control-systems-state-estimation

name	control-systems-state-estimation
description	Design and debug state estimation, filtering, system identification, and control-oriented models for robotics, autonomous systems, and embedded control. Use when choosing observers, Kalman variants, sensor fusion structure, stability assumptions, or diagnosing drift, lag, and closed-loop estimation failures.

Control Systems State Estimation

Use this skill when sensing, dynamics, and feedback assumptions determine whether the whole system is stable.

Core Workflow

1. Define the state, control inputs, measurements, disturbances, and operating regime.
2. Choose the model fidelity needed for the decision at hand.
3. Use references/estimation-checklist.md to check observability, noise assumptions, delays, and linearization points.
4. Separate estimation quality from controller quality.
5. Validate in both nominal and failure regimes.

Execution Rules

1. Make continuous-time versus discrete-time assumptions explicit.
2. Track units and coordinate frames carefully.
3. Treat delay and bias as first-class model components.
4. Do not call a filter stable without the operating regime and assumptions.

Output Contract

Return:

1. State and measurement model.
2. Estimator choice rationale.
3. Failure modes.
4. Validation plan.
5. Open modeling risks.