# Mpc Configurator

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

- Skill: `a5c-ai/mpc-configurator` (Agent Skill)
- Install (CLI): `npx skillmds@latest add a5c-ai/mpc-configurator`
- Raw SKILL.md: https://api.skillmd.com/api/skills/a5c-ai/mpc-configurator/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: a5c-ai (https://skillmd.com/u/a5c-ai)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/a5c-ai/mpc-configurator

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# 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