diff --git a/y2017/control_loops/python/BUILD b/y2017/control_loops/python/BUILD
index 56a9f3d..cb599a3 100644
--- a/y2017/control_loops/python/BUILD
+++ b/y2017/control_loops/python/BUILD
@@ -37,3 +37,66 @@
     '//frc971/control_loops/python:controls',
   ],
 )
+
+py_binary(
+  name = 'shooter',
+  srcs = [
+    'shooter.py',
+  ],
+  deps = [
+    '//external:python-gflags',
+    '//external:python-glog',
+    '//frc971/control_loops/python:controls',
+  ]
+)
+
+py_binary(
+  name = 'indexer',
+  srcs = [
+    'indexer.py',
+  ],
+  deps = [
+    '//external:python-gflags',
+    '//external:python-glog',
+    '//frc971/control_loops/python:controls',
+  ]
+)
+
+py_binary(
+  name = 'intake',
+  srcs = [
+    'intake.py',
+  ],
+  deps = [
+    '//aos/common/util:py_trapezoid_profile',
+    '//external:python-gflags',
+    '//external:python-glog',
+    '//frc971/control_loops/python:controls',
+  ]
+)
+
+py_binary(
+  name = 'turret',
+  srcs = [
+    'turret.py',
+  ],
+  deps = [
+    '//aos/common/util:py_trapezoid_profile',
+    '//external:python-gflags',
+    '//external:python-glog',
+    '//frc971/control_loops/python:controls',
+  ]
+)
+
+py_binary(
+  name = 'hood',
+  srcs = [
+    'hood.py',
+  ],
+  deps = [
+    '//aos/common/util:py_trapezoid_profile',
+    '//external:python-gflags',
+    '//external:python-glog',
+    '//frc971/control_loops/python:controls',
+  ]
+)
diff --git a/y2017/control_loops/python/drivetrain.py b/y2017/control_loops/python/drivetrain.py
index a9e5101..17e0079 100755
--- a/y2017/control_loops/python/drivetrain.py
+++ b/y2017/control_loops/python/drivetrain.py
@@ -28,13 +28,13 @@
     # Free Current in Amps
     self.free_current = 4.7 * self.num_motors
     # Moment of inertia of the drivetrain in kg m^2
-    self.J = 1.5
+    self.J = 2.0
     # Mass of the robot, in kg.
-    self.m = 50
+    self.m = 24
     # Radius of the robot, in meters (requires tuning by hand)
-    self.rb = 0.6 / 2.0
+    self.rb = 0.59055 / 2.0
     # Radius of the wheels, in meters.
-    self.r = 0.041275
+    self.r = 0.08255 / 2.0
     # Resistance of the motor, divided by the number of motors.
     self.resistance = 12.0 / self.stall_current
     # Motor velocity constant
diff --git a/y2017/control_loops/python/hood.py b/y2017/control_loops/python/hood.py
new file mode 100755
index 0000000..5be85cd
--- /dev/null
+++ b/y2017/control_loops/python/hood.py
@@ -0,0 +1,328 @@
+#!/usr/bin/python
+
+from aos.common.util.trapezoid_profile import TrapezoidProfile
+from frc971.control_loops.python import control_loop
+from frc971.control_loops.python import controls
+import numpy
+import sys
+import matplotlib
+from matplotlib import pylab
+import gflags
+import glog
+
+FLAGS = gflags.FLAGS
+
+try:
+  gflags.DEFINE_bool('plot', False, 'If true, plot the loop response.')
+except gflags.DuplicateFlagError:
+  pass
+
+class Hood(control_loop.ControlLoop):
+  def __init__(self, name='Hood'):
+    super(Hood, self).__init__(name)
+    # Stall Torque in N m
+    self.stall_torque = 0.43
+    # Stall Current in Amps
+    self.stall_current = 53.0
+    # Free Speed in RPM
+    self.free_speed = 13180.0
+    # Free Current in Amps
+    self.free_current = 1.8
+
+    # 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
+    # First axle gear ratio off the motor
+    self.G1 = (12.0 / 60.0)
+    # Second axle gear ratio off the motor
+    self.G2 = self.G1 * (14.0 / 36.0)
+    # Third axle gear ratio off the motor
+    self.G3 = self.G2 * (14.0 / 36.0)
+    # Gear ratio
+    self.G = (12.0 / 60.0) * (14.0 / 36.0) * (14.0 / 36.0) * (18.0 / 345.0)
+
+    # 36 tooth gear inertia in kg * m^2
+    self.big_gear_inertia = 0.5 * 0.039 * ((36.0 / 40.0 * 0.025) ** 2)
+
+    # Motor inertia in kg * m^2
+    self.motor_inertia = 0.000006
+    glog.debug(self.big_gear_inertia)
+
+    # Moment of inertia, measured in CAD.
+    # Extra mass to compensate for friction is added on.
+    self.J = 0.08 + \
+             self.big_gear_inertia * ((self.G1 / self.G) ** 2) + \
+             self.big_gear_inertia * ((self.G2 / self.G) ** 2) + \
+             self.motor_inertia * ((1.0 / self.G) ** 2.0)
+    glog.debug('J effective %f', self.J)
+
+    # Control loop time step
+    self.dt = 0.005
+
+    # State is [position, velocity]
+    # Input is [Voltage]
+
+    C1 = self.Kt / (self.R * self.J * self.Kv * self.G * self.G)
+    C2 = self.Kt / (self.J * self.R * self.G)
+
+    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)
+
+    controllability = controls.ctrb(self.A, self.B)
+
+    glog.debug('Free speed is %f',
+               -self.B_continuous[1, 0] / self.A_continuous[1, 1] * 12.0)
+    glog.debug(repr(self.A_continuous))
+
+    # Calculate the LQR controller gain
+    q_pos = 2.0
+    q_vel = 500.0
+    self.Q = numpy.matrix([[(1.0 / (q_pos ** 2.0)), 0.0],
+                           [0.0, (1.0 / (q_vel ** 2.0))]])
+
+    self.R = numpy.matrix([[(5.0 / (12.0 ** 2.0))]])
+    self.K = controls.dlqr(self.A, self.B, self.Q, self.R)
+
+    # Calculate the feed forwards gain.
