Reformat python and c++ files
Change-Id: I7d7d99a2094c2a9181ed882735b55159c14db3b0
diff --git a/y2017/control_loops/python/column.py b/y2017/control_loops/python/column.py
index 1f8bd76..70cd649 100755
--- a/y2017/control_loops/python/column.py
+++ b/y2017/control_loops/python/column.py
@@ -14,383 +14,413 @@
FLAGS = gflags.FLAGS
try:
- gflags.DEFINE_bool('plot', False, 'If true, plot the loop response.')
+ gflags.DEFINE_bool('plot', False, 'If true, plot the loop response.')
except gflags.DuplicateFlagError:
- pass
+ pass
class ColumnController(control_loop.ControlLoop):
- def __init__(self, name='Column'):
- super(ColumnController, self).__init__(name)
- self.turret = turret.Turret(name + 'Turret')
- self.indexer = indexer.Indexer(name + 'Indexer')
- # Control loop time step
- self.dt = 0.005
+ def __init__(self, name='Column'):
+ super(ColumnController, self).__init__(name)
+ self.turret = turret.Turret(name + 'Turret')
+ self.indexer = indexer.Indexer(name + 'Indexer')
- # State is [position_indexer,
- # velocity_indexer,
- # position_shooter,
- # velocity_shooter]
- # Input is [volts_indexer, volts_shooter]
- self.A_continuous = numpy.matrix(numpy.zeros((3, 3)))
- self.B_continuous = numpy.matrix(numpy.zeros((3, 2)))
+ # Control loop time step
+ self.dt = 0.005
- self.A_continuous[0, 0] = -(self.indexer.Kt / self.indexer.Kv / (self.indexer.J * self.indexer.resistance * self.indexer.G * self.indexer.G) +
- self.turret.Kt / self.turret.Kv / (self.indexer.J * self.turret.resistance * self.turret.G * self.turret.G))
- self.A_continuous[0, 2] = self.turret.Kt / self.turret.Kv / (self.indexer.J * self.turret.resistance * self.turret.G * self.turret.G)
- self.B_continuous[0, 0] = self.indexer.Kt / (self.indexer.J * self.indexer.resistance * self.indexer.G)
- self.B_continuous[0, 1] = -self.turret.Kt / (self.indexer.J * self.turret.resistance * self.turret.G)
+ # State is [position_indexer,
+ # velocity_indexer,
+ # position_shooter,
+ # velocity_shooter]
+ # Input is [volts_indexer, volts_shooter]
+ self.A_continuous = numpy.matrix(numpy.zeros((3, 3)))
+ self.B_continuous = numpy.matrix(numpy.zeros((3, 2)))
- self.A_continuous[1, 2] = 1
+ self.A_continuous[0, 0] = -(
+ self.indexer.Kt / self.indexer.Kv /
+ (self.indexer.J * self.indexer.resistance * self.indexer.G *
+ self.indexer.G) + self.turret.Kt / self.turret.Kv /
+ (self.indexer.J * self.turret.resistance * self.turret.G *
+ self.turret.G))
+ self.A_continuous[0, 2] = self.turret.Kt / self.turret.Kv / (
+ self.indexer.J * self.turret.resistance * self.turret.G *
+ self.turret.G)
+ self.B_continuous[0, 0] = self.indexer.Kt / (
+ self.indexer.J * self.indexer.resistance * self.indexer.G)
+ self.B_continuous[0, 1] = -self.turret.Kt / (
+ self.indexer.J * self.turret.resistance * self.turret.G)
- self.A_continuous[2, 0] = self.turret.Kt / self.turret.Kv / (self.turret.J * self.turret.resistance * self.turret.G * self.turret.G)
- self.A_continuous[2, 2] = -self.turret.Kt / self.turret.Kv / (self.turret.J * self.turret.resistance * self.turret.G * self.turret.G)
