blob: 710233bcb990fc4826f4468b019ab76d38fd2ff3 [file] [log] [blame]
#!/usr/bin/python3
import gflags
import glog
import argparse
import numpy
import sys
from matplotlib import pylab
from frc971.control_loops.python import control_loop
FLAGS = gflags.FLAGS
gflags.DEFINE_bool('plot', False, 'If true, plot the loop response.')
class SprungShooter(control_loop.ControlLoop):
def __init__(self, name="RawSprungShooter"):
super(SprungShooter, self).__init__(name)
# Stall Torque in N m
self.stall_torque = .4982
# Stall Current in Amps
self.stall_current = 85
# Free Speed in RPM
self.free_speed = 19300.0
# Free Current in Amps
self.free_current = 1.2
# Effective mass of the shooter in kg.
# This rough estimate should about include the effect of the masses
# of the gears. If this number is too low, the eigen values of self.A
# will start to become extremely small.
self.J = 200
# Resistance of the motor, divided by the number of motors.
self.R = 12.0 / self.stall_current / 2.0
# Motor velocity constant
self.Kv = ((self.free_speed / 60.0 * 2.0 * numpy.pi) /
(12.0 - self.R * self.free_current))
# Torque constant
self.Kt = self.stall_torque / self.stall_current
# Spring constant for the springs, N/m
self.Ks = 2800.0
# Maximum extension distance (Distance from the 0 force point on the
# spring to the latch position.)
self.max_extension = 0.32385
# Gear ratio multiplied by radius of final sprocket.
self.G = 10.0 / 40.0 * 20.0 / 54.0 * 24.0 / 54.0 * 20.0 / 84.0 * 16.0 * (
3.0 / 8.0) / (2.0 * numpy.pi) * 0.0254
# Control loop time step
self.dt = 0.005
# State feedback matrices
self.A_continuous = numpy.matrix(
[[0, 1],
[
-self.Ks / self.J,
-self.Kt / self.Kv / (self.J * self.G * self.G * self.R)
]])
self.B_continuous = numpy.matrix(
[[0], [self.Kt / (self.J * self.G * self.R)]])
self.C = numpy.matrix([[1, 0]])
self.D = numpy.matrix([[0]])
self.A, self.B = self.ContinuousToDiscrete(self.A_continuous,
self.B_continuous, self.dt)
self.PlaceControllerPoles([0.4501, 0.4499])
self.PlaceObserverPoles([0.05001, 0.04999])
self.U_max = numpy.matrix([[12.0]])
self.U_min = numpy.matrix([[-12.0]])
self.InitializeState()
class Shooter(SprungShooter):
def __init__(self, name="RawShooter"):
super(Shooter, self).__init__(name)
# State feedback matrices
self.A_continuous = numpy.matrix(
[[0, 1],
[0, -self.Kt / self.Kv / (self.J * self.G * self.G * self.R)]])
self.B_continuous = numpy.matrix(
[[0], [self.Kt / (self.J * self.G * self.R)]])
self.A, self.B = self.ContinuousToDiscrete(self.A_continuous,
self.B_continuous, self.dt)
self.PlaceControllerPoles([0.45001, 0.44999])
self.PlaceObserverPoles([0.05001, 0.04999])
self.U_max = numpy.matrix([[12.0]])
self.U_min = numpy.matrix([[-12.0]])
self.InitializeState()
class SprungShooterDeltaU(SprungShooter):
def __init__(self, name="SprungShooter"):
super(SprungShooterDeltaU, self).__init__(name)
A_unaugmented = self.A
B_unaugmented = self.B
A_continuous_unaugmented = self.A_continuous
B_continuous_unaugmented = self.B_continuous
self.A_continuous = numpy.matrix(numpy.zeros((3, 3)))
self.A_continuous[0:2, 0:2] = A_continuous_unaugmented
self.A_continuous[0:2, 2] = B_continuous_unaugmented
self.B_continuous = numpy.matrix(numpy.zeros((3, 1)))
self.B_continuous[2, 0] = 1.0 / self.dt
self.A = numpy.matrix([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0],
[0.0, 0.0, 1.0]])
self.A[0:2, 0:2] = A_unaugmented
self.A[0:2, 2] = B_unaugmented
self.B = numpy.matrix([[0.0], [0.0], [1.0]])
self.C = numpy.matrix([[1.0, 0.0, 0.0]])
self.D = numpy.matrix([[0.0]])
self.PlaceControllerPoles([0.50, 0.35, 0.80])
glog.debug('K')
glog.debug(str(self.K))
glog.debug('Placed controller poles are')
glog.debug(str(numpy.linalg.eig(self.A - self.B * self.K)[0]))
self.rpl = .05
self.ipl = 0.008
self.PlaceObserverPoles(
[self.rpl + 1j * self.ipl, self.rpl - 1j * self.ipl, 0.90])
glog.debug('Placed observer poles are')
