mpc-horizon-tuning
Selecting MPC prediction horizon and cost matrices for web handling.
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Selecting MPC prediction horizon and cost matrices for web handling.
npxskills add benchflow-ai/skillsbench--skill mpc-horizon-tuningLoading…
Horizon N affects performance and computation:
For R2R systems with dt=0.01s: N = 5-15 typical
State cost Q: Emphasize tension tracking
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
R = 0.01-0.1 × eye(n_u) # Smaller = more aggressive
| 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 |
Use LQR solution for terminal cost to guarantee stability:
P = solve_continuous_are(A, B, Q, R)
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Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).