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33 lines
852 B
33 lines
852 B
6 years ago
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#!/usr/bin/env python3
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import numpy as np
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import control
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dt = 0.01
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A = np.array([[ 0. , 1. ], [-0.78823806, 1.78060701]])
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B = np.array([[-2.23399437e-05], [ 7.58330763e-08]])
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C = np.array([[1., 0.]])
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# Kalman tuning
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Q = np.diag([1, 1])
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R = np.atleast_2d(1e5)
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(_, _, L) = control.dare(A.T, C.T, Q, R)
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L = L.T
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# LQR tuning
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Q = np.diag([2e5, 1e-5])
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R = np.atleast_2d(1)
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(_, _, K) = control.dare(A, B, Q, R)
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A_cl = (A - B.dot(K))
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sys = control.ss(A_cl, B, C, 0, dt)
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dc_gain = control.dcgain(sys)
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print(("self.A = np." + A.__repr__()).replace('\n', ''))
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print(("self.B = np." + B.__repr__()).replace('\n', ''))
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print(("self.C = np." + C.__repr__()).replace('\n', ''))
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print(("self.K = np." + K.__repr__()).replace('\n', ''))
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print(("self.L = np." + L.__repr__()).replace('\n', ''))
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print("self.dc_gain = " + str(dc_gain))
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