+ self.A_continuous[1, 2] = 1
- self.B_continuous[2, 1] = self.turret.Kt / (self.turret.J * self.turret.resistance * self.turret.G)
+ self.A_continuous[2, 0] = self.turret.Kt / self.turret.Kv / (
+ self.turret.J * self.turret.resistance * self.turret.G *
+ self.turret.G)
+ self.A_continuous[2, 2] = -self.turret.Kt / self.turret.Kv / (
+ self.turret.J * self.turret.resistance * self.turret.G *
+ self.turret.G)
- self.C = numpy.matrix([[1, 0, 0], [0, 1, 0]])
- self.D = numpy.matrix([[0, 0], [0, 0]])
+ self.B_continuous[2, 1] = self.turret.Kt / (
+ self.turret.J * self.turret.resistance * self.turret.G)
- self.A, self.B = self.ContinuousToDiscrete(
- self.A_continuous, self.B_continuous, self.dt)
+ self.C = numpy.matrix([[1, 0, 0], [0, 1, 0]])
+ self.D = numpy.matrix([[0, 0], [0, 0]])
- q_indexer_vel = 13.0
- q_pos = 0.05
- q_vel = 0.8
- self.Q = numpy.matrix([[(1.0 / (q_indexer_vel ** 2.0)), 0.0, 0.0],
- [0.0, (1.0 / (q_pos ** 2.0)), 0.0],
- [0.0, 0.0, (1.0 / (q_vel ** 2.0))]])
+ self.A, self.B = self.ContinuousToDiscrete(self.A_continuous,
+ self.B_continuous, self.dt)
- self.R = numpy.matrix([[(1.0 / (12.0 ** 2.0)), 0.0],
- [0.0, (1.0 / (12.0 ** 2.0))]])
- self.K = controls.dlqr(self.A, self.B, self.Q, self.R)
+ q_indexer_vel = 13.0
+ q_pos = 0.05
+ q_vel = 0.8
+ self.Q = numpy.matrix([[(1.0 / (q_indexer_vel**2.0)), 0.0, 0.0],
+ [0.0, (1.0 / (q_pos**2.0)), 0.0],
+ [0.0, 0.0, (1.0 / (q_vel**2.0))]])
- glog.debug('Controller poles are ' + repr(numpy.linalg.eig(self.A - self.B * self.K)[0]))
+ self.R = numpy.matrix([[(1.0 / (12.0**2.0)), 0.0],
+ [0.0, (1.0 / (12.0**2.0))]])
+ self.K = controls.dlqr(self.A, self.B, self.Q, self.R)
- q_vel_indexer_ff = 0.000005
- q_pos_ff = 0.0000005
- q_vel_ff = 0.00008
- self.Qff = numpy.matrix([[(1.0 / (q_vel_indexer_ff ** 2.0)), 0.0, 0.0],
- [0.0, (1.0 / (q_pos_ff ** 2.0)), 0.0],
- [0.0, 0.0, (1.0 / (q_vel_ff ** 2.0))]])
+ glog.debug('Controller poles are ' +
+ repr(numpy.linalg.eig(self.A - self.B * self.K)[0]))
- self.Kff = controls.TwoStateFeedForwards(self.B, self.Qff)
+ q_vel_indexer_ff = 0.000005
+ q_pos_ff = 0.0000005
+ q_vel_ff = 0.00008
+ self.Qff = numpy.matrix([[(1.0 / (q_vel_indexer_ff**2.0)), 0.0, 0.0],
+ [0.0, (1.0 / (q_pos_ff**2.0)), 0.0],
+ [0.0, 0.0, (1.0 / (q_vel_ff**2.0))]])
- self.U_max = numpy.matrix([[12.0], [12.0]])
- self.U_min = numpy.matrix([[-12.0], [-12.0]])
+ self.Kff = controls.TwoStateFeedForwards(self.B, self.Qff)
- self.InitializeState()
+ self.U_max = numpy.matrix([[12.0], [12.0]])
+ self.U_min = numpy.matrix([[-12.0], [-12.0]])
+
+ self.InitializeState()
class Column(ColumnController):
- def __init__(self, name='Column', disable_indexer=False):
- super(Column, self).__init__(name)
- A_continuous = numpy.matrix(numpy.zeros((4, 4)))
- B_continuous = numpy.matrix(numpy.zeros((4, 2)))
- A_continuous[0, 1] = 1
- A_continuous[1:, 1:] = self.A_continuous
- B_continuous[1:, :] = self.B_continuous
+ def __init__(self, name='Column', disable_indexer=False):
+ super(Column, self).__init__(name)