+    q_pos_ff = 0.005
+    q_vel_ff = 1.0
+    self.Qff = numpy.matrix([[(1.0 / (q_pos_ff ** 2.0)), 0.0],
+                             [0.0, (1.0 / (q_vel_ff ** 2.0))]])
+
+    self.Kff = controls.TwoStateFeedForwards(self.B, self.Qff)
+
+    glog.debug('K %s', repr(self.K))
+    glog.debug('Poles are %s',
+              repr(numpy.linalg.eig(self.A - self.B * self.K)[0]))
+
+    q_pos = 0.10
+    q_vel = 1.65
+    self.Q = numpy.matrix([[(q_pos ** 2.0), 0.0],
+                           [0.0, (q_vel ** 2.0)]])
+
+    r_volts = 0.025
+    self.R = numpy.matrix([[(r_volts ** 2.0)]])
+
+    self.KalmanGain, self.Q_steady = controls.kalman(
+        A=self.A, B=self.B, C=self.C, Q=self.Q, R=self.R)
+
+    glog.debug('Kal %s', repr(self.KalmanGain))
+    self.L = self.A * self.KalmanGain
+    glog.debug('KalL is %s', repr(self.L))
+
+    # 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()
+
+class IntegralHood(Hood):
+  def __init__(self, name='IntegralHood'):
+    super(IntegralHood, self).__init__(name=name)
+
+    self.A_continuous_unaugmented = self.A_continuous
+    self.B_continuous_unaugmented = self.B_continuous
+
+    self.A_continuous = numpy.matrix(numpy.zeros((3, 3)))
+    self.A_continuous[0:2, 0:2] = self.A_continuous_unaugmented
+    self.A_continuous[0:2, 2] = self.B_continuous_unaugmented
+
+    self.B_continuous = numpy.matrix(numpy.zeros((3, 1)))
+    self.B_continuous[0:2, 0] = self.B_continuous_unaugmented
+
+    self.C_unaugmented = self.C
+    self.C = numpy.matrix(numpy.zeros((1, 3)))
+    self.C[0:1, 0:2] = self.C_unaugmented
+
+    self.A, self.B = self.ContinuousToDiscrete(
+        self.A_continuous, self.B_continuous, self.dt)
+
+    q_pos = 0.12
+    q_vel = 2.00
+    q_voltage = 3.0
+    self.Q = numpy.matrix([[(q_pos ** 2.0), 0.0, 0.0],
+                           [0.0, (q_vel ** 2.0), 0.0],
+                           [0.0, 0.0, (q_voltage ** 2.0)]])
+
+    r_pos = 0.05
+    self.R = numpy.matrix([[(r_pos ** 2.0)]])
+
+    self.KalmanGain, self.Q_steady = controls.kalman(
+        A=self.A, B=self.B, C=self.C, Q=self.Q, R=self.R)
+    self.L = self.A * self.KalmanGain
+
+    self.K_unaugmented = self.K
+    self.K = numpy.matrix(numpy.zeros((1, 3)))
+    self.K[0, 0:2] = self.K_unaugmented
+    self.K[0, 2] = 1
+
+    self.Kff = numpy.concatenate((self.Kff, numpy.matrix(numpy.zeros((1, 1)))), axis=1)
+
+    self.InitializeState()
+
+class ScenarioPlotter(object):
+  def __init__(self):
+    # Various lists for graphing things.
+    self.t = []
+    self.x = []
+    self.v = []
+    self.a = []
+    self.x_hat = []
+    self.u = []
+    self.offset = []
+
+  def run_test(self, hood, end_goal,
+             controller_hood,
+             observer_hood=None,
+             iterations=200):
+    """Runs the hood plant with an initial condition and goal.
+
+      Args:
+        hood: hood object to use.
+        end_goal: end_goal state.
+        controller_hood: Hood object to get K from, or None if we should
+            use hood.
+        observer_hood: Hood object to use for the observer, or None if we should
+            use the actual state.
+        iterations: Number of timesteps to run the model for.
+    """
+
+    if controller_hood is None:
+      controller_hood = hood
+
+    vbat = 12.0
+
+    if self.t:
+      initial_t = self.t[-1] + hood.dt
+    else:
+      initial_t = 0
+
+    goal = numpy.concatenate((hood.X, numpy.matrix(numpy.zeros((1, 1)))), axis=0)
+
+    profile = TrapezoidProfile(hood.dt)
+    profile.set_maximum_acceleration(10.0)
+    profile.set_maximum_velocity(1.0)
+    profile.SetGoal(goal[0, 0])
+
+    U_last = numpy.matrix(numpy.zeros((1, 1)))
+    for i in xrange(iterations):
+      observer_hood.Y = hood.Y
+      observer_hood.CorrectObserver(U_last)
+
+      self.offset.append(observer_hood.X_hat[2, 0])
+      self.x_hat.append(observer_hood.X_hat[0, 0])
+
+      next_goal = numpy.concatenate(
+          (profile.Update(end_goal[0, 0], end_goal[1, 0]),
+           numpy.matrix(numpy.zeros((1, 1)))),
+          axis=0)
+
+      ff_U = controller_hood.Kff * (next_goal - observer_hood.A * goal)
+
+      U_uncapped = controller_hood.K * (goal - observer_hood.X_hat) + ff_U
+      U = U_uncapped.copy()
+      U[0, 0] = numpy.clip(U[0, 0], -vbat, vbat)
+      self.x.append(hood.X[0, 0])
+
+      if self.v:
+        last_v = self.v[-1]
+      else:
+        last_v = 0
+
+      self.v.append(hood.X[1, 0])
+      self.a.append((self.v[-1] - last_v) / hood.dt)
+
+      offset = 0.0
+      if i > 100:
+        offset = 2.0
+      hood.Update(U + offset)
+
+      observer_hood.PredictObserver(U)
+
+      self.t.append(initial_t + i * hood.dt)
+      self.u.append(U[0, 0])
+
+      ff_U -= U_uncapped - U
+      goal = controller_hood.A * goal + controller_hood.B * ff_U
+
+      if U[0, 0] != U_uncapped[0, 0]:
+        profile.MoveCurrentState(
+            numpy.matrix([[goal[0, 0]], [goal[1, 0]]]))
+
+    glog.debug('Time: %f', self.t[-1])
+    glog.debug('goal_error %s', repr(end_goal - goal))
+    glog.debug('error %s', repr(observer_hood.X_hat - end_goal))
+
+  def Plot(self):
+    pylab.subplot(3, 1, 1)
+    pylab.plot(self.t, self.x, label='x')
+    pylab.plot(self.t, self.x_hat, label='x_hat')
+    pylab.legend()
+
+    pylab.subplot(3, 1, 2)
+    pylab.plot(self.t, self.u, label='u')
+    pylab.plot(self.t, self.offset, label='voltage_offset')
+    pylab.legend()
+
+    pylab.subplot(3, 1, 3)
+    pylab.plot(self.t, self.a, label='a')
+    pylab.legend()
+
+    pylab.show()
+
+
+def main(argv):
+
+  scenario_plotter = ScenarioPlotter()
+
+  hood = Hood()
+  hood_controller = IntegralHood()
+  observer_hood = IntegralHood()
+
+  # Test moving the hood with constant separation.
+  initial_X = numpy.matrix([[0.0], [0.0]])
+  R = numpy.matrix([[numpy.pi/2.0], [0.0], [0.0]])
+  scenario_plotter.run_test(hood, end_goal=R,
+                            controller_hood=hood_controller,
+                            observer_hood=observer_hood, iterations=200)
+
+  if FLAGS.plot:
+    scenario_plotter.Plot()
+
+  # Write the generated constants out to a file.
+  if len(argv) != 5:
+    glog.fatal('Expected .h file name and .cc file name for the hood and integral hood.')
