blob: b1f378e495de8672d62d0c4a41bfeabe2845a5c2 [file] [log] [blame]
#!/usr/bin/python
import control_loop
import controls
import numpy
import sys
from matplotlib import pylab
class Claw(control_loop.ControlLoop):
def __init__(self, name="RawClaw"):
super(Claw, self).__init__(name)
# Stall Torque in N m
self.stall_torque = 1.4
# Stall Current in Amps
self.stall_current = 86
# Free Speed in RPM
self.free_speed = 6200.0
# Free Current in Amps
self.free_current = 1.5
# Moment of inertia of the claw in kg m^2
# TODO(aschuh): Measure this in reality. It doesn't seem high enough.
# James measured 0.51, but that can't be right given what I am seeing.
self.J = 2.0
# Resistance of the motor
self.R = 12.0 / self.stall_current + 0.024 + .003
# Motor velocity constant
self.Kv = ((self.free_speed / 60.0 * 2.0 * numpy.pi) /
(13.5 - self.R * self.free_current))
# Torque constant
self.Kt = self.stall_torque / self.stall_current
# Gear ratio
self.G = 1.0 / ((84.0 / 20.0) * (50.0 / 14.0) * (40.0 / 14.0) * (40.0 / 12.0))
# Control loop time step
self.dt = 0.01
# State is [bottom position, top - bottom position,
# bottom velocity, top - bottom velocity]
# Input is [bottom power, top power]
# Motor time constant.
self.motor_timeconstant = self.Kt / self.Kv / (self.J * self.G * self.G * self.R)
# State feedback matrices
self.A_continuous = numpy.matrix(
[[0, 0, 1, 0],
[0, 0, 0, 1],
[0, 0, -self.motor_timeconstant, 0],
[0, 0, 0, -self.motor_timeconstant]])
self.motor_feedforward = self.Kt / (self.J * self.G * self.R)
self.B_continuous = numpy.matrix(
[[0, 0],
[0, 0],
[self.motor_feedforward, 0],
[-self.motor_feedforward, self.motor_feedforward]])
self.C = numpy.matrix([[1, 0, 0, 0],
[1, 1, 0, 0]])
self.D = numpy.matrix([[0, 0],
[0, 0]])
self.A, self.B = self.ContinuousToDiscrete(
self.A_continuous, self.B_continuous, self.dt)
#controlability = controls.ctrb(self.A, self.B);
#print "Rank of controlability matrix.", numpy.linalg.matrix_rank(controlability)
self.PlaceControllerPoles([0.85, 0.45, 0.45, 0.85])
self.rpl = .05
self.ipl = 0.008
self.PlaceObserverPoles([self.rpl + 1j * self.ipl,
self.rpl - 1j * self.ipl,
self.rpl + 1j * self.ipl,
self.rpl - 1j * self.ipl])
self.U_max = numpy.matrix([[12.0], [12.0]])
self.U_min = numpy.matrix([[-12.0], [-12.0]])
self.InitializeState()
class ClawDeltaU(Claw):
def __init__(self, name="Claw"):
super(ClawDeltaU, self).__init__(name)
A_unaugmented = self.A
B_unaugmented = self.B
C_unaugmented = self.C
self.A = numpy.matrix([[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 1.0]])
self.A[0:4, 0:4] = A_unaugmented
self.A[0:4, 4] = B_unaugmented[0:4, 0]
self.B = numpy.matrix([[0.0, 0.0],
[0.0, 0.0],
[0.0, 0.0],
[0.0, 0.0],
[1.0, 0.0]])
self.B[0:4, 1] = B_unaugmented[0:4, 1]
self.C = numpy.concatenate((C_unaugmented, numpy.matrix([[0.0], [0.0]])),
axis=1)
self.D = numpy.matrix([[0.0, 0.0],
[0.0, 0.0]])
#self.PlaceControllerPoles([0.55, 0.35, 0.55, 0.35, 0.80])
self.Q = numpy.matrix([[(1.0 / (0.04 ** 2.0)), 0.0, 0.0, 0.0, 0.0],
[0.0, (1.0 / (0.01 ** 2)), 0.0, 0.0, 0.0],
[0.0, 0.0, 0.01, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.08, 0.0],
[0.0, 0.0, 0.0, 0.0, (1.0 / (10.0 ** 2))]])
self.R = numpy.matrix([[0.000001, 0.0],
[0.0, 1.0 / (10.0 ** 2.0)]])
self.K = controls.dlqr(self.A, self.B, self.Q, self.R)
self.K = numpy.matrix([[50.0, 0.0, 10.0, 0.0, 1.0],
[50.0, 0.0, 10.0, 0.0, 1.0]])
#self.K = numpy.matrix([[50.0, -100.0, 0, -10, 0],
# [50.0, 100.0, 0, 10, 0]])
controlability = controls.ctrb(self.A, self.B);
print "Rank of augmented controlability matrix.", numpy.linalg.matrix_rank(controlability)
print "K"
print self.K
print "Placed controller poles are"
print numpy.linalg.eig(self.A - self.B * self.K)[0]
print [numpy.abs(x) for x in numpy.linalg.eig(self.A - self.B * self.K)[0]]
self.rpl = .05
self.ipl = 0.008
self.PlaceObserverPoles([self.rpl + 1j * self.ipl, 0.10, 0.09,
self.rpl - 1j * self.ipl, 0.90])
#print "A is"
#print self.A
#print "L is"
#print self.L
#print "C is"
#print self.C
#print "A - LC is"
#print self.A - self.L * self.C
#print "Placed observer poles are"
#print numpy.linalg.eig(self.A - self.L * self.C)[0]
self.U_max = numpy.matrix([[12.0], [12.0]])
self.U_min = numpy.matrix([[-12.0], [-12.0]])
self.InitializeState()
def ClipDeltaU(claw, U):
delta_u = U[0, 0]
top_u = U[1, 0]
old_bottom_u = claw.X[4, 0]
