NumPy and SciPy installed for curve fitting:
pip install numpy scipy matplotlib
Integrate the model into your Openclaw Skills workflow by defining your local system parameters and passing your experimental CSV or array data to the fitting function.
First-Order Model Fitting Data Schema & Taxonomy
The skill processes and generates data following this structure:
| Parameter | Description | Unit |
|---|---|---|
y_ambient |
The initial baseline value before the step input. | Output Units |
u |
The magnitude of the step input applied. | Input Units |
K |
Process Gain: (Steady State - Ambient) / u. | Output/Input |
tau |
Time Constant: Time to reach 63.2% of response. | Seconds |
R-squared |
Statistical measure of how close the data is to the fitted line. | Decimal (0-1) |
First-Order Model Fitting Advanced Features
- Support for non-linear least squares fitting using Levenberg-Marquardt algorithms.
- Automated noise filtering to improve parameter accuracy in low-signal environments.
- Multi-agent support for batch processing large datasets of experimental trials.
- Integration hooks for real-time visualization of the fitted curve against raw data points.
SKILL.md
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