+  else:
+    namespaces = ['y2017', 'control_loops', 'superstructure', 'hood']
+    hood = Hood('Hood')
+    loop_writer = control_loop.ControlLoopWriter('Hood', [hood],
+                                                 namespaces=namespaces)
+    loop_writer.Write(argv[1], argv[2])
+
+    integral_hood = IntegralHood('IntegralHood')
+    integral_loop_writer = control_loop.ControlLoopWriter('IntegralHood', [integral_hood],
+                                                          namespaces=namespaces)
+    integral_loop_writer.Write(argv[3], argv[4])
+
+
+if __name__ == '__main__':
+  argv = FLAGS(sys.argv)
+  glog.init()
+  sys.exit(main(argv))
diff --git a/y2017/control_loops/python/indexer.py b/y2017/control_loops/python/indexer.py
new file mode 100755
index 0000000..a5088cb
--- /dev/null
+++ b/y2017/control_loops/python/indexer.py
@@ -0,0 +1,298 @@
+#!/usr/bin/python
+
+from frc971.control_loops.python import control_loop
+from frc971.control_loops.python import controls
+import numpy
+import sys
+from matplotlib import pylab
+
+import gflags
+import glog
+
+FLAGS = gflags.FLAGS
+
+gflags.DEFINE_bool('plot', False, 'If true, plot the loop response.')
+
+class VelocityIndexer(control_loop.ControlLoop):
+  def __init__(self, name='VelocityIndexer'):
+    super(VelocityIndexer, self).__init__(name)
+    # Stall Torque in N m
+    self.stall_torque = 0.71
+    # Stall Current in Amps
+    self.stall_current = 134
+    # Free Speed in RPM
+    self.free_speed = 18730.0
+    # Free Current in Amps
+    self.free_current = 0.7
+    # Moment of inertia of the indexer halves in kg m^2
+    # This is measured as Iyy in CAD (the moment of inertia around the Y axis).
+    # Inner part of indexer -> Iyy = 59500 lb * mm * mm
+    # Inner spins with 12 / 48 * 18 / 48 * 24 / 36 * 16 / 72
+    # Outer part of indexer -> Iyy = 210000 lb * mm * mm
+    # 1 775 pro -> 12 / 48 * 18 / 48 * 30 / 422
+
+    self.J_inner = 0.0269
+    self.J_outer = 0.0952
+    # Gear ratios for the inner and outer parts.
+    self.G_inner = (12.0 / 48.0) * (18.0 / 48.0) * (24.0 / 36.0) * (16.0 / 72.0)
+    self.G_outer = (12.0 / 48.0) * (18.0 / 48.0) * (30.0 / 422.0)
+
+    # Motor inertia in kg * m^2
+    self.motor_inertia = 0.000006
+
+    # The output coordinate system is in radians for the inner part of the
+    # indexer.
+    # Compute the effective moment of inertia assuming all the mass is in that
+    # coordinate system.
+    self.J = (
+        self.J_inner * self.G_inner * self.G_inner +
+        self.J_outer * self.G_outer * self.G_outer) / (self.G_inner * self.G_inner) + \
+        self.motor_inertia * ((1.0 / self.G_inner) ** 2.0)
+    glog.debug('J is %f', self.J)
+    self.G = self.G_inner
+
+    # 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
+    # Control loop time step
+    self.dt = 0.005
+
+    # State feedback matrices
+    # [angular velocity]
+    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.PlaceControllerPoles([.82])
+    glog.debug(repr(self.K))
+
+    self.PlaceObserverPoles([0.3])
+
+    self.U_max = numpy.matrix([[12.0]])
+    self.U_min = numpy.matrix([[-12.0]])
+
+    qff_vel = 8.0
+    self.Qff = numpy.matrix([[1.0 / (qff_vel ** 2.0)]])
+
+    self.Kff = controls.TwoStateFeedForwards(self.B, self.Qff)
+    self.InitializeState()
+
+
+class Indexer(VelocityIndexer):
+  def __init__(self, name='Indexer'):
+    super(Indexer, self).__init__(name)
+
+    self.A_continuous_unaugmented = self.A_continuous
+    self.B_continuous_unaugmented = self.B_continuous
+
+    self.A_continuous = numpy.matrix(numpy.zeros((2, 2)))
+    self.A_continuous[1:2, 1:2] = self.A_continuous_unaugmented
+    self.A_continuous[0, 1] = 1
+
+    self.B_continuous = numpy.matrix(numpy.zeros((2, 1)))
+    self.B_continuous[1:2, 0] = self.B_continuous_unaugmented
+
+    # State feedback matrices
+    # [position, angular velocity]
+    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.rpl = .45
+    self.ipl = 0.07
+    self.PlaceObserverPoles([self.rpl + 1j * self.ipl,
+                             self.rpl - 1j * self.ipl])
+
+    self.K_unaugmented = self.K
+    self.K = numpy.matrix(numpy.zeros((1, 2)))
+    self.K[0, 1:2] = self.K_unaugmented
+    self.Kff_unaugmented = self.Kff
+    self.Kff = numpy.matrix(numpy.zeros((1, 2)))
+    self.Kff[0, 1:2] = self.Kff_unaugmented
+
+    self.InitializeState()
+
+
+class IntegralIndexer(Indexer):
+  def __init__(self, name="IntegralIndexer"):
+    super(IntegralIndexer, self).__init__(name=name)
+
+    self.A_continuous_unaugmented = self.A_continuous
+    self.B_continuous_unaugmented = self.B_continuous
+
+    self.A_continuous = numpy.matrix(numpy.zeros((3, 3)))
+    self.A_continuous[0:2, 0:2] = self.A_continuous_unaugmented
+    self.A_continuous[0:2, 2] = self.B_continuous_unaugmented
+
+    self.B_continuous = numpy.matrix(numpy.zeros((3, 1)))
+    self.B_continuous[0:2, 0] = self.B_continuous_unaugmented
+
+    self.C_unaugmented = self.C
+    self.C = numpy.matrix(numpy.zeros((1, 3)))
+    self.C[0:1, 0:2] = self.C_unaugmented
+
+    self.A, self.B = self.ContinuousToDiscrete(
+        self.A_continuous, self.B_continuous, self.dt)
+
+    q_pos = 2.0
+    q_vel = 0.001
+    q_voltage = 10.0
+    self.Q = numpy.matrix([[(q_pos ** 2.0), 0.0, 0.0],
+                           [0.0, (q_vel ** 2.0), 0.0],
+                           [0.0, 0.0, (q_voltage ** 2.0)]])
+
+    r_pos = 0.001
+    self.R = numpy.matrix([[(r_pos ** 2.0)]])
+
+    self.KalmanGain, self.Q_steady = controls.kalman(
+        A=self.A, B=self.B, C=self.C, Q=self.Q, R=self.R)
+    self.L = self.A * self.KalmanGain
+
+    self.K_unaugmented = self.K
+    self.K = numpy.matrix(numpy.zeros((1, 3)))
+    self.K[0, 0:2] = self.K_unaugmented
+    self.K[0, 2] = 1
+    self.Kff_unaugmented = self.Kff
+    self.Kff = numpy.matrix(numpy.zeros((1, 3)))
+    self.Kff[0, 0:2] = self.Kff_unaugmented
+
+    self.InitializeState()
+
+
+class ScenarioPlotter(object):
+  def __init__(self):
+    # Various lists for graphing things.
+    self.t = []
+    self.x = []
+    self.v = []
+    self.a = []
+    self.x_hat = []
+    self.u = []
+    self.offset = []
+
+  def run_test(self, indexer, goal, iterations=200, controller_indexer=None,
+             observer_indexer=None):
+    """Runs the indexer plant with an initial condition and goal.
+
+      Args:
+        indexer: Indexer object to use.
+        goal: goal state.
+        iterations: Number of timesteps to run the model for.