# TODO(austin): Preserve the difference between the top and bottom power.
new_unclipped_bottom_u = old_bottom_u + delta_u
#print "Bottom is", new_unclipped_bottom_u, "Top is", top_u
if new_unclipped_bottom_u > claw.U_max[0, 0]:
#print "Bottom is too big. Was", new_unclipped_bottom_u, "changing top by", new_unclipped_bottom_u - claw.U_max[0, 0]
top_u -= new_unclipped_bottom_u - claw.U_max[0, 0]
new_unclipped_bottom_u = claw.U_max[0, 0]
if top_u > claw.U_max[1, 0]:
new_unclipped_bottom_u -= top_u - claw.U_max[1, 0]
top_u = claw.U_max[1, 0]
if top_u < claw.U_min[1, 0]:
new_unclipped_bottom_u -= top_u - claw.U_min[1, 0]
top_u = claw.U_min[1, 0]
if new_unclipped_bottom_u < claw.U_min[0, 0]:
top_u -= new_unclipped_bottom_u - claw.U_min[0, 0]
new_unclipped_bottom_u = claw.U_min[0, 0]
new_bottom_u = numpy.clip(new_unclipped_bottom_u, claw.U_min[0, 0], claw.U_max[0, 0])
new_top_u = numpy.clip(top_u, claw.U_min[1, 0], claw.U_max[1, 0])
return numpy.matrix([[new_bottom_u - old_bottom_u], [new_top_u]])
def main(argv):
# Simulate the response of the system to a step input.
#claw = ClawDeltaU()
#simulated_x = []
#for _ in xrange(100):
# claw.Update(numpy.matrix([[12.0]]))
# simulated_x.append(claw.X[0, 0])
#pylab.plot(range(100), simulated_x)
#pylab.show()
# Simulate the closed loop response of the system to a step input.
top_claw = ClawDeltaU("TopClaw")
close_loop_x_bottom = []
close_loop_x_sep = []
close_loop_u_bottom = []
close_loop_u_top = []
R = numpy.matrix([[1.0], [0.0], [0.0], [0.0], [0.0]])
top_claw.X[0, 0] = 0
for _ in xrange(50):
#print "Error is", (R - top_claw.X_hat)
U = top_claw.K * (R - top_claw.X_hat)
U = ClipDeltaU(top_claw, U)
top_claw.UpdateObserver(U)
top_claw.Update(U)
close_loop_x_bottom.append(top_claw.X[0, 0] * 10)
close_loop_u_bottom.append(top_claw.X[4, 0])
close_loop_x_sep.append(top_claw.X[1, 0] * 10)
close_loop_u_top.append(U[1, 0])
pylab.plot(range(50), close_loop_x_bottom, label='x bottom')
pylab.plot(range(50), close_loop_u_bottom, label='u bottom')
pylab.plot(range(50), close_loop_x_sep, label='seperation')
pylab.plot(range(50), close_loop_u_top, label='u top')
pylab.legend()
pylab.show()
# Write the generated constants out to a file.
if len(argv) != 9:
print "Expected .h file name and .cc file name for"
print "both the plant and unaugmented plant"
else:
top_unaug_claw = Claw("RawTopClaw")
top_unaug_loop_writer = control_loop.ControlLoopWriter("RawTopClaw",
[top_unaug_claw])
if argv[1][-3:] == '.cc':
top_unaug_loop_writer.Write(argv[2], argv[1])
else:
top_unaug_loop_writer.Write(argv[1], argv[2])
top_loop_writer = control_loop.ControlLoopWriter("TopClaw", [top_claw])
if argv[3][-3:] == '.cc':
top_loop_writer.Write(argv[4], argv[3])
else:
top_loop_writer.Write(argv[3], argv[4])
bottom_claw = ClawDeltaU("BottomClaw")
bottom_unaug_claw = Claw("RawBottomClaw")
bottom_unaug_loop_writer = control_loop.ControlLoopWriter(
"RawBottomClaw", [bottom_unaug_claw])
if argv[5][-3:] == '.cc':
bottom_unaug_loop_writer.Write(argv[6], argv[5])
else:
bottom_unaug_loop_writer.Write(argv[5], argv[6])
bottom_loop_writer = control_loop.ControlLoopWriter("BottomClaw",
[bottom_claw])
if argv[7][-3:] == '.cc':
bottom_loop_writer.Write(argv[8], argv[7])
else:
bottom_loop_writer.Write(argv[7], argv[8])
if __name__ == '__main__':
sys.exit(main(sys.argv))