mpc-horizon-tuning

Tune MPC horizon and Q/R cost matrices for tension control in R2R systems.

4|Updated Feb 22, 2026

Search Tags:#mpc #control-systems #lqr #tension-control #horizon-tuning #are

Ready to run

npx skills add https://github.com/GeneralReasoning/env-skillsbench --skill mpc-horizon-tuning-generalreasoning

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: mpc-horizon-tuning
Source: https://github.com/GeneralReasoning/env-skillsbench/tree/main/r2r-mpc-control/environment/skills/mpc-horizon-tuning
Command: npx skills add https://github.com/GeneralReasoning/env-skillsbench --skill mpc-horizon-tuning-generalreasoning

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Selecting horizon length and cost matrices for model predictive control to balance performance and computation in tension-control systems.

Core Features & Use Cases

Horizon selection guidance for N based on dt and settling time.
Cost design guidelines for Q and R to prioritize tension tracking vs actuator effort.
Terminal cost guidance via LQR for stability.
Real-world scenario: tuning tension in web-handling lines and robotic arms.

Quick Start

Set N to a middle range (e.g., 5–15 for dt=0.01s) and choose Q and R as described to achieve stable tension tracking without excessive computation.

Frequently Asked Questions about mpc-horizon-tuning

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I tune MPC horizon length for tension control systems?▼

To tune MPC horizon length for tension control, select a prediction horizon N in the middle range (e.g., 5–15 for a discrete-time loop with dt=0.01s) to balance tracking performance against computational effort. This ensures stable operation without excessive calculation.

How do I design Q and R cost matrices for MPC tension tracking?▼

Design MPC cost matrices Q and R by weighting Q to prioritize precise tension tracking and R to penalize actuator effort. Adjusting these matrices lets you balance system responsiveness against input energy consumption in web-handling control loops.

Why use an LQR terminal cost for MPC stability in tension control?▼

An LQR terminal cost ensures MPC stability in tension control by approximating the infinite-horizon optimal cost at the end of the prediction window. This strategy guarantees closed-loop stability for discrete-time R2R systems.

Can I use this MPC horizon tuning approach for robotic arms and web-handling lines?▼

Yes, this MPC horizon tuning approach applies directly to real-world tension control scenarios including web-handling lines and robotic arms. It provides horizon selection and cost-design guidance for stable tension tracking.

What is the recommended settling time and horizon range for discrete-time tension control with dt=0.01s?▼

For discrete-time tension control with dt=0.01s, a recommended horizon range is 5–15 steps based on your system's settling time. This selection aligns horizon length with dynamics to prevent computational overload.