+        controller_indexer: Indexer object to get K from, or None if we should
+            use indexer.
+        observer_indexer: Indexer object to use for the observer, or None if we
+            should use the actual state.
+    """
+
+    if controller_indexer is None:
+      controller_indexer = indexer
+
+    vbat = 12.0
+
+    if self.t:
+      initial_t = self.t[-1] + indexer.dt
+    else:
+      initial_t = 0
+
+    for i in xrange(iterations):
+      X_hat = indexer.X
+
+      if observer_indexer is not None:
+        X_hat = observer_indexer.X_hat
+        self.x_hat.append(observer_indexer.X_hat[1, 0])
+
+      ff_U = controller_indexer.Kff * (goal - observer_indexer.A * goal)
+
+      U = controller_indexer.K * (goal - X_hat) + ff_U
+      U[0, 0] = numpy.clip(U[0, 0], -vbat, vbat)
+      self.x.append(indexer.X[0, 0])
+
+
+      if self.v:
+        last_v = self.v[-1]
+      else:
+        last_v = 0
+
+      self.v.append(indexer.X[1, 0])
+      self.a.append((self.v[-1] - last_v) / indexer.dt)
+
+      if observer_indexer is not None:
+        observer_indexer.Y = indexer.Y
+        observer_indexer.CorrectObserver(U)
+        self.offset.append(observer_indexer.X_hat[2, 0])
+
+      applied_U = U.copy()
+      if i > 30:
+        applied_U += 2
+      indexer.Update(applied_U)
+
+      if observer_indexer is not None:
+        observer_indexer.PredictObserver(U)
+
+      self.t.append(initial_t + i * indexer.dt)
+      self.u.append(U[0, 0])
+
+  def Plot(self):
+    pylab.subplot(3, 1, 1)
+    pylab.plot(self.t, self.v, label='x')
+    pylab.plot(self.t, self.x_hat, label='x_hat')
+    pylab.legend()
+
+    pylab.subplot(3, 1, 2)
+    pylab.plot(self.t, self.u, label='u')
+    pylab.plot(self.t, self.offset, label='voltage_offset')
+    pylab.legend()
+
+    pylab.subplot(3, 1, 3)
+    pylab.plot(self.t, self.a, label='a')
+    pylab.legend()
+
+    pylab.show()
+
+
+def main(argv):
+  scenario_plotter = ScenarioPlotter()
+
+  indexer = Indexer()
+  indexer_controller = IntegralIndexer()
+  observer_indexer = IntegralIndexer()
+
+  initial_X = numpy.matrix([[0.0], [0.0]])
+  R = numpy.matrix([[0.0], [20.0], [0.0]])
+  scenario_plotter.run_test(indexer, goal=R, controller_indexer=indexer_controller,
+                            observer_indexer=observer_indexer, iterations=200)
+
+  if FLAGS.plot:
+    scenario_plotter.Plot()
+
+  if len(argv) != 5:
+    glog.fatal('Expected .h file name and .cc file name')
+  else:
+    namespaces = ['y2017', 'control_loops', 'superstructure', 'indexer']
+    indexer = Indexer('Indexer')
+    loop_writer = control_loop.ControlLoopWriter('Indexer', [indexer],
+                                                 namespaces=namespaces)
+    loop_writer.Write(argv[1], argv[2])
+
+    integral_indexer = IntegralIndexer('IntegralIndexer')
+    integral_loop_writer = control_loop.ControlLoopWriter(
+        'IntegralIndexer', [integral_indexer], namespaces=namespaces)
+    integral_loop_writer.Write(argv[3], argv[4])
+
+
+if __name__ == '__main__':
+  argv = FLAGS(sys.argv)
+  glog.init()
+  sys.exit(main(argv))
diff --git a/y2017/control_loops/python/intake.py b/y2017/control_loops/python/intake.py
new file mode 100755
index 0000000..41cfc0d
--- /dev/null
+++ b/y2017/control_loops/python/intake.py
@@ -0,0 +1,321 @@
+#!/usr/bin/python
+
+from aos.common.util.trapezoid_profile import TrapezoidProfile
+from frc971.control_loops.python import control_loop
+from frc971.control_loops.python import controls
+import numpy
+import sys
+import matplotlib
+from matplotlib import pylab
+import gflags
+import glog
+
+FLAGS = gflags.FLAGS
+
+try:
+  gflags.DEFINE_bool('plot', False, 'If true, plot the loop response.')
+except gflags.DuplicateFlagError:
+  pass
+
+class Intake(control_loop.ControlLoop):
+  def __init__(self, name='Intake'):
+    super(Intake, self).__init__(name)
+    # Stall Torque in N m
+    self.stall_torque = 0.71
+    # Stall Current in Amps
+    self.stall_current = 134.0
+    # Free Speed in RPM
+    self.free_speed = 18730.0
+    # Free Current in Amps
+    self.free_current = 0.7
+
+    # 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
+
+    # (1 / 35.0) * (20.0 / 40.0) -> 16 tooth sprocket on #25 chain
+    # Gear ratio
+    self.G = (1.0 / 35.0) * (20.0 / 40.0)
+    self.r = 16.0 * 0.25 / (2.0 * numpy.pi) * 0.0254
+
+    # Motor inertia in kg m^2
+    self.motor_inertia = 0.00001187
+
+    # 5.4 kg of moving mass for the intake
+    self.mass = 5.4 + self.motor_inertia / ((self.G * self.r) ** 2.0)
+
+    # Control loop time step
+    self.dt = 0.005
+
+    # State is [position, velocity]
+    # Input is [Voltage]
+
+    C1 = self.Kt / (self.G * self.G * self.r * self.r * self.R  * self.mass * self.Kv)
+    C2 = self.Kt / (self.G * self.r * self.R  * self.mass)
+
+    self.A_continuous = numpy.matrix(
+        [[0, 1],
+         [0, -C1]])
+
+    # Start with the unmodified input
+    self.B_continuous = numpy.matrix(
+        [[0],
+         [C2]])
+    glog.debug(repr(self.A_continuous))
+    glog.debug(repr(self.B_continuous))
+
+    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)
+
+    controllability = controls.ctrb(self.A, self.B)
+
+    glog.debug('Free speed is %f',
+               -self.B_continuous[1, 0] / self.A_continuous[1, 1] * 12.0)
+
+    q_pos = 0.015
+    q_vel = 0.3
+    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)
+
+    q_pos_ff = 0.005
+    q_vel_ff = 1.0
+    self.Qff = numpy.matrix([[(1.0 / (q_pos_ff ** 2.0)), 0.0],
+                             [0.0, (1.0 / (q_vel_ff ** 2.0))]])
+
+    self.Kff = controls.TwoStateFeedForwards(self.B, self.Qff)
+
+    glog.debug('K %s', repr(self.K))
+    glog.debug('Poles are %s',
+              repr(numpy.linalg.eig(self.A - self.B * self.K)[0]))
+
+    self.rpl = 0.30
+    self.ipl = 0.10
+    self.PlaceObserverPoles([self.rpl + 1j * self.ipl,
+                             self.rpl - 1j * self.ipl])
+
+    glog.debug('L is %s', repr(self.L))
+
+    q_pos = 0.10
