frc971/control_loops/python/claw.py

#!/usr/bin/python

import control_loop
import controls
import polytope
import polydrivetrain
import numpy
import sys
import matplotlib
from matplotlib import pylab

class Claw(control_loop.ControlLoop):
  def __init__(self, name="Claw", mass=None):
    super(Claw, self).__init__(name)
    # Stall Torque in N m
    self.stall_torque = 0.476
    # Stall Current in Amps
    self.stall_current = 80.730
    # Free Speed in RPM
    self.free_speed = 13906.0
    # Free Current in Amps
    self.free_current = 5.820
    # Mass of the claw
    if mass is None:
      self.mass = 5.0
    else:
      self.mass = mass

    # Resistance of the motor
    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 = (56.0 / 12.0) * (54.0 / 14.0) * (64.0 / 14.0) * (72.0 / 18.0)
    # Claw length
    self.r = 18 * 0.0254

    self.J = self.r * self.mass

    # Control loop time step
    self.dt = 0.005

    # State is [position, velocity]
    # Input is [Voltage]

    C1 = self.G * self.G * self.Kt / (self.R  * self.J * self.Kv)
    C2 = self.Kt * self.G / (self.J * self.R)

    self.A_continuous = numpy.matrix(
        [[0, 1],
         [0, -C1]])

    # Start with the unmodified input
    self.B_continuous = numpy.matrix(
        [[0],
         [C2]])

    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)

    print self.A

    controlability = controls.ctrb(self.A, self.B);
    print "Rank of augmented controlability matrix.", numpy.linalg.matrix_rank(controlability)

    q_pos = 0.03
    q_vel = 0.5
    self.Q = numpy.matrix([[(1.0 / (q_pos ** 2.0)), 0.0],
                           [0.0, (1.0 / (q_vel ** 2.0))]])

    self.R = numpy.matrix([[(1.0 / (12.0 ** 2.0))]])
    self.K = controls.dlqr(self.A, self.B, self.Q, self.R)
    print self.K

    print numpy.linalg.eig(self.A - self.B * self.K)[0]

    self.rpl = 0.20
    self.ipl = 0.05
    self.PlaceObserverPoles([self.rpl + 1j * self.ipl,
                             self.rpl - 1j * self.ipl])

    # The box formed by U_min and U_max must encompass all possible values,
    # or else Austin's code gets angry.
    self.U_max = numpy.matrix([[12.0]])
    self.U_min = numpy.matrix([[-12.0]])

    self.InitializeState()


def run_test(claw, initial_X, goal, max_separation_error=0.01,
             show_graph=True, iterations=200, controller_claw=None,
             observer_claw=None):
  """Runs the claw plant with an initial condition and goal.

    The tests themselves are not terribly sophisticated; I just test for
    whether the goal has been reached and whether the separation goes
    outside of the initial and goal values by more than max_separation_error.
    Prints out something for a failure of either condition and returns
    False if tests fail.
    Args:
      claw: claw object to use.
      initial_X: starting state.
      goal: goal state.
      show_graph: Whether or not to display a graph showing the changing
           states and voltages.
      iterations: Number of timesteps to run the model for.
      controller_claw: claw object to get K from, or None if we should
          use claw.
      observer_claw: claw object to use for the observer, or None if we should
          use the actual state.
  """

  claw.X = initial_X

  if controller_claw is None:
    controller_claw = claw

  if observer_claw is not None:
    observer_claw.X_hat = initial_X + 0.01
    observer_claw.X_hat = initial_X

  # Various lists for graphing things.
  t = []
  x = []
  v = []
  x_hat = []
  u = []

  sep_plot_gain = 100.0

  for i in xrange(iterations):
    X_hat = claw.X
    if observer_claw is not None:
      X_hat = observer_claw.X_hat
      x_hat.append(observer_claw.X_hat[0, 0])
    U = controller_claw.K * (goal - X_hat)
    U[0, 0] = numpy.clip(U[0, 0], -12, 12)
    x.append(claw.X[0, 0])
    v.append(claw.X[1, 0])
    if observer_claw is not None:
      observer_claw.PredictObserver(U)
    claw.Update(U)
    if observer_claw is not None:
      observer_claw.Y = claw.Y
      observer_claw.CorrectObserver(U)

    t.append(i * claw.dt)
    u.append(U[0, 0])

  if show_graph:
    pylab.subplot(2, 1, 1)
    pylab.plot(t, x, label='x')
    if observer_claw is not None:
      pylab.plot(t, x_hat, label='x_hat')
    pylab.legend()

    pylab.subplot(2, 1, 2)
    pylab.plot(t, u, label='u')
    pylab.legend()
    pylab.show()


def main(argv):
  loaded_mass = 5
  #loaded_mass = 0
  claw = Claw(mass=5 + loaded_mass)
  claw_controller = Claw(mass=5 + 5)
  observer_claw = Claw(mass=5 + 5)
  #observer_claw = None

  # Test moving the claw with constant separation.
  initial_X = numpy.matrix([[0.0], [0.0]])
  R = numpy.matrix([[1.0], [0.0]])
  run_test(claw, initial_X, R, controller_claw=claw_controller,
           observer_claw=observer_claw)

  # Write the generated constants out to a file.
  if len(argv) != 3:
    print "Expected .h file name and .cc file name for the claw."
  else:
    claw = Claw("Claw")
    loop_writer = control_loop.ControlLoopWriter("Claw", [claw])
    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))