flight-controls
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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'- Fluxly category
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Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
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The current SKILL.md does not define a fixed output example. Usage
For visualization (Bode plots, step response plots):
# Control Systems Engineering
## Usage
```bash
# 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):
```bash
python3 /workspace/bridge.py matplotlib_plot 'YOUR_PLOTTING_CODE'
``` Evidence boundary and execution chain
Author text anchors workflow facts; Fluxly only indexes current sections, terms, files, and commands.
sections -> Usage
terms -> no emphasized key terms
files/cmd -> workspace/bridge.py
body sha256 -> c4471f68b5dc
Decide Fit First
Design Intent
How To Use It
Boundaries And Review