+    q_vel = 1.65
+    self.Q = numpy.matrix([[(q_pos ** 2.0), 0.0],
+                           [0.0, (q_vel ** 2.0)]])
+
+    r_volts = 0.025
+    self.R = numpy.matrix([[(r_volts ** 2.0)]])
+
+    self.KalmanGain, self.Q_steady = controls.kalman(
+        A=self.A, B=self.B, C=self.C, Q=self.Q, R=self.R)
+
+    glog.debug('Kal %s', repr(self.KalmanGain))
+    self.L = self.A * self.KalmanGain
+    glog.debug('KalL is %s', repr(self.L))
+
+    # 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()
+
+class IntegralIntake(Intake):
+  def __init__(self, name='IntegralIntake'):
+    super(IntegralIntake, self).__init__(name=name)
+
+    self.A_continuous_unaugmented = self.A_continuous
+    self.B_continuous_unaugmented = self.B_continuous
+
+    self.A_continuous = numpy.matrix(numpy.zeros((3, 3)))
+    self.A_continuous[0:2, 0:2] = self.A_continuous_unaugmented
+    self.A_continuous[0:2, 2] = self.B_continuous_unaugmented
+
+    self.B_continuous = numpy.matrix(numpy.zeros((3, 1)))
+    self.B_continuous[0:2, 0] = self.B_continuous_unaugmented
+
+    self.C_unaugmented = self.C
+    self.C = numpy.matrix(numpy.zeros((1, 3)))
+    self.C[0:1, 0:2] = self.C_unaugmented
+
+    self.A, self.B = self.ContinuousToDiscrete(
+        self.A_continuous, self.B_continuous, self.dt)
+
+    q_pos = 0.12
+    q_vel = 2.00
+    q_voltage = 40.0
+    self.Q = numpy.matrix([[(q_pos ** 2.0), 0.0, 0.0],
+                           [0.0, (q_vel ** 2.0), 0.0],
+                           [0.0, 0.0, (q_voltage ** 2.0)]])
+
+    r_pos = 0.05
+    self.R = numpy.matrix([[(r_pos ** 2.0)]])
+
+    self.KalmanGain, self.Q_steady = controls.kalman(
+        A=self.A, B=self.B, C=self.C, Q=self.Q, R=self.R)
+    self.L = self.A * self.KalmanGain
+
+    self.K_unaugmented = self.K
+    self.K = numpy.matrix(numpy.zeros((1, 3)))
+    self.K[0, 0:2] = self.K_unaugmented
+    self.K[0, 2] = 1
+
+    self.Kff = numpy.concatenate((self.Kff, numpy.matrix(numpy.zeros((1, 1)))), axis=1)
+
+    self.InitializeState()
+
+class ScenarioPlotter(object):
+  def __init__(self):
+    # Various lists for graphing things.
+    self.t = []
+    self.x = []
+    self.v = []
+    self.a = []
+    self.x_hat = []
+    self.u = []
+    self.offset = []
+
+  def run_test(self, intake, end_goal,
+             controller_intake,
+             observer_intake=None,
+             iterations=200):
+    """Runs the intake plant with an initial condition and goal.
+
+      Args:
+        intake: intake object to use.
+        end_goal: end_goal state.
+        controller_intake: Intake object to get K from, or None if we should
+            use intake.
+        observer_intake: Intake object to use for the observer, or None if we should
+            use the actual state.
+        iterations: Number of timesteps to run the model for.
+    """
+
+    if controller_intake is None:
+      controller_intake = intake
+
+    vbat = 12.0
+
+    if self.t:
+      initial_t = self.t[-1] + intake.dt
+    else:
+      initial_t = 0
+
+    goal = numpy.concatenate((intake.X, numpy.matrix(numpy.zeros((1, 1)))), axis=0)
+
+    profile = TrapezoidProfile(intake.dt)
+    profile.set_maximum_acceleration(10.0)
+    profile.set_maximum_velocity(0.3)
+    profile.SetGoal(goal[0, 0])
+
+    U_last = numpy.matrix(numpy.zeros((1, 1)))
+    for i in xrange(iterations):
+      observer_intake.Y = intake.Y
+      observer_intake.CorrectObserver(U_last)
+
+      self.offset.append(observer_intake.X_hat[2, 0])
+      self.x_hat.append(observer_intake.X_hat[0, 0])
+
+      next_goal = numpy.concatenate(
+          (profile.Update(end_goal[0, 0], end_goal[1, 0]),
+           numpy.matrix(numpy.zeros((1, 1)))),
+          axis=0)
+
+      ff_U = controller_intake.Kff * (next_goal - observer_intake.A * goal)
+
+      U_uncapped = controller_intake.K * (goal - observer_intake.X_hat) + ff_U
+      U_uncapped = controller_intake.K * (end_goal - observer_intake.X_hat)
+      U = U_uncapped.copy()
+      U[0, 0] = numpy.clip(U[0, 0], -vbat, vbat)
+      self.x.append(intake.X[0, 0])
+
+      if self.v:
+        last_v = self.v[-1]
+      else:
+        last_v = 0
+
+      self.v.append(intake.X[1, 0])
+      self.a.append((self.v[-1] - last_v) / intake.dt)
+
+      offset = 0.0
+      if i > 100:
+        offset = 2.0
+      intake.Update(U + offset)
+
+      observer_intake.PredictObserver(U)
+
+      self.t.append(initial_t + i * intake.dt)
+      self.u.append(U[0, 0])
+
+      ff_U -= U_uncapped - U
+      goal = controller_intake.A * goal + controller_intake.B * ff_U
+
+      if U[0, 0] != U_uncapped[0, 0]:
+        profile.MoveCurrentState(
+            numpy.matrix([[goal[0, 0]], [goal[1, 0]]]))
+
+    glog.debug('Time: %f', self.t[-1])
+    glog.debug('goal_error %s', repr(end_goal - goal))
+    glog.debug('error %s', repr(observer_intake.X_hat - end_goal))
+
+  def Plot(self):
+    pylab.subplot(3, 1, 1)
+    pylab.plot(self.t, self.x, label='x')
+    pylab.plot(self.t, self.x_hat, label='x_hat')
+    pylab.legend()
+
+    pylab.subplot(3, 1, 2)
+    pylab.plot(self.t, self.u, label='u')
+    pylab.plot(self.t, self.offset, label='voltage_offset')
+    pylab.legend()
+
+    pylab.subplot(3, 1, 3)
+    pylab.plot(self.t, self.a, label='a')
+    pylab.legend()
+
+    pylab.show()
+
+
+def main(argv):
+  scenario_plotter = ScenarioPlotter()
+
+  intake = Intake()
+  intake_controller = IntegralIntake()
+  observer_intake = IntegralIntake()
+
+  # Test moving the intake with constant separation.
+  initial_X = numpy.matrix([[0.0], [0.0]])
+  R = numpy.matrix([[0.1], [0.0], [0.0]])
+  scenario_plotter.run_test(intake, end_goal=R,
+                            controller_intake=intake_controller,
+                            observer_intake=observer_intake, iterations=400)
+
+  if FLAGS.plot:
+    scenario_plotter.Plot()
+
+  # Write the generated constants out to a file.
+  if len(argv) != 5:
+    glog.fatal('Expected .h file name and .cc file name for the intake and integral intake.')
