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ashfordeOU (@ashfordeou) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Python Control Design by ashfordeou

Name: Python Control Design
Author: ashfordeOU

Python Control Design

Use when designing and validating feedback control laws with Python control-systems tooling: evaluate gain and phase margins against acceptance limits (6 dB and 45 degrees), classify closed-loop stability from the margins, and apply Ziegler-Nichols tuning to get initial PID gains. Supports controller sanity checks (positive proportional, non-negative integral and derivative gains) before simulation or root-locus and Bode iteration. Pairs with the ARP4754A development-assurance context for control law development. Trigger: control law, pid, transfer function, state space, gain margin, phase margin, root locus, bode, stability, controller tuning.

ashfordeOU 6727da4 3 files · 7.7 KB Updated Sep 17, 2026

File contents

Python Control Design (gnc-autonomy/control/python-control-design)

Use when the task is control law design and evaluation with Python control-systems tooling: margin checks, stability classification, and PID tuning.

Domain quick reference

Standard acceptance margins: gain margin >= 6 dB, phase margin

= 45 degrees.
A loop with both margins positive is closed-loop stable; a non-positive margin indicates instability.
Ziegler-Nichols continuous-cycling tuning from ultimate gain ku and ultimate period tu: kp = 0.6 * ku, ki = 2 * kp / tu, kd = kp * tu / 8.
Structural sanity for PID gains: kp > 0, ki >= 0, kd >= 0.
Python control tooling (control, slycot) computes margins, root locus, and Bode plots for iteration.

Workflow

Build the plant model as a transfer function or state-space system.
Compute gain and phase margins from the open-loop response and check them against the acceptance minima with scripts/python_control_logic.py.
Classify closed-loop stability from the margins.
Tune an initial PID with Ziegler-Nichols and sanity-check the gains.
Iterate with root-locus/Bode analysis until the margins pass.

Pitfalls

Calling the loop stable from a single positive margin.
Tuning PID gains without checking the ultimate gain/period validity.
Mixing dB and ratio margin values in one comparison.
Accepting negative integral or derivative gains silently.

Behavior contract (gate 3)

The margin, stability, and tuning logic is exercised by the gate 3 contract test: scripts/test_python_control.py against scripts/python_control_logic.py (stdlib unittest, offline). Run: python3 scripts/test_python_control.py

Compliance

ARP4754A is proprietary (SAE); name + paraphrase only per standards-map.yaml and brief 06 (revision note: ARP4754B supersedes; this skill keys to A, the certification-baseline revision).
compliance: STANDARDS-REF, gated: false.

---name: python-control-designdescription: Use when designing and validating feedback control laws with Python control-systems tooling: evaluate gain and phase margins against acceptance limits (6 dB and 45 degrees), classify closed-loop stability from the margins, and apply Ziegler-Nichols tuning to get initial PID gains. Supports controller sanity checks (positive proportional, non-negative integral and derivative gains) before simulation or root-locus and Bode iteration. Pairs with the ARP4754A development-assurance context for control law development. Trigger: control law, pid, transfer function, state space, gain margin, phase margin, root locus, bode, stability, controller tuning.license: Apache-2.0---# Python Control Design (gnc-autonomy/control/python-control-design)Use when the task is control law design and evaluation with Pythoncontrol-systems tooling: margin checks, stability classification,and PID tuning.## Domain quick reference- Standard acceptance margins: gain margin >= 6 dB, phase margin >= 45 degrees.- A loop with both margins positive is closed-loop stable; a non-positive margin indicates instability.- Ziegler-Nichols continuous-cycling tuning from ultimate gain ku and ultimate period tu: kp = 0.6 * ku, ki = 2 * kp / tu, kd = kp * tu / 8.- Structural sanity for PID gains: kp > 0, ki >= 0, kd >= 0.- Python control tooling (control, slycot) computes margins, root locus, and Bode plots for iteration.## Workflow1. Build the plant model as a transfer function or state-space system.2. Compute gain and phase margins from the open-loop response and check them against the acceptance minima with scripts/python_control_logic.py.3. Classify closed-loop stability from the margins.4. Tune an initial PID with Ziegler-Nichols and sanity-check the gains.5. Iterate with root-locus/Bode analysis until the margins pass.## Pitfalls- Calling the loop stable from a single positive margin.- Tuning PID gains without checking the ultimate gain/period validity.- Mixing dB and ratio margin values in one comparison.- Accepting negative integral or derivative gains silently.## Behavior contract (gate 3)The margin, stability, and tuning logic is exercised by the gate 3contract test: scripts/test_python_control.py againstscripts/python_control_logic.py (stdlib unittest, offline). Run:python3 scripts/test_python_control.py## Compliance- ARP4754A is proprietary (SAE); name + paraphrase only per standards-map.yaml and brief 06 (revision note: ARP4754B supersedes; this skill keys to A, the certification-baseline revision).- compliance: STANDARDS-REF, gated: false.

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