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mpc-configurator skill

/plugins/babysitter/skills/babysit/process/specializations/domains/science/chemical-engineering/skills/mpc-configurator

This skill configures and tunes Model Predictive Control systems, guiding model identification, horizon selection, and performance optimization.

npx playbooks add skill a5c-ai/babysitter --skill mpc-configurator

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: mpc-configurator
description: Model Predictive Control configuration skill for MPC model identification, tuning, and implementation
allowed-tools:
  - Read
  - Write
  - Glob
  - Grep
  - Edit
  - Bash
metadata:
  specialization: chemical-engineering
  domain: science
  category: Process Control
  skill-id: CE-SK-021
---

# MPC Configurator Skill

## Purpose

The MPC Configurator Skill supports Model Predictive Control implementation including model identification, controller configuration, and performance tuning.

## Capabilities

- Step test design and execution
- Dynamic model identification
- MPC model validation
- CV/MV/DV selection
- Constraint configuration
- Objective function tuning
- Prediction/control horizon selection
- Move suppression tuning
- Performance monitoring

## Usage Guidelines

### When to Use
- Implementing new MPC applications
- Retuning existing MPC controllers
- Identifying process models
- Optimizing MPC performance

### Prerequisites
- Regulatory control stable
- Step test data available
- Process constraints identified
- Economic objectives defined

### Best Practices
- Ensure quality step test data
- Validate models thoroughly
- Start with conservative tuning
- Monitor controller performance

## Process Integration

This skill integrates with:
- Model Predictive Control Implementation
- Control Strategy Development
- PID Controller Tuning

## Configuration

```yaml
mpc-configurator:
  platforms:
    - DMCplus
    - RMPCT
    - Pavilion
    - Honeywell-RMPCT
  identification-methods:
    - step-response
    - subspace
    - prediction-error
```

## Output Artifacts

- Process models
- Controller configuration
- Tuning parameters
- Validation reports
- Performance metrics

Overview

This skill provides a focused Model Predictive Control (MPC) configurator for model identification, controller setup, and performance tuning. It guides step test design, dynamic model estimation, constraint handling, and objective tuning to accelerate MPC deployment. The skill produces validated process models, controller parameters, and monitoring reports tailored to common industrial MPC platforms.

How this skill works

The skill inspects process data from step tests and other identification experiments to estimate dynamic models using step-response, subspace, or prediction-error methods. It helps select controlled, manipulated, and disturbance variables, suggests prediction and control horizons, and configures constraints and move suppression. Built-in validation routines compare simulated MPC responses to measured data and generate tuning adjustments until performance and robustness targets are met.

When to use it

When implementing a new MPC application for a process or unit
When retuning or upgrading an existing MPC controller after process changes
When identifying or re-identifying process models from step-test data
When defining constraints and economic objectives for constrained control
When preparing MPC configuration artifacts for handoff or automation

Best practices

Collect high-quality, representative step tests with stable regulatory control
Start with conservative tuning and verify robustness before aggressive optimization
Validate identified models with hold-out data and closed-loop simulations
Document selected CV/MV/DV mappings, constraints, and economic weights
Monitor online performance metrics and iterate tuning after deployment

Example use cases

Designing step tests, estimating a multi-input multi-output process model, and generating DMC/RMPCT-compatible model files
Converting PID-controlled loops to MPC by selecting CVs/MVs, setting horizons, and creating initial tuning parameters
Retuning an MPC after a process retrofit by re-identifying dynamics and adjusting constraint handling
Running model validation and generating a report of prediction error, fit statistics, and recommended tuning changes
Creating configuration artifacts (tuning matrices, move suppression, horizons) for integration with platforms like DMCplus or Pavilion

FAQ

What identification methods are supported?

Step-response, subspace, and prediction-error identification methods are supported to fit a range of process dynamics.

What prerequisites are required before using the skill?

Stable regulatory control, good quality step-test data, defined process constraints, and clear economic objectives are recommended.

Which outputs does the skill generate?

It generates process models, controller configuration files, tuning parameters, validation reports, and performance metrics for deployment.