Babysitter mpc-configurator

Model Predictive Control configuration skill for MPC model identification, tuning, and implementation

install

source · Clone the upstream repo

git clone https://github.com/a5c-ai/babysitter

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/domains/science/chemical-engineering/skills/mpc-configurator" ~/.claude/skills/a5c-ai-babysitter-mpc-configurator && rm -rf "$T"

manifest: library/specializations/domains/science/chemical-engineering/skills/mpc-configurator/SKILL.md

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

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