A model-based engineering skill for calculating precise PI/PID controller gains using the Internal Model Control framework.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install hvac-control-imc-tuning-rules
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-imc-tuning-rules 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 IMC Tuning Rules?
The IMC Tuning Rules skill provides a systematic, model-based methodology for tuning controllers in automated systems. By leveraging the Internal Model Control (IMC) framework, this tool allows developers to derive proportional and integral gains directly from identified process parameters such as gain ($K$) and time constant ($\tau$). This approach simplifies the complex task of manual PID tuning into a single-parameter decision process.
Using Openclaw Skills to implement these rules ensures that your first-order systems achieve guaranteed stability and predictable closed-loop responses. By focusing on the desired closed-loop speed ($\lambda$), engineers can effortlessly balance the trade-off between aggressive system performance and robust stability across various industrial or hobbyist automation projects.
IMC Tuning Rules Use Cases
- Automating the tuning process for industrial heating or cooling systems.
- Configuring flight controllers or motor speed regulators in robotics.
- Standardizing controller performance across multiple devices using Openclaw Skills.
- Rapidly prototyping PID loops where manual trial-and-error tuning is inefficient.
- Implementing robust control in IoT devices with uncertain model accuracy.
How IMC Tuning Rules Works
- Identify the process gain ($K$) and time constant ($\tau$) of your first-order system.
- Select a tuning parameter ($\lambda$) based on the desired closed-loop speed and robustness.
- Input the parameters into the IMC algorithm provided by this skill.
- The skill calculates the Proportional Gain ($K_p$) and Integral Gain ($K_i$).
- Apply the calculated gains to your PI controller implementation to achieve a predictable rise time and zero steady-state error.
IMC Tuning Rules Setup
To integrate these tuning rules into your project, ensure you have a Python environment ready. You can use the following structure to calculate gains within your Openclaw Skills workflow:
# No external dependencies required for the core logic.
# Simply include the calculation helper in your control script.
# Example calculation call
results = calculate_imc_gains(K=1.5, tau=10.0, lambda_factor=1.0)
print(f"Target Kp: {results['Kp']}")
IMC Tuning Rules Data Schema & Taxonomy
The skill utilizes a structured data format to manage process variables and controller outputs. Below is the metadata taxonomy for the gain calculation:
| Parameter | Description | Unit/Type |
|---|---|---|
K |
Process Gain | Numeric (Output/Input ratio) |
tau |
Time Constant | Time (Seconds/Minutes) |
lambda |
Desired Response Speed | Time (Seconds/Minutes) |
Kp |
Proportional Gain | Calculated Output |
Ki |
Integral Gain | Calculated Output (1/Time) |
Kd |
Derivative Gain | Constant (0 for 1st order) |
IMC Tuning Rules Advanced Features
- Single-parameter tuning via Lambda scaling to manage aggressive vs. conservative system behavior.
- Predictable closed-loop response modeling ($y(t)$) to forecast settling times before deployment.
- Native support for anti-windup logic integration within Openclaw Skills to handle output saturation.
- Mathematical verification of stability for all first-order process models.
SKILL.md
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