| 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"} |
| graph | {"domains":["domain:chemical-engineering"],"skillAreas":["skill-area:mathematical-reasoning","skill-area:physics-simulation","skill-area:dynamic-programming"],"workflows":["workflow:experiment-design"],"roles":["role:research-engineer","role:systems-integration-engineer"]} |
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
