Ejecuta cualquier Skill en Manus
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Ejecuta cualquier Skill en Manus con un clic

flight-controls

Design and simulate control systems — transfer functions, PID controllers, Bode plots, root locus, stability analysis, state-space modeling. Uses scipy.signal and sympy.

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Resumen

Design and simulate control systems — transfer functions, PID controllers, Bode plots, root locus, stability analysis, state-space modeling. Uses scipy.signal and sympy.

Comando de instalación

npx skills add https://github.com/00000star/AGI_engineering_forge --skill flight-controls

Copia y pega este comando en Claude Code para instalar la habilidad

Fuente

00000star/AGI_engineering_forge

Estrellas0

Forks0

Actualizado26 de marzo de 2026, 19:32

SKILL.md

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name	flight-controls
description	Design and simulate control systems — transfer functions, PID controllers, Bode plots, root locus, stability analysis, state-space modeling. Uses scipy.signal and sympy.
metadata	{"openclaw":{"emoji":"🎛️","requires":{"anyBins":["python3"]}}}

Control Systems Engineering

Usage

# PID step response
python3 /workspace/bridge.py scipy_solver '
from scipy import signal
num = [129.6, 324, 81]
den = [15, 131.6, 324, 81]
sys = signal.TransferFunction(num, den)
t, y = signal.step(sys, T=np.linspace(0, 5, 1000))
overshoot = (max(y) - 1.0) * 100
print(f"Overshoot: {overshoot:.1f}%")
print(f"Final value: {y[-1]:.4f}")
'

# Symbolic transfer function
python3 /workspace/bridge.py sympy_math '
s = symbols("s")
G = 100 / (s**2 + 10*s + 100)
print(f"DC gain: {limit(G, s, 0)}")
print(f"Poles: {solve(s**2 + 10*s + 100, s)}")
'

For visualization (Bode plots, step response plots):

python3 /workspace/bridge.py matplotlib_plot 'YOUR_PLOTTING_CODE'

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Fuente

00000star

00000star/AGI_engineering_forge

Abrir repositorio de GitHub Ver repositorios del creador

Comando de instalación

Descarga

Ejecutar en Manus

Útil paraSOC

Ingenieros aeroespacialesArquitectura e ingeniería17-2011L4

name	flight-controls
description	Design and simulate control systems — transfer functions, PID controllers, Bode plots, root locus, stability analysis, state-space modeling. Uses scipy.signal and sympy.
metadata	{"openclaw":{"emoji":"🎛️","requires":{"anyBins":["python3"]}}}

Control Systems Engineering

Usage

# PID step response
python3 /workspace/bridge.py scipy_solver '
from scipy import signal
num = [129.6, 324, 81]
den = [15, 131.6, 324, 81]
sys = signal.TransferFunction(num, den)
t, y = signal.step(sys, T=np.linspace(0, 5, 1000))
overshoot = (max(y) - 1.0) * 100
print(f"Overshoot: {overshoot:.1f}%")
print(f"Final value: {y[-1]:.4f}")
'

# Symbolic transfer function
python3 /workspace/bridge.py sympy_math '
s = symbols("s")
G = 100 / (s**2 + 10*s + 100)
print(f"DC gain: {limit(G, s, 0)}")
print(f"Poles: {solve(s**2 + 10*s + 100, s)}")
'

For visualization (Bode plots, step response plots):

python3 /workspace/bridge.py matplotlib_plot 'YOUR_PLOTTING_CODE'