evo-r2r-linearization
SkillAI & modelsDerives linearized state-space model (A, B matrices) for 6-section R2R system, computes steady-state operating points, discretizes, and computes LQR gain.
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Then ask your AI: use the evo-r2r-linearization skill
What this skill tells your AI
The instructions your AI receives, as published by openlair/openskill in tasks-evolved/r2r-mpc-control/environment/skills/evo-r2r-linearization/SKILL.md and read by ahel’s review.
Computes linearized state-space model for R2R web handling systems.
Usage
import sys
sys.path.insert(0, '/app/environment/skills/evo-r2r-linearization/scripts')
from utils import (
compute_steady_state_velocities,
compute_steady_state_torques,
build_continuous_AB,
discretize_system,
compute_lqr_gain,
get_full_reference_state
)
Key Functions
compute_steady_state_velocities(T_ref, EA, v0)- Cascade velocities using (EA-T) formulacompute_steady_state_torques(T_ref, v_ref, R, fb)- Compute equilibrium torquesbuild_continuous_AB(T_ss, v_ss, EA, L, R, J, fb, v0)- Build 12x12 A and 12x6 B Jacobiansdiscretize_system(A_cont, B_cont, dt)- ZOH discretization via scipycompute_lqr_gain(Ad, Bd, Q, R_mat)- Solve DARE, return K_lqr and Pget_full_reference_state(T_ref, EA, v0, R, fb)- Get full x_ref and u_ref
Signals
- GitHub stars
- 89
- Forks
- 4
- Last commit
- Sep 2026
Advanced
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evo-r2r-linearization- Source
- github.com/openlair/openskill