glog.debug(str(numpy.linalg.eig(self.A - self.L * self.C)[0]))
self.U_max = numpy.matrix([[12.0]])
self.U_min = numpy.matrix([[-12.0]])
self.InitializeState()
class ShooterDeltaU(Shooter):
def __init__(self, name="Shooter"):
super(ShooterDeltaU, self).__init__(name)
A_unaugmented = self.A
B_unaugmented = self.B
A_continuous_unaugmented = self.A_continuous
B_continuous_unaugmented = self.B_continuous
self.A_continuous = numpy.matrix(numpy.zeros((3, 3)))
self.A_continuous[0:2, 0:2] = A_continuous_unaugmented
self.A_continuous[0:2, 2] = B_continuous_unaugmented
self.B_continuous = numpy.matrix(numpy.zeros((3, 1)))
self.B_continuous[2, 0] = 1.0 / self.dt
self.A = numpy.matrix([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0],
[0.0, 0.0, 1.0]])
self.A[0:2, 0:2] = A_unaugmented
self.A[0:2, 2] = B_unaugmented
self.B = numpy.matrix([[0.0], [0.0], [1.0]])
self.C = numpy.matrix([[1.0, 0.0, 0.0]])
self.D = numpy.matrix([[0.0]])
self.PlaceControllerPoles([0.55, 0.45, 0.80])
glog.debug('K')
glog.debug(str(self.K))
glog.debug('Placed controller poles are')
glog.debug(str(numpy.linalg.eig(self.A - self.B * self.K)[0]))
self.rpl = .05
self.ipl = 0.008
self.PlaceObserverPoles(
[self.rpl + 1j * self.ipl, self.rpl - 1j * self.ipl, 0.90])
glog.debug('Placed observer poles are')
glog.debug(str(numpy.linalg.eig(self.A - self.L * self.C)[0]))
self.U_max = numpy.matrix([[12.0]])
self.U_min = numpy.matrix([[-12.0]])
self.InitializeState()
def ClipDeltaU(shooter, old_voltage, delta_u):
old_u = old_voltage
new_u = numpy.clip(old_u + delta_u, shooter.U_min, shooter.U_max)
return new_u - old_u
def main(argv):
argv = FLAGS(argv)
# Simulate the response of the system to a goal.
sprung_shooter = SprungShooterDeltaU()
raw_sprung_shooter = SprungShooter()
close_loop_x = []
close_loop_u = []
goal_position = -0.3
R = numpy.matrix(
[[goal_position], [0.0],
[-sprung_shooter.A[1, 0] / sprung_shooter.A[1, 2] * goal_position]])
voltage = numpy.matrix([[0.0]])
for _ in range(500):
U = sprung_shooter.K * (R - sprung_shooter.X_hat)
U = ClipDeltaU(sprung_shooter, voltage, U)
sprung_shooter.Y = raw_sprung_shooter.Y + 0.01
sprung_shooter.UpdateObserver(U)
voltage += U
raw_sprung_shooter.Update(voltage)
close_loop_x.append(raw_sprung_shooter.X[0, 0] * 10)
close_loop_u.append(voltage[0, 0])
if FLAGS.plot:
pylab.plot(range(500), close_loop_x)
pylab.plot(range(500), close_loop_u)
pylab.show()
shooter = ShooterDeltaU()
raw_shooter = Shooter()
close_loop_x = []
close_loop_u = []
goal_position = -0.3
R = numpy.matrix([[goal_position], [0.0],
[-shooter.A[1, 0] / shooter.A[1, 2] * goal_position]])
voltage = numpy.matrix([[0.0]])
for _ in range(500):
U = shooter.K * (R - shooter.X_hat)
U = ClipDeltaU(shooter, voltage, U)
shooter.Y = raw_shooter.Y + 0.01
shooter.UpdateObserver(U)
voltage += U
raw_shooter.Update(voltage)
close_loop_x.append(raw_shooter.X[0, 0] * 10)
close_loop_u.append(voltage[0, 0])
if FLAGS.plot:
pylab.plot(range(500), close_loop_x)
pylab.plot(range(500), close_loop_u)
pylab.show()
# Write the generated constants out to a file.
unaug_sprung_shooter = SprungShooter("RawSprungShooter")
unaug_shooter = Shooter("RawShooter")
namespaces = ['y2014', 'control_loops', 'shooter']
unaug_loop_writer = control_loop.ControlLoopWriter(
"RawShooter", [unaug_sprung_shooter, unaug_shooter],
namespaces=namespaces)
unaug_loop_writer.Write(argv[4], argv[3])
sprung_shooter = SprungShooterDeltaU()
shooter = ShooterDeltaU()
loop_writer = control_loop.ControlLoopWriter(
"Shooter", [sprung_shooter, shooter], namespaces=namespaces)
loop_writer.AddConstant(
control_loop.Constant("kMaxExtension", "%f",
sprung_shooter.max_extension))
loop_writer.AddConstant(
control_loop.Constant("kSpringConstant", "%f", sprung_shooter.Ks))
loop_writer.AddConstant(
control_loop.Constant("kDt", "%f", sprung_shooter.dt))
loop_writer.Write(argv[2], argv[1])
if __name__ == '__main__':
sys.exit(main(sys.argv))