blob: 27ecc160ba393266e6446ddec4c7da22b6f81729 [file] [log] [blame]
#!/usr/bin/python
import numpy
import sys
from matplotlib import pylab
import control_loop
import slycot
class Shooter(control_loop.ControlLoop):
def __init__(self):
super(Shooter, self).__init__("Shooter")
# Stall Torque in N m
self.stall_torque = 2.42211227883219
# Stall Current in Amps
self.stall_current = 133
# Free Speed in RPM
self.free_speed = 4650.0
# Free Current in Amps
self.free_current = 2.7
# 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
# 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 = 40.0 / 34.0
# Control loop time step
self.dt = 0.01
# State feedback matrices
self.A_continuous = numpy.matrix(
[[-self.Kt / self.Kv / (self.J * self.G * self.G * self.R)]])
self.B_continuous = numpy.matrix(
[[self.Kt / (self.J * self.G * self.R)]])
self.C = numpy.matrix([[1]])
self.D = numpy.matrix([[0]])
self.A, self.B = self.ContinuousToDiscrete(self.A_continuous, self.B_continuous,
self.dt)
self.InitializeState()
self.PlaceControllerPoles([.8])
# LQR stuff for optimization, if needed.
#print self.K
#self.R_LQR = numpy.matrix([[1.5]])
#self.P = slycot.sb02od(1, 1, self.A, self.B, self.C * self.C.T, self.R, 'D')[0]
#self.K = (numpy.linalg.inv(self.R_LQR + self.B.T * self.P * self.B)
# * self.B.T * self.P * self.A)
#print numpy.linalg.eig(self.A - self.B * self.K)
self.PlaceObserverPoles([0.45])
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 = 400
R = numpy.matrix([[velocity_goal]])
goal = False
for i in pylab.linspace(0,1.99,200):
# Iterate the position up.
R = numpy.matrix([[velocity_goal]])
U = numpy.clip(shooter.K * (R - shooter.X_hat) +
(numpy.identity(shooter.A.shape[0]) - shooter.A) * R / shooter.B,
shooter.U_min, shooter.U_max)
shooter.UpdateObserver(U)
shooter.Update(U)
close_loop_x.append(shooter.X[0, 0])
close_loop_U.append(U[0, 0])
if (abs(R[0, 0] - shooter.X[0, 0]) < R[0, 0]* 0.01 and (not goal)):
goal = True
print i
#pylab.plotfile("shooter.csv", (0,1))
pylab.plot(pylab.linspace(0,1.99,200), close_loop_U)
#pylab.plotfile("shooter.csv", (0,2))
pylab.plot(pylab.linspace(0,1.99,200), close_loop_x)
pylab.show()
# Simulate spin down.
spin_down_x = [];
for _ in xrange(150):
U = 0
shooter.UpdateObserver(U)
shooter.Update(U)
spin_down_x.append(shooter.X[0, 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))