62 lines
1.4 KiB
Python
62 lines
1.4 KiB
Python
import os
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import matplotlib.pyplot as plt
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from control.matlab import *
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import numpy as np
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# Input: Current (A)
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# Output: Torque (Nm)
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# Params: Kt (Nm/A)
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def motor(Kt):
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return tf(Kt, 1)
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# Mass-Spring-Damper
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# Input: Force
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# Output: Position
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# Params: m (kg)
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# b
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# k (N/m)
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def mass(m, b, k):
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A = [[0, 1.], [-k/m, -b/m]]
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B = [[0], [1/m]]
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C = [[1., 0]]
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return ss(A, B, C, 0)
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# Input: Torque (Nm)
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# Output: Force (N)
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# Params: r (m)
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def pulley(r):
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return tf(r, 1)
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# Make s a transfer function s/1
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s = tf('s')
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print(s)
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# build a new transfer function using our variable s as a handy placeholder
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sys = 1 / (s*s + s + 1)
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print(sys)
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# Hit the system with a step command
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yout, T = step(sys)
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plt.plot(T, yout)
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# convert our continuous time model to discrete time via Tustin at 0.01s timestep
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sysd = c2d(tf(sys), 0.01, method='tustin')
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print(sysd)
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# Hit the discrete system with a step command, and sample it at 0.01 timestep from 0 to 14 seconds
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yout, T = step(sysd, np.arange(0, 14, 0.01))
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plt.plot(T, yout)
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plt.legend(['Continuous', 'Discrete'])
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# Build a system based on the series connection of the motor, pulley, and mass "blocks"
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sys = series(motor(2.5), pulley(0.015), mass(0.10, .1, .1))
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print(tf(sys))
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# Step our series system, returning y (outputs) and x (states)
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yout, T, xout = step(sys, return_x=True)
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plt.figure()
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plt.plot(T, yout)
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plt.plot(T, xout)
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plt.legend(['Displacement', r'$x$', r'$\dot{x}$'])
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plt.show() |