+  else:
+    namespaces = ['y2017', 'control_loops', 'superstructure', 'intake']
+    intake = Intake('Intake')
+    loop_writer = control_loop.ControlLoopWriter('Intake', [intake],
+                                                 namespaces=namespaces)
+    loop_writer.Write(argv[1], argv[2])
+
+    integral_intake = IntegralIntake('IntegralIntake')
+    integral_loop_writer = control_loop.ControlLoopWriter('IntegralIntake', [integral_intake],
+                                                          namespaces=namespaces)
+    integral_loop_writer.Write(argv[3], argv[4])
+
+if __name__ == '__main__':
+  argv = FLAGS(sys.argv)
+  glog.init()
+  sys.exit(main(argv))
diff --git a/y2017/control_loops/python/polydrivetrain.py b/y2017/control_loops/python/polydrivetrain.py
index 353618b..29cb209 100755
--- a/y2017/control_loops/python/polydrivetrain.py
+++ b/y2017/control_loops/python/polydrivetrain.py
@@ -129,7 +129,7 @@
     # FF * X = U (steady state)
     self.FF = self.B.I * (numpy.eye(2) - self.A)
 
-    self.PlaceControllerPoles([0.67, 0.67])
+    self.PlaceControllerPoles([0.85, 0.85])
     self.PlaceObserverPoles([0.02, 0.02])
 
     self.G_high = self._drivetrain.G_high
diff --git a/y2017/control_loops/python/shooter.py b/y2017/control_loops/python/shooter.py
new file mode 100755
index 0000000..d7c505b
--- /dev/null
+++ b/y2017/control_loops/python/shooter.py
@@ -0,0 +1,280 @@
+#!/usr/bin/python
+
+from frc971.control_loops.python import control_loop
+from frc971.control_loops.python import controls
+import numpy
+import sys
+from matplotlib import pylab
+
+import gflags
+import glog
+
+FLAGS = gflags.FLAGS
+
+gflags.DEFINE_bool('plot', False, 'If true, plot the loop response.')
+
+class VelocityShooter(control_loop.ControlLoop):
+  def __init__(self, name='VelocityShooter'):
+    super(VelocityShooter, self).__init__(name)
+    # Number of motors
+    self.num_motors = 2.0
+    # Stall Torque in N m
+    self.stall_torque = 0.71 * self.num_motors
+    # Stall Current in Amps
+    self.stall_current = 134.0 * self.num_motors
+    # Free Speed in RPM
+    self.free_speed = 18730.0
+    # Free Current in Amps
+    self.free_current = 0.7 * self.num_motors
+    # Moment of inertia of the shooter wheel in kg m^2
+    # 1400.6 grams/cm^2
+    # 1.407 *1e-4 kg m^2
+    self.J = 0.00080
+    # 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 = 12.0 / 36.0
+    # Control loop time step
+    self.dt = 0.005
+
+    # State feedback matrices
+    # [angular velocity]
+    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.PlaceControllerPoles([.90])
+
+    self.PlaceObserverPoles([0.3])
+
+    self.U_max = numpy.matrix([[12.0]])
+    self.U_min = numpy.matrix([[-12.0]])
+
+    qff_vel = 8.0
+    self.Qff = numpy.matrix([[1.0 / (qff_vel ** 2.0)]])
+
+    self.Kff = controls.TwoStateFeedForwards(self.B, self.Qff)
+    self.InitializeState()
+
+
+class Shooter(VelocityShooter):
+  def __init__(self, name='Shooter'):
+    super(Shooter, self).__init__(name)
+
+    self.A_continuous_unaugmented = self.A_continuous
+    self.B_continuous_unaugmented = self.B_continuous
+
+    self.A_continuous = numpy.matrix(numpy.zeros((2, 2)))
+    self.A_continuous[1:2, 1:2] = self.A_continuous_unaugmented
+    self.A_continuous[0, 1] = 1
+
+    self.B_continuous = numpy.matrix(numpy.zeros((2, 1)))
+    self.B_continuous[1:2, 0] = self.B_continuous_unaugmented
+
+    # State feedback matrices
+    # [position, angular velocity]
+    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.rpl = .45
+    self.ipl = 0.07
+    self.PlaceObserverPoles([self.rpl + 1j * self.ipl,
+                             self.rpl - 1j * self.ipl])
+
+    self.K_unaugmented = self.K
+    self.K = numpy.matrix(numpy.zeros((1, 2)))
+    self.K[0, 1:2] = self.K_unaugmented
+    self.Kff_unaugmented = self.Kff
+    self.Kff = numpy.matrix(numpy.zeros((1, 2)))
+    self.Kff[0, 1:2] = self.Kff_unaugmented
+
+    self.InitializeState()
+
+
+class IntegralShooter(Shooter):
+  def __init__(self, name='IntegralShooter'):
+    super(IntegralShooter, self).__init__(name=name)
+
+    self.A_continuous_unaugmented = self.A_continuous
+    self.B_continuous_unaugmented = self.B_continuous
+
+    self.A_continuous = numpy.matrix(numpy.zeros((3, 3)))
+    self.A_continuous[0:2, 0:2] = self.A_continuous_unaugmented
+    self.A_continuous[0:2, 2] = self.B_continuous_unaugmented
+
+    self.B_continuous = numpy.matrix(numpy.zeros((3, 1)))
+    self.B_continuous[0:2, 0] = self.B_continuous_unaugmented
+
+    self.C_unaugmented = self.C
+    self.C = numpy.matrix(numpy.zeros((1, 3)))
+    self.C[0:1, 0:2] = self.C_unaugmented
+
+    self.A, self.B = self.ContinuousToDiscrete(
+        self.A_continuous, self.B_continuous, self.dt)
+
+    q_pos = 2.0
+    q_vel = 0.001
+    q_voltage = 10.0
+    self.Q = numpy.matrix([[(q_pos ** 2.0), 0.0, 0.0],
+                           [0.0, (q_vel ** 2.0), 0.0],
+                           [0.0, 0.0, (q_voltage ** 2.0)]])
+
+    r_pos = 0.001
+    self.R = numpy.matrix([[(r_pos ** 2.0)]])
+
+    self.KalmanGain, self.Q_steady = controls.kalman(
+        A=self.A, B=self.B, C=self.C, Q=self.Q, R=self.R)
+    self.L = self.A * self.KalmanGain
+
+    self.K_unaugmented = self.K
+    self.K = numpy.matrix(numpy.zeros((1, 3)))
+    self.K[0, 0:2] = self.K_unaugmented
+    self.K[0, 2] = 1
+    self.Kff_unaugmented = self.Kff
+    self.Kff = numpy.matrix(numpy.zeros((1, 3)))
+    self.Kff[0, 0:2] = self.Kff_unaugmented
+
+    self.InitializeState()
+
+
+class ScenarioPlotter(object):
+  def __init__(self):
+    # Various lists for graphing things.
+    self.t = []
+    self.x = []
+    self.v = []
+    self.a = []
+    self.x_hat = []
+    self.u = []
+    self.offset = []
+
+  def run_test(self, shooter, goal, iterations=200, controller_shooter=None,
+             observer_shooter=None):
+    """Runs the shooter plant with an initial condition and goal.
+
+      Args:
+        shooter: Shooter object to use.
+        goal: goal state.
+        iterations: Number of timesteps to run the model for.
