evo-r2r-mpc-controller
SkillMonitoring & opsImplements MPC controller with prediction matrices, QP formulation, simulation loop for R2R web handling. Generates controller_params.json, control_log.json, and metrics.json.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the evo-r2r-mpc-controller 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-mpc-controller/SKILL.md and read by ahel’s review.
MPC controller for 6-section R2R system with reference step tracking.
Usage
import sys
sys.path.insert(0, '/app/environment/skills/evo-r2r-mpc-controller/scripts')
from utils import (
build_prediction_matrices,
build_qp_matrices,
solve_mpc_step,
run_simulation_loop,
compute_performance_metrics,
save_output_files
)
Key Functions
build_prediction_matrices(Ad, Bd, N)- Build Phi and Gamma matricesbuild_qp_matrices(Phi, Gamma, Q, R_mat, P, N)- Build QP Hessian and gradientsolve_mpc_step(H, F, dx, N, nu)- Solve one MPC step (unconstrained)run_simulation_loop(sim, linearize_fn, get_ref_fn, Q, R_mat, N, num_steps)- Full sim loopcompute_performance_metrics(log_data, T_ref_final)- Compute SSE, settling time, etc.save_output_files(controller_params, log_data, metrics)- Save all 3 JSON files
Output Files
- controller_params.json: A_matrix (continuous), B_matrix (continuous), K_lqr, Q_diag, R_diag, horizon_N
- control_log.json: phase="control", data array with time/tensions/velocities/control_inputs/references
- metrics.json: steady_state_error, settling_time, max_tension, min_tension
Signals
- GitHub stars
- 89
- Forks
- 4
- Last commit
- Sep 2026
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evo-r2r-mpc-controller- Source
- github.com/openlair/openskill