+ A_continuous = numpy.matrix(numpy.zeros((4, 4)))
+ B_continuous = numpy.matrix(numpy.zeros((4, 2)))
- self.A_continuous = A_continuous
- self.B_continuous = B_continuous
+ A_continuous[0, 1] = 1
+ A_continuous[1:, 1:] = self.A_continuous
+ B_continuous[1:, :] = self.B_continuous
- self.A, self.B = self.ContinuousToDiscrete(
- self.A_continuous, self.B_continuous, self.dt)
+ self.A_continuous = A_continuous
+ self.B_continuous = B_continuous
- self.C = numpy.matrix([[1, 0, 0, 0], [-1, 0, 1, 0]])
- self.D = numpy.matrix([[0, 0], [0, 0]])
+ self.A, self.B = self.ContinuousToDiscrete(self.A_continuous,
+ self.B_continuous, self.dt)
- orig_K = self.K
- self.K = numpy.matrix(numpy.zeros((2, 4)))
- self.K[:, 1:] = orig_K
+ self.C = numpy.matrix([[1, 0, 0, 0], [-1, 0, 1, 0]])
+ self.D = numpy.matrix([[0, 0], [0, 0]])
- glog.debug('K is ' + repr(self.K))
- # TODO(austin): Do we want to damp velocity out or not when disabled?
- #if disable_indexer:
- # self.K[0, 1] = 0.0
- # self.K[1, 1] = 0.0
+ orig_K = self.K
+ self.K = numpy.matrix(numpy.zeros((2, 4)))
+ self.K[:, 1:] = orig_K
- orig_Kff = self.Kff
- self.Kff = numpy.matrix(numpy.zeros((2, 4)))
- self.Kff[:, 1:] = orig_Kff
+ glog.debug('K is ' + repr(self.K))
+ # TODO(austin): Do we want to damp velocity out or not when disabled?
+ #if disable_indexer:
+ # self.K[0, 1] = 0.0
+ # self.K[1, 1] = 0.0
- q_pos = 0.12
- q_vel = 2.00
- self.Q = numpy.matrix([[(q_pos ** 2.0), 0.0, 0.0, 0.0],
- [0.0, (q_vel ** 2.0), 0.0, 0.0],
- [0.0, 0.0, (q_pos ** 2.0), 0.0],
- [0.0, 0.0, 0.0, (q_vel ** 2.0)]])
+ orig_Kff = self.Kff
+ self.Kff = numpy.matrix(numpy.zeros((2, 4)))
+ self.Kff[:, 1:] = orig_Kff
- r_pos = 0.05
- self.R = numpy.matrix([[(r_pos ** 2.0), 0.0],
- [0.0, (r_pos ** 2.0)]])
+ q_pos = 0.12
+ q_vel = 2.00
+ self.Q = numpy.matrix([[(q_pos**2.0), 0.0, 0.0, 0.0],
+ [0.0, (q_vel**2.0), 0.0, 0.0],
+ [0.0, 0.0, (q_pos**2.0), 0.0],
+ [0.0, 0.0, 0.0, (q_vel**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
+ r_pos = 0.05
+ self.R = numpy.matrix([[(r_pos**2.0), 0.0], [0.0, (r_pos**2.0)]])
- self.InitializeState()
+ 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.InitializeState()
class IntegralColumn(Column):
- def __init__(self, name='IntegralColumn', voltage_error_noise=None,
- disable_indexer=False):
- super(IntegralColumn, self).__init__(name)
- A_continuous = numpy.matrix(numpy.zeros((6, 6)))
- A_continuous[0:4, 0:4] = self.A_continuous
- A_continuous[0:4:, 4:6] = self.B_continuous
+ def __init__(self,
+ name='IntegralColumn',
+ voltage_error_noise=None,
+ disable_indexer=False):
+ super(IntegralColumn, self).__init__(name)
- B_continuous = numpy.matrix(numpy.zeros((6, 2)))
- B_continuous[0:4, :] = self.B_continuous
+ A_continuous = numpy.matrix(numpy.zeros((6, 6)))
+ A_continuous[0:4, 0:4] = self.A_continuous
+ A_continuous[0:4:, 4:6] = self.B_continuous
- self.A_continuous = A_continuous
- self.B_continuous = B_continuous
+ B_continuous = numpy.matrix(numpy.zeros((6, 2)))
+ B_continuous[0:4, :] = self.B_continuous
