create a pid controller for a uav in python

To create a PID controller for a UAV in Python, you can use the control library, which provides a comprehensive set of tools for analyzing and designing classical and modern control systems.

Here's an example of how to create a PID controller for a UAV:


main.py
from control import matlab, margin, pid

# Plant transfer function
num = [1]
den = [1, 3, 2]
P = matlab.tf(num, den)

# Design PID controller
Kp = 1.2
Ki = 1.0
Kd = 0.2
C = pid(Kp, Ki, Kd)

# Open-loop transfer function
L = P * C

# Compute stability margins
gm, pm, wg, wp = margin(L)

# Print results
print('Gain margin: {:.2f} dB'.format(20 * math.log10(gm)))
print('Phase margin: {:.2f} deg'.format(pm))
print('Crossover frequency: {:.2f} rad/s'.format(wp))

# Plot Bode diagram
mag, phase, omega = matlab.bode(L, dB=True, Hz=True)

# Simulate system response
t = numpy.linspace(0, 20, 1000)
u = numpy.ones_like(t)
t, y, x = matlab.lsim(P, u, t)
t, yout, xout = matlab.lsim(L, u, t)

# Plot system response
matplotlib.pyplot.plot(t, y, label='Plant')
matplotlib.pyplot.plot(t, yout, label='System')
matplotlib.pyplot.legend()
matplotlib.pyplot.show()
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In this example, we first define the plant transfer function P as a second-order system with a pole at -1 and a zero at -2. We then design a PID controller C with gains Kp, Ki, and Kd. We compute the open-loop transfer function L as the product of P and C, and then compute the stability margins gm, pm, wg, and wp using the margin function. Finally, we plot the Bode diagram of L, simulate the system response to a step input, and plot the results.

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