finite-horizon-lqr
CommunityEfficient MPC control via finite-horizon LQR.
AuthorKaiserWhoLearns
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Solves finite-horizon LQR problems for MPC by computing the optimal sequence of state-feedback gains.
Core Features & Use Cases
- Backward Riccati recursion to compute optimal feedback gains for each step.
- Forward simulation using the computed gains to generate the first control input from an initial state.
- Applicable to linear-quadratic control problems within model-predictive control workflows and real-time scenarios.
Quick Start
Run finite-horizon LQR with your A, B, Q, R matrices and horizon N to compute the first control input from x0.
Dependency Matrix
Required Modules
numpy
Components
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: finite-horizon-lqr Download link: https://github.com/KaiserWhoLearns/skillsbench/archive/main.zip#finite-horizon-lqr Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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