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pid-tuner skill

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This skill optimizes PID controller parameters using IMC, Lambda, Cohen-Coon, and Ziegler-Nichols methods to improve stability and responsiveness.

npx playbooks add skill a5c-ai/babysitter --skill pid-tuner

Review the files below or copy the command above to add this skill to your agents.

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SKILL.md

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---
name: pid-tuner
description: PID controller tuning skill for loop optimization using various tuning methods and performance criteria
allowed-tools:
  - Read
  - Write
  - Glob
  - Grep
  - Edit
  - Bash
metadata:
  specialization: chemical-engineering
  domain: science
  category: Process Control
  skill-id: CE-SK-020
---

# PID Tuner Skill

## Purpose

The PID Tuner Skill optimizes PID controller parameters using various tuning methods to achieve desired control performance and robustness.

## Capabilities

- Process identification (step tests, relay)
- First-order plus dead-time (FOPDT) modeling
- Tuning methods (IMC, Lambda, Cohen-Coon, Ziegler-Nichols)
- Performance criteria optimization (IAE, ISE, ITAE)
- Robustness analysis
- Loop interaction assessment
- Tuning for various objectives (setpoint, disturbance)
- Bumpless transfer configuration

## Usage Guidelines

### When to Use
- Tuning new control loops
- Retuning underperforming loops
- Optimizing control performance
- Commissioning control systems

### Prerequisites
- Process in stable operation
- Loop components commissioned
- Process model or test data available
- Performance criteria defined

### Best Practices
- Start with conservative tuning
- Test in simulation first
- Validate robustness
- Document tuning rationale

## Process Integration

This skill integrates with:
- PID Controller Tuning
- Control Strategy Development
- Process Startup Procedure Development

## Configuration

```yaml
pid-tuner:
  tuning-methods:
    - IMC
    - lambda
    - cohen-coon
    - ziegler-nichols
    - SIMC
  performance-criteria:
    - IAE
    - ISE
    - ITAE
```

## Output Artifacts

- Tuning parameters
- Process models
- Performance metrics
- Robustness analysis
- Tuning recommendations

Overview

This skill provides PID controller tuning and loop optimization using multiple established methods and performance criteria. It produces recommended gains, process models, and robustness assessments to improve setpoint tracking and disturbance rejection. It is designed for commissioning, retuning, and performance-driven optimization of control loops.

How this skill works

The skill performs process identification using step tests or relay methods and fits FOPDT (first-order plus dead-time) models. It applies several tuning rules (IMC, Lambda, Cohen-Coon, Ziegler-Nichols, SIMC) and optimizes parameters against IAE, ISE, or ITAE criteria. It then evaluates robustness, loop interaction, and bumpless transfer settings and outputs tuned parameters, model files, and performance metrics.

When to use it

Tuning a new PID loop during commissioning
Retuning loops that show oscillation, poor settling, or sluggish response
Optimizing controller settings for a specific performance metric (IAE/ISE/ITAE)
Assessing robustness before deploying aggressive gains
Validating controller changes in simulation before plant rollout

Best practices

Start with conservative gains and verify behavior in simulation or a test environment
Collect clear step or relay test data with the process in stable operation
Select performance criteria that match control priorities (setpoint vs disturbance rejection)
Validate robustness across expected process gain and dead-time variations
Document model fit, tuning rationale, and test cases for traceability

Example use cases

Commissioning a temperature or flow loop with FOPDT identification and IMC tuning
Retuning a pressure control loop that overshoots by optimizing for ITAE to reduce settling time
Comparing Cohen-Coon and SIMC results to balance aggressive response and robustness
Running relay tests to identify dynamics for constrained control systems
Generating tuning recommendations and robustness reports for operator handover

FAQ

What data do I need to get a reliable tuning?

Provide step or relay test data with the process operating stably, and include measured input/output timestamps. Longer tests with clear settling yield better FOPDT fits.

Which tuning method should I pick first?

Start with IMC or SIMC for a balance of performance and robustness. Use Ziegler-Nichols for quick aggressive tuning and Cohen-Coon when dead-time is significant.

Can I tune for disturbance rejection instead of setpoint?

Yes. Choose performance criteria and tuning objectives aligned to disturbance rejection (e.g., IAE on disturbance steps) and validate in simulation.