benchflow-ai/skillsbench/mpc-horizon-tuning

Name: mpc-horizon-tuning
Author: benchflow-ai

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By benchflow-ai
AI/ML
benchflow-ai/skillsbench
tasks/r2r-mpc-control/environment/skills/mpc-horizon-tuning/SKILL.md
Updated 25 Sep 2026
Version 1.0.01 version recorded

Source check pending

Checking that the SKILL.md file is still in the repository.

Selecting MPC prediction horizon and cost matrices for web handling.

Install

npx vskill@latest install benchflow-ai/skillsbench/mpc-horizon-tuning

SKILL.md

MPC Tuning for Tension Control

Prediction Horizon Selection

Horizon N affects performance and computation:

Too short (N < 5): Poor disturbance rejection
Too long (N > 20): Excessive computation
Rule of thumb: N ≈ 2-3× settling time / dt

For R2R systems with dt=0.01s: N = 5-15 typical

Cost Matrix Design

State cost Q: Emphasize tension tracking

python

Q_tension = 100 / T_ref²  # High weight on tensions
Q_velocity = 0.1 / v_ref²  # Lower weight on velocities
Q = diag([Q_tension × 6, Q_velocity × 6])

Control cost R: Penalize actuator effort

python

R = 0.01-0.1 × eye(n_u)  # Smaller = more aggressive

Trade-offs

Higher Q	Effect
Faster tracking	More control effort
Lower steady-state error	More aggressive transients

Higher R	Effect
Smoother control	Slower response
Less actuator wear	Higher tracking error

Terminal Cost

Use LQR solution for terminal cost to guarantee stability:

python

P = solve_continuous_are(A, B, Q, R)

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Source

SKILL.md in the repositoryCheckingtasks/r2r-mpc-control/environment/skills/mpc-horizon-tuning/SKILL.md

Popularity

Repository stars: 1,765
Repository forks: 367
7-day trend score: 37

Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 12 Sep 2026, a month ago.

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