+        controller_shooter: Shooter object to get K from, or None if we should
+            use shooter.
+        observer_shooter: Shooter object to use for the observer, or None if we
+            should use the actual state.
+    """
+
+    if controller_shooter is None:
+      controller_shooter = shooter
+
+    vbat = 12.0
+
+    if self.t:
+      initial_t = self.t[-1] + shooter.dt
+    else:
+      initial_t = 0
+
+    for i in xrange(iterations):
+      X_hat = shooter.X
+
+      if observer_shooter is not None:
+        X_hat = observer_shooter.X_hat
+        self.x_hat.append(observer_shooter.X_hat[1, 0])
+
+      ff_U = controller_shooter.Kff * (goal - observer_shooter.A * goal)
+
+      U = controller_shooter.K * (goal - X_hat) + ff_U
+      U[0, 0] = numpy.clip(U[0, 0], -vbat, vbat)
+      self.x.append(shooter.X[0, 0])
+
+
+      if self.v:
+        last_v = self.v[-1]
+      else:
+        last_v = 0
+
+      self.v.append(shooter.X[1, 0])
+      self.a.append((self.v[-1] - last_v) / shooter.dt)
+
+      if observer_shooter is not None:
+        observer_shooter.Y = shooter.Y
+        observer_shooter.CorrectObserver(U)
+        self.offset.append(observer_shooter.X_hat[2, 0])
+
+      applied_U = U.copy()
+      if i > 30:
+        applied_U += 2
+      shooter.Update(applied_U)
+
+      if observer_shooter is not None:
+        observer_shooter.PredictObserver(U)
+
+      self.t.append(initial_t + i * shooter.dt)
+      self.u.append(U[0, 0])
+
+      glog.debug('Time: %f', self.t[-1])
+
+  def Plot(self):
+    pylab.subplot(3, 1, 1)
+    pylab.plot(self.t, self.v, label='x')
+    pylab.plot(self.t, self.x_hat, label='x_hat')
+    pylab.legend()
+
+    pylab.subplot(3, 1, 2)
+    pylab.plot(self.t, self.u, label='u')
+    pylab.plot(self.t, self.offset, label='voltage_offset')
+    pylab.legend()
+
+    pylab.subplot(3, 1, 3)
+    pylab.plot(self.t, self.a, label='a')
+    pylab.legend()
+
+    pylab.show()
+
+
+def main(argv):
+  scenario_plotter = ScenarioPlotter()
+
+  shooter = Shooter()
+  shooter_controller = IntegralShooter()
+  observer_shooter = IntegralShooter()
+
+  initial_X = numpy.matrix([[0.0], [0.0]])
+  R = numpy.matrix([[0.0], [100.0], [0.0]])
+  scenario_plotter.run_test(shooter, goal=R, controller_shooter=shooter_controller,
+                            observer_shooter=observer_shooter, iterations=200)
+
+  if FLAGS.plot:
+    scenario_plotter.Plot()
+
+  if len(argv) != 5:
+    glog.fatal('Expected .h file name and .cc file name')
+  else:
+    namespaces = ['y2017', 'control_loops', 'superstructure', 'shooter']
+    shooter = Shooter('Shooter')
+    loop_writer = control_loop.ControlLoopWriter('Shooter', [shooter],
+                                                 namespaces=namespaces)
+    loop_writer.Write(argv[1], argv[2])
+
+    integral_shooter = IntegralShooter('IntegralShooter')
+    integral_loop_writer = control_loop.ControlLoopWriter(
+        'IntegralShooter', [integral_shooter], namespaces=namespaces)
+    integral_loop_writer.Write(argv[3], argv[4])
+
+
+if __name__ == '__main__':
+  argv = FLAGS(sys.argv)
+  glog.init()
+  sys.exit(main(argv))
diff --git a/y2017/control_loops/python/turret.py b/y2017/control_loops/python/turret.py
new file mode 100755
index 0000000..454be9e
--- /dev/null
+++ b/y2017/control_loops/python/turret.py
@@ -0,0 +1,314 @@
+#!/usr/bin/python
+
+from aos.common.util.trapezoid_profile import TrapezoidProfile
+from frc971.control_loops.python import control_loop
+from frc971.control_loops.python import controls
+import numpy
+import sys
+import matplotlib
+from matplotlib import pylab
+import gflags
+import glog
+
+FLAGS = gflags.FLAGS
+
+try:
+  gflags.DEFINE_bool('plot', False, 'If true, plot the loop response.')
+except gflags.DuplicateFlagError:
+  pass
+
+class Turret(control_loop.ControlLoop):
+  def __init__(self, name='Turret'):
+    super(Turret, self).__init__(name)
+    # Stall Torque in N m
+    self.stall_torque = 0.43
+    # Stall Current in Amps
+    self.stall_current = 53
+    # Free Speed in RPM
+    self.free_speed = 13180
+    # Free Current in Amps
+    self.free_current = 1.8
+
+    # 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 = (1.0 / 7.0) * (1.0 / 5.0) * (16.0 / 92.0)
+
+    # Motor inertia in kg * m^2
+    self.motor_inertia = 0.000006
+
+    # Moment of inertia, measured in CAD.
+    # Extra mass to compensate for friction is added on.
+    self.J = 0.05 + self.motor_inertia * ((1.0 / self.G) ** 2.0)
+
+    # Control loop time step
+    self.dt = 0.005
+
+    # State is [position, velocity]
+    # Input is [Voltage]
+
+    C1 = self.Kt / (self.R  * self.J * self.Kv * self.G * self.G)
+    C2 = self.Kt / (self.J * self.R * self.G)
+
+    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)
+
+    controllability = controls.ctrb(self.A, self.B)
+
+    glog.debug('Free speed is %f',
+               -self.B_continuous[1, 0] / self.A_continuous[1, 1] * 12.0)
+
+    # Calculate the LQR controller gain
+    q_pos = 0.20
+    q_vel = 5.0
+    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)
+
+    # Calculate the feed forwards gain.
