blob: 83beb90530bedb3512f7eef9284fe0ccdda886b0 [file] [log] [blame]
#!/usr/bin/python
import numpy
import sys
from matplotlib import pylab
import control_loop
class Shooter(control_loop.ControlLoop):
def __init__(self):
super(Shooter, self).__init__("Shooter")
# Stall Torque in N m
self.stall_torque = 0.49819248
# Stall Current in Amps
self.stall_current = 85
# Free Speed in RPM
self.free_speed = 19300.0 - 1500.0
# Free Current in Amps
self.free_current = 1.4
# Moment of inertia of the shooter wheel in kg m^2
self.J = 0.0032
# Resistance of the motor, divided by 2 to account for the 2 motors
self.R = 12.0 / self.stall_current / 2
# 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
# Gear ratio
self.G = 11.0 / 34.0
# Control loop time step
self.dt = 0.01
# 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.C = numpy.matrix([[1, 0]])
self.D = numpy.matrix([[0]])
self.ContinuousToDiscrete(self.A_continuous, self.B_continuous,
self.dt, self.C)
self.PlaceControllerPoles([.6, .981])
self.rpl = .45
self.ipl = 0.07
self.PlaceObserverPoles([self.rpl + 1j * self.ipl,
self.rpl - 1j * self.ipl])
self.U_max = numpy.matrix([[12.0]])
self.U_min = numpy.matrix([[-12.0]])
def main(argv):
# Simulate the response of the system to a step input.
shooter_data = numpy.genfromtxt('shooter/shooter_data.csv', delimiter=',')
shooter = Shooter()
simulated_x = []
real_x = []
x_vel = []
initial_x = shooter_data[0, 2]
last_x = initial_x
for i in xrange(shooter_data.shape[0]):
shooter.Update(numpy.matrix([[shooter_data[i, 1]]]))
simulated_x.append(shooter.X[0, 0])
x_offset = shooter_data[i, 2] - initial_x
real_x.append(x_offset)
x_vel.append((shooter_data[i, 2] - last_x) * 100.0)
last_x = shooter_data[i, 2]
sim_delay = 1
pylab.plot(range(sim_delay, shooter_data.shape[0] + sim_delay),
simulated_x, label='Simulation')
pylab.plot(range(shooter_data.shape[0]), real_x, label='Reality')
pylab.plot(range(shooter_data.shape[0]), x_vel, label='Velocity')
pylab.legend()
pylab.show()
# Simulate the closed loop response of the system to a step input.
shooter = Shooter()
close_loop_x = []
close_loop_U = []
velocity_goal = 300
R = numpy.matrix([[0.0], [velocity_goal]])
for _ in pylab.linspace(0,1.99,200):
# Iterate the position up.
R = numpy.matrix([[R[0, 0] + 10.5], [velocity_goal]])
# Prevents the position goal from going beyond what is necessary.
velocity_weight_scalar = 0.35
max_reference = (
(shooter.U_max[0, 0] - velocity_weight_scalar *
(velocity_goal - shooter.X_hat[1, 0]) * shooter.K[0, 1]) /
shooter.K[0, 0] +
shooter.X_hat[0, 0])
min_reference = (
(shooter.U_min[0, 0] - velocity_weight_scalar *
(velocity_goal - shooter.X_hat[1, 0]) * shooter.K[0, 1]) /
shooter.K[0, 0] +
shooter.X_hat[0, 0])
R[0, 0] = numpy.clip(R[0, 0], min_reference, max_reference)
U = numpy.clip(shooter.K * (R - shooter.X_hat),
shooter.U_min, shooter.U_max)
shooter.UpdateObserver(U)
shooter.Update(U)
close_loop_x.append(shooter.X[1, 0])
close_loop_U.append(U[0, 0])
#pylab.plotfile("shooter.csv", (0,1))
#pylab.plot(pylab.linspace(0,1.99,200), close_loop_U, 'ro')
#pylab.plotfile("shooter.csv", (0,2))
pylab.plot(pylab.linspace(0,1.99,200), close_loop_x, 'ro')
pylab.show()
# Simulate spin down.
spin_down_x = [];
R = numpy.matrix([[50.0], [0.0]])
for _ in xrange(150):
U = 0
shooter.UpdateObserver(U)
shooter.Update(U)
spin_down_x.append(shooter.X[1, 0])
#pylab.plot(range(150), spin_down_x)
#pylab.show()
if len(argv) != 3:
print "Expected .h file name and .cc file name"
else:
loop_writer = control_loop.ControlLoopWriter("Shooter", [shooter])
if argv[1][-3:] == '.cc':
loop_writer.Write(argv[2], argv[1])
else:
loop_writer.Write(argv[1], argv[2])
if __name__ == '__main__':
sys.exit(main(sys.argv))