A precise feedback control mechanism that calculates error values and applies corrections via proportional, integral, and derivative terms.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install adaptive-cruise-control-pid-controller
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 adaptive-cruise-control-pid-controller 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 PID Controller Implementation?
The PID Controller is a specialized component within the Openclaw Skills ecosystem designed for industrial and mechanical control systems. By continuously monitoring the difference between a desired setpoint and a measured variable, it applies calculated adjustments to maintain system stability. This implementation provides a standardized way to integrate complex feedback loops into your AI agents, making Openclaw Skills more capable of handling physical-world interactions and adaptive processes.
PID Controller Implementation Use Cases
- Regulating vehicle speed for adaptive cruise control systems.
- Managing throttle and brake inputs in autonomous navigation.
- Controlling temperature and environmental variables in smart building automation.
- Stabilizing robotic movements and mechanical positioning tasks.
- Managing pressure and flow rates in industrial fluid systems.
How PID Controller Implementation Works
- The controller calculates the current error by subtracting the measured value from the target setpoint.
- The proportional term (Kp) produces an output proportional to the current error magnitude.
- The integral term (Ki) accounts for the accumulation of past errors to eliminate steady-state offsets.
- The derivative term (Kd) estimates the future trend of the error based on its current rate of change.
- The combined output is processed through optional clamping to ensure it stays within operational limits.
- The state is updated, saving the current error for the next derivative calculation cycle.
PID Controller Implementation Setup
To utilize this implementation within your Openclaw Skills project, incorporate the controller class into your agent's toolset.
# Example of integrating the PID logic into your environment
openclaw install pid-controller-skill
Initialize the controller with your specific gains:
controller = PIDController(kp=1.2, ki=0.5, kd=0.1, output_min=0, output_max=100)
PID Controller Implementation Data Schema & Taxonomy
The PID Controller manages internal state to track time-series error data. Below is the primary metadata and state structure used by these Openclaw Skills:
| Parameter | Type | Description |
|---|---|---|
kp |
Float | Proportional gain for immediate error correction. |
ki |
Float | Integral gain for resolving cumulative error. |
kd |
Float | Derivative gain for dampening oscillations. |
integral |
Float | The accumulated sum of error over time. |
prev_error |
Float | The error value from the previous time step. |
output_min/max |
Float | Bounds for the control signal output. |
PID Controller Implementation Advanced Features
- Anti-windup protection via clamping and back-calculation to prevent integral saturation.
- Discrete-time implementation support for variable timestep (dt) environments.
- Manual tuning guidelines for optimizing Kp, Ki, and Kd parameters within Openclaw Skills.
- State reset functionality for clearing integral history during system transitions.
- High-precision derivative calculations with noise sensitivity management.
SKILL.md
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