+    q_pos_ff = 0.005
+    q_vel_ff = 1.0
+    self.Qff = numpy.matrix([[(1.0 / (q_pos_ff ** 2.0)), 0.0],
+                             [0.0, (1.0 / (q_vel_ff ** 2.0))]])
+
+    self.Kff = controls.TwoStateFeedForwards(self.B, self.Qff)
+
+    glog.debug('K %s', repr(self.K))
+    glog.debug('Poles are %s',
+              repr(numpy.linalg.eig(self.A - self.B * self.K)[0]))
+
+    q_pos = 0.10
+    q_vel = 1.65
+    self.Q = numpy.matrix([[(q_pos ** 2.0), 0.0],
+                           [0.0, (q_vel ** 2.0)]])
+
+    r_volts = 0.025
+    self.R = numpy.matrix([[(r_volts ** 2.0)]])
+
+    self.KalmanGain, self.Q_steady = controls.kalman(
+        A=self.A, B=self.B, C=self.C, Q=self.Q, R=self.R)
+
+    glog.debug('Kal %s', repr(self.KalmanGain))
+    self.L = self.A * self.KalmanGain
+    glog.debug('KalL is %s', repr(self.L))
+
+    # 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()
+
+class IntegralTurret(Turret):
+  def __init__(self, name='IntegralTurret'):
+    super(IntegralTurret, self).__init__(name=name)
+
+    self.A_continuous_unaugmented = self.A_continuous
+    self.B_continuous_unaugmented = self.B_continuous
+
+    self.A_continuous = numpy.matrix(numpy.zeros((3, 3)))
+    self.A_continuous[0:2, 0:2] = self.A_continuous_unaugmented
+    self.A_continuous[0:2, 2] = self.B_continuous_unaugmented
+
+    self.B_continuous = numpy.matrix(numpy.zeros((3, 1)))
+    self.B_continuous[0:2, 0] = self.B_continuous_unaugmented
+
+    self.C_unaugmented = self.C
+    self.C = numpy.matrix(numpy.zeros((1, 3)))
+    self.C[0:1, 0:2] = self.C_unaugmented
+
+    self.A, self.B = self.ContinuousToDiscrete(
+        self.A_continuous, self.B_continuous, self.dt)
+
+    q_pos = 0.12
+    q_vel = 2.00
+    q_voltage = 3.0
+    self.Q = numpy.matrix([[(q_pos ** 2.0), 0.0, 0.0],
+                           [0.0, (q_vel ** 2.0), 0.0],
+                           [0.0, 0.0, (q_voltage ** 2.0)]])
+
+    r_pos = 0.05
+    self.R = numpy.matrix([[(r_pos ** 2.0)]])
+
+    self.KalmanGain, self.Q_steady = controls.kalman(
+        A=self.A, B=self.B, C=self.C, Q=self.Q, R=self.R)
+    self.L = self.A * self.KalmanGain
+
+    self.K_unaugmented = self.K
+    self.K = numpy.matrix(numpy.zeros((1, 3)))
+    self.K[0, 0:2] = self.K_unaugmented
+    self.K[0, 2] = 1
+
+    self.Kff = numpy.concatenate((self.Kff, numpy.matrix(numpy.zeros((1, 1)))), axis=1)
+
+    self.InitializeState()
+
+class ScenarioPlotter(object):
+  def __init__(self):
+    # Various lists for graphing things.
+    self.t = []
+    self.x = []
+    self.v = []
+    self.a = []
+    self.x_hat = []
+    self.u = []
+    self.offset = []
+
+  def run_test(self, turret, end_goal,
+             controller_turret,
+             observer_turret=None,
+             iterations=200):
+    """Runs the turret plant with an initial condition and goal.
+
+      Args:
+        turret: turret object to use.
+        end_goal: end_goal state.
+        controller_turret: Turret object to get K from, or None if we should
+            use turret.
+        observer_turret: Turret object to use for the observer, or None if we should
+            use the actual state.
+        iterations: Number of timesteps to run the model for.
+    """
+
+    if controller_turret is None:
+      controller_turret = turret
+
+    vbat = 12.0
+
+    if self.t:
+      initial_t = self.t[-1] + turret.dt
+    else:
+      initial_t = 0
+
+    goal = numpy.concatenate((turret.X, numpy.matrix(numpy.zeros((1, 1)))), axis=0)
+
+    profile = TrapezoidProfile(turret.dt)
+    profile.set_maximum_acceleration(100.0)
+    profile.set_maximum_velocity(7.0)
+    profile.SetGoal(goal[0, 0])
+
+    U_last = numpy.matrix(numpy.zeros((1, 1)))
+    for i in xrange(iterations):
+      observer_turret.Y = turret.Y
+      observer_turret.CorrectObserver(U_last)
+
+      self.offset.append(observer_turret.X_hat[2, 0])
+      self.x_hat.append(observer_turret.X_hat[0, 0])
+
+      next_goal = numpy.concatenate(
+          (profile.Update(end_goal[0, 0], end_goal[1, 0]),
+           numpy.matrix(numpy.zeros((1, 1)))),
+          axis=0)
+
+      ff_U = controller_turret.Kff * (next_goal - observer_turret.A * goal)
+
+      U_uncapped = controller_turret.K * (goal - observer_turret.X_hat) + ff_U
+      U_uncapped = controller_turret.K * (end_goal - observer_turret.X_hat)
+      U = U_uncapped.copy()
+      U[0, 0] = numpy.clip(U[0, 0], -vbat, vbat)
+      self.x.append(turret.X[0, 0])
+
+      if self.v:
+        last_v = self.v[-1]
+      else:
+        last_v = 0
+
+      self.v.append(turret.X[1, 0])
+      self.a.append((self.v[-1] - last_v) / turret.dt)
+
+      offset = 0.0
+      if i > 100:
+        offset = 2.0
+      turret.Update(U + offset)
+
+      observer_turret.PredictObserver(U)
+
+      self.t.append(initial_t + i * turret.dt)
+      self.u.append(U[0, 0])
+
+      ff_U -= U_uncapped - U
+      goal = controller_turret.A * goal + controller_turret.B * ff_U
+
+      if U[0, 0] != U_uncapped[0, 0]:
+        profile.MoveCurrentState(
+            numpy.matrix([[goal[0, 0]], [goal[1, 0]]]))
+
+    glog.debug('Time: %f', self.t[-1])
+    glog.debug('goal_error %s', repr(end_goal - goal))
+    glog.debug('error %s', repr(observer_turret.X_hat - end_goal))
+
+  def Plot(self):
+    pylab.subplot(3, 1, 1)
+    pylab.plot(self.t, self.x, label='x')
+    pylab.plot(self.t, self.x_hat, label='x_hat')
+    pylab.legend()
+
+    pylab.subplot(3, 1, 2)
+    pylab.plot(self.t, self.u, label='u')
+    pylab.plot(self.t, self.offset, label='voltage_offset')
+    pylab.legend()
+
+    pylab.subplot(3, 1, 3)
+    pylab.plot(self.t, self.a, label='a')
+    pylab.legend()
+
+    pylab.show()
+
+
+def main(argv):
+  argv = FLAGS(argv)
+  glog.init()
+
+  scenario_plotter = ScenarioPlotter()
+
+  turret = Turret()
+  turret_controller = IntegralTurret()
+  observer_turret = IntegralTurret()
+
+  # Test moving the turret with constant separation.
+  initial_X = numpy.matrix([[0.0], [0.0]])
+  R = numpy.matrix([[numpy.pi/2.0], [0.0], [0.0]])
+  scenario_plotter.run_test(turret, end_goal=R,
+                            controller_turret=turret_controller,
+                            observer_turret=observer_turret, iterations=200)
+
+  if FLAGS.plot:
+    scenario_plotter.Plot()
+
+  # Write the generated constants out to a file.
+  if len(argv) != 5:
+    glog.fatal('Expected .h file name and .cc file name for the turret and integral turret.')
+  else:
+    namespaces = ['y2017', 'control_loops', 'superstructure', 'turret']
+    turret = Turret('Turret')
+    loop_writer = control_loop.ControlLoopWriter('Turret', [turret],
+                                                 namespaces=namespaces)
+    loop_writer.Write(argv[1], argv[2])
+
+    integral_turret = IntegralTurret('IntegralTurret')
+    integral_loop_writer = control_loop.ControlLoopWriter(
+        'IntegralTurret', [integral_turret],
+        namespaces=namespaces)
+    integral_loop_writer.Write(argv[3], argv[4])
+
+if __name__ == '__main__':
+  sys.exit(main(sys.argv))