- self.A, self.B = self.ContinuousToDiscrete(
- self.A_continuous, self.B_continuous, self.dt)
+ self.A_continuous = A_continuous
+ self.B_continuous = B_continuous
- C = numpy.matrix(numpy.zeros((2, 6)))
- C[0:2, 0:4] = self.C
- self.C = C
+ self.A, self.B = self.ContinuousToDiscrete(self.A_continuous,
+ self.B_continuous, self.dt)
- self.D = numpy.matrix([[0, 0], [0, 0]])
+ C = numpy.matrix(numpy.zeros((2, 6)))
+ C[0:2, 0:4] = self.C
+ self.C = C
- orig_K = self.K
- self.K = numpy.matrix(numpy.zeros((2, 6)))
- self.K[:, 0:4] = orig_K
+ self.D = numpy.matrix([[0, 0], [0, 0]])
- # TODO(austin): I'm not certain this is ideal. If someone spins the bottom
- # at a constant rate, we'll learn a voltage offset. That should translate
- # directly to a voltage on the turret to hold it steady. I'm also not
- # convinced we care that much. If the indexer is off, it'll stop rather
- # quickly anyways, so this is mostly a moot point.
- if not disable_indexer:
- self.K[0, 4] = 1
- self.K[1, 5] = 1
+ orig_K = self.K
+ self.K = numpy.matrix(numpy.zeros((2, 6)))
+ self.K[:, 0:4] = orig_K
- orig_Kff = self.Kff
- self.Kff = numpy.matrix(numpy.zeros((2, 6)))
- self.Kff[:, 0:4] = orig_Kff
+ # TODO(austin): I'm not certain this is ideal. If someone spins the bottom
+ # at a constant rate, we'll learn a voltage offset. That should translate
+ # directly to a voltage on the turret to hold it steady. I'm also not
+ # convinced we care that much. If the indexer is off, it'll stop rather
+ # quickly anyways, so this is mostly a moot point.
+ if not disable_indexer:
+ self.K[0, 4] = 1
+ self.K[1, 5] = 1
- q_pos = 0.40
- q_vel = 2.00
- q_voltage = 8.0
- if voltage_error_noise is not None:
- q_voltage = voltage_error_noise
+ orig_Kff = self.Kff
+ self.Kff = numpy.matrix(numpy.zeros((2, 6)))
+ self.Kff[:, 0:4] = orig_Kff
- self.Q = numpy.matrix([[(q_pos ** 2.0), 0.0, 0.0, 0.0, 0.0, 0.0],
- [0.0, (q_vel ** 2.0), 0.0, 0.0, 0.0, 0.0],
- [0.0, 0.0, (q_pos ** 2.0), 0.0, 0.0, 0.0],
- [0.0, 0.0, 0.0, (q_vel ** 2.0), 0.0, 0.0],
- [0.0, 0.0, 0.0, 0.0, (q_voltage ** 2.0), 0.0],
- [0.0, 0.0, 0.0, 0.0, 0.0, (q_voltage ** 2.0)]])
+ q_pos = 0.40
+ q_vel = 2.00
+ q_voltage = 8.0
+ if voltage_error_noise is not None:
+ q_voltage = voltage_error_noise
- r_pos = 0.05
- self.R = numpy.matrix([[(r_pos ** 2.0), 0.0],
- [0.0, (r_pos ** 2.0)]])
+ self.Q = numpy.matrix([[(q_pos**2.0), 0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, (q_vel**2.0), 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, (q_pos**2.0), 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, (q_vel**2.0), 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, (q_voltage**2.0), 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0, (q_voltage**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
+ r_pos = 0.05
+ self.R = numpy.matrix([[(r_pos**2.0), 0.0], [0.0, (r_pos**2.0)]])
- self.InitializeState()
+ 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.InitializeState()
class ScenarioPlotter(object):
- def __init__(self):
- # Various lists for graphing things.
