MPC Controller Skill

SkillAI & models

Expert skill for Model Predictive Control implementation and tuning

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the MPC Controller Skill skill

What this skill tells your AI

The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/robotics-simulation/skills/mpc-controller/SKILL.md and read by ahel’s review.

Overview

Expert skill for designing, implementing, and tuning Model Predictive Controllers for robotic systems, including both linear and nonlinear MPC.

Capabilities

  • Derive kinematic and dynamic robot models
  • Formulate MPC optimization problems (QP, NLP)
  • Configure CasADi for symbolic differentiation
  • Set up ACADO code generation for real-time MPC
  • Implement constraint handling (velocity, acceleration, collision)
  • Configure cost function weights (tracking, control effort)
  • Implement warm starting for fast convergence
  • Set up NMPC for nonlinear systems
  • Configure terminal constraints and costs
  • Optimize solver parameters for real-time execution

Target Processes

  • mpc-controller-design.js
  • trajectory-optimization.js
  • dynamic-obstacle-avoidance.js
  • path-planning-algorithm.js

Dependencies

  • CasADi
  • ACADO Toolkit
  • OSQP
  • qpOASES
  • Ipopt

Usage Context

This skill is invoked when processes require advanced model-based control, trajectory tracking with constraints, or real-time optimization-based control strategies.

Output Artifacts

  • MPC formulation code
  • CasADi symbolic models
  • ACADO generated code
  • QP/NLP solver configurations
  • Cost function tuning parameters
  • Constraint specifications

Signals

GitHub stars
2k
Forks
112
Last commit
Sep 2026
Advanced
Item type
skill
Key
mpc-controller-skill
Source
github.com/a5c-ai/babysitter