- self.t = []
- self.xi = []
- self.xt = []
- self.vi = []
- self.vt = []
- self.ai = []
- self.at = []
- self.x_hat = []
- self.ui = []
- self.ut = []
- self.ui_fb = []
- self.ut_fb = []
- self.offseti = []
- self.offsett = []
- self.turret_error = []
- def run_test(self, column, end_goal,
- controller_column,
- observer_column=None,
- iterations=200):
- """Runs the column plant with an initial condition and goal.
+ def __init__(self):
+ # Various lists for graphing things.
+ self.t = []
+ self.xi = []
+ self.xt = []
+ self.vi = []
+ self.vt = []
+ self.ai = []
+ self.at = []
+ self.x_hat = []
+ self.ui = []
+ self.ut = []
+ self.ui_fb = []
+ self.ut_fb = []
+ self.offseti = []
+ self.offsett = []
+ self.turret_error = []
- Args:
- column: column object to use.
- end_goal: end_goal state.
- controller_column: Intake object to get K from, or None if we should
- use column.
- observer_column: 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.
- """
+ def run_test(self,
+ column,
+ end_goal,
+ controller_column,
+ observer_column=None,
+ iterations=200):
+ """Runs the column plant with an initial condition and goal.
- if controller_column is None:
- controller_column = column
+ Args:
+ column: column object to use.
+ end_goal: end_goal state.
+ controller_column: Intake object to get K from, or None if we should
+ use column.
+ observer_column: 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.
+ """
- vbat = 12.0
+ if controller_column is None:
+ controller_column = column
- if self.t:
- initial_t = self.t[-1] + column.dt
- else:
- initial_t = 0
+ vbat = 12.0
- goal = numpy.concatenate((column.X, numpy.matrix(numpy.zeros((2, 1)))), axis=0)
+ if self.t:
+ initial_t = self.t[-1] + column.dt
+ else:
+ initial_t = 0
- profile = TrapezoidProfile(column.dt)
- profile.set_maximum_acceleration(10.0)
- profile.set_maximum_velocity(3.0)
- profile.SetGoal(goal[2, 0])
+ goal = numpy.concatenate((column.X, numpy.matrix(numpy.zeros((2, 1)))),
+ axis=0)
- U_last = numpy.matrix(numpy.zeros((2, 1)))
- for i in xrange(iterations):
- observer_column.Y = column.Y
- observer_column.CorrectObserver(U_last)
+ profile = TrapezoidProfile(column.dt)
+ profile.set_maximum_acceleration(10.0)
+ profile.set_maximum_velocity(3.0)
+ profile.SetGoal(goal[2, 0])
- self.offseti.append(observer_column.X_hat[4, 0])
- self.offsett.append(observer_column.X_hat[5, 0])
- self.x_hat.append(observer_column.X_hat[0, 0])
+ U_last = numpy.matrix(numpy.zeros((2, 1)))
+ for i in xrange(iterations):
+ observer_column.Y = column.Y
+ observer_column.CorrectObserver(U_last)
- next_goal = numpy.concatenate(
- (end_goal[0:2, :],
- profile.Update(end_goal[2, 0], end_goal[3, 0]),
- end_goal[4:6, :]),
- axis=0)
+ self.offseti.append(observer_column.X_hat[4, 0])
+ self.offsett.append(observer_column.X_hat[5, 0])
+ self.x_hat.append(observer_column.X_hat[0, 0])
- ff_U = controller_column.Kff * (next_goal - observer_column.A * goal)
- fb_U = controller_column.K * (goal - observer_column.X_hat)
- self.turret_error.append((goal[2, 0] - column.X[2, 0]) * 100.0)
- self.ui_fb.append(fb_U[0, 0])
- self.ut_fb.append(fb_U[1, 0])
+ next_goal = numpy.concatenate(
+ (end_goal[0:2, :], profile.Update(
+ end_goal[2, 0], end_goal[3, 0]), end_goal[4:6, :]),
+ axis=0)
- U_uncapped = ff_U + fb_U
+ ff_U = controller_column.Kff * (
+ next_goal - observer_column.A * goal)
+ fb_U = controller_column.K * (goal - observer_column.X_hat)
+ self.turret_error.append((goal[2, 0] - column.X[2, 0]) * 100.0)
+ self.ui_fb.append(fb_U[0, 0])
+ self.ut_fb.append(fb_U[1, 0])
- U = U_uncapped.copy()
- U[0, 0] = numpy.clip(U[0, 0], -vbat, vbat)
- U[1, 0] = numpy.clip(U[1, 0], -vbat, vbat)
- self.xi.append(column.X[0, 0])
- self.xt.append(column.X[2, 0])
+ U_uncapped = ff_U + fb_U
- if self.vi:
- last_vi = self.vi[-1]
- else:
- last_vi = 0
- if self.vt:
- last_vt = self.vt[-1]
- else:
- last_vt = 0
+ U = U_uncapped.copy()
+ U[0, 0] = numpy.clip(U[0, 0], -vbat, vbat)
+ U[1, 0] = numpy.clip(U[1, 0], -vbat, vbat)
+ self.xi.append(column.X[0, 0])
+ self.xt.append(column.X[2, 0])
- self.vi.append(column.X[1, 0])
- self.vt.append(column.X[3, 0])
- self.ai.append((self.vi[-1] - last_vi) / column.dt)
- self.at.append((self.vt[-1] - last_vt) / column.dt)
+ if self.vi:
+ last_vi = self.vi[-1]
+ else:
+ last_vi = 0
+ if self.vt:
+ last_vt = self.vt[-1]
+ else:
+ last_vt = 0
- offset = 0.0
- if i > 100:
- offset = 1.0
- column.Update(U + numpy.matrix([[0.0], [offset]]))
+ self.vi.append(column.X[1, 0])
+ self.vt.append(column.X[3, 0])
+ self.ai.append((self.vi[-1] - last_vi) / column.dt)
+ self.at.append((self.vt[-1] - last_vt) / column.dt)
- observer_column.PredictObserver(U)
+ offset = 0.0
+ if i > 100:
+ offset = 1.0
+ column.Update(U + numpy.matrix([[0.0], [offset]]))
- self.t.append(initial_t + i * column.dt)
- self.ui.append(U[0, 0])
- self.ut.append(U[1, 0])
+ observer_column.PredictObserver(U)
- ff_U -= U_uncapped - U
- goal = controller_column.A * goal + controller_column.B * ff_U
+ self.t.append(initial_t + i * column.dt)
+ self.ui.append(U[0, 0])
+ self.ut.append(U[1, 0])
- if U[1, 0] != U_uncapped[1, 0]:
- profile.MoveCurrentState(
- numpy.matrix([[goal[2, 0]], [goal[3, 0]]]))
+ ff_U -= U_uncapped - U
+ goal = controller_column.A * goal + controller_column.B * ff_U
- glog.debug('Time: %f', self.t[-1])
- glog.debug('goal_error %s', repr((end_goal - goal).T))
- glog.debug('error %s', repr((observer_column.X_hat - end_goal).T))
+ if U[1, 0] != U_uncapped[1, 0]:
+ profile.MoveCurrentState(
+ numpy.matrix([[goal[2, 0]], [goal[3, 0]]]))
- def Plot(self):
- pylab.subplot(3, 1, 1)
- pylab.plot(self.t, self.xi, label='x_indexer')
- pylab.plot(self.t, self.xt, label='x_turret')
- pylab.plot(self.t, self.x_hat, label='x_hat')
- pylab.plot(self.t, self.turret_error, label='turret_error * 100')
- pylab.legend()
+ glog.debug('Time: %f', self.t[-1])
+ glog.debug('goal_error %s', repr((end_goal - goal).T))
+ glog.debug('error %s', repr((observer_column.X_hat - end_goal).T))
- pylab.subplot(3, 1, 2)
- pylab.plot(self.t, self.ui, label='u_indexer')
- pylab.plot(self.t, self.ui_fb, label='u_indexer_fb')
- pylab.plot(self.t, self.ut, label='u_turret')
- pylab.plot(self.t, self.ut_fb, label='u_turret_fb')
- pylab.plot(self.t, self.offseti, label='voltage_offset_indexer')
- pylab.plot(self.t, self.offsett, label='voltage_offset_turret')
- pylab.legend()
+ def Plot(self):
+ pylab.subplot(3, 1, 1)
+ pylab.plot(self.t, self.xi, label='x_indexer')
+ pylab.plot(self.t, self.xt, label='x_turret')
+ pylab.plot(self.t, self.x_hat, label='x_hat')
+ pylab.plot(self.t, self.turret_error, label='turret_error * 100')
+ pylab.legend()
- pylab.subplot(3, 1, 3)
- pylab.plot(self.t, self.ai, label='a_indexer')
- pylab.plot(self.t, self.at, label='a_turret')
- pylab.plot(self.t, self.vi, label='v_indexer')
- pylab.plot(self.t, self.vt, label='v_turret')
- pylab.legend()
+ pylab.subplot(3, 1, 2)
+ pylab.plot(self.t, self.ui, label='u_indexer')
+ pylab.plot(self.t, self.ui_fb, label='u_indexer_fb')
+ pylab.plot(self.t, self.ut, label='u_turret')
+ pylab.plot(self.t, self.ut_fb, label='u_turret_fb')
+ pylab.plot(self.t, self.offseti, label='voltage_offset_indexer')
+ pylab.plot(self.t, self.offsett, label='voltage_offset_turret')
+ pylab.legend()
- pylab.show()
+ pylab.subplot(3, 1, 3)
+ pylab.plot(self.t, self.ai, label='a_indexer')
+ pylab.plot(self.t, self.at, label='a_turret')
+ pylab.plot(self.t, self.vi, label='v_indexer')
+ pylab.plot(self.t, self.vt, label='v_turret')
+ pylab.legend()
+
+ pylab.show()
def main(argv):
- scenario_plotter = ScenarioPlotter()
+ scenario_plotter = ScenarioPlotter()
- column = Column()
- column_controller = IntegralColumn()
- observer_column = IntegralColumn()
+ column = Column()
+ column_controller = IntegralColumn()
+ observer_column = IntegralColumn()
- initial_X = numpy.matrix([[0.0], [0.0], [0.0], [0.0]])
- R = numpy.matrix([[0.0], [10.0], [5.0], [0.0], [0.0], [0.0]])
- scenario_plotter.run_test(column, end_goal=R, controller_column=column_controller,
- observer_column=observer_column, iterations=400)
+ initial_X = numpy.matrix([[0.0], [0.0], [0.0], [0.0]])
+ R = numpy.matrix([[0.0], [10.0], [5.0], [0.0], [0.0], [0.0]])
+ scenario_plotter.run_test(
+ column,
+ end_goal=R,
+ controller_column=column_controller,
+ observer_column=observer_column,
+ iterations=400)
- if FLAGS.plot:
- scenario_plotter.Plot()
+ if FLAGS.plot:
+ scenario_plotter.Plot()
- if len(argv) != 7:
- glog.fatal('Expected .h file name and .cc file names')
- else:
- namespaces = ['y2017', 'control_loops', 'superstructure', 'column']
- column = Column('Column')
- loop_writer = control_loop.ControlLoopWriter('Column', [column],
- namespaces=namespaces)
- loop_writer.AddConstant(control_loop.Constant(
- 'kIndexerFreeSpeed', '%f', column.indexer.free_speed))
- loop_writer.AddConstant(control_loop.Constant(
- 'kIndexerOutputRatio', '%f', column.indexer.G))
- loop_writer.AddConstant(control_loop.Constant(
- 'kTurretFreeSpeed', '%f', column.turret.free_speed))
- loop_writer.AddConstant(control_loop.Constant(
- 'kTurretOutputRatio', '%f', column.turret.G))
- loop_writer.Write(argv[1], argv[2])
+ if len(argv) != 7:
+ glog.fatal('Expected .h file name and .cc file names')
+ else:
+ namespaces = ['y2017', 'control_loops', 'superstructure', 'column']
+ column = Column('Column')
+ loop_writer = control_loop.ControlLoopWriter(
+ 'Column', [column], namespaces=namespaces)
+ loop_writer.AddConstant(
+ control_loop.Constant('kIndexerFreeSpeed', '%f',
+ column.indexer.free_speed))
+ loop_writer.AddConstant(
+ control_loop.Constant('kIndexerOutputRatio', '%f',
+ column.indexer.G))
+ loop_writer.AddConstant(
+ control_loop.Constant('kTurretFreeSpeed', '%f',
+ column.turret.free_speed))
+ loop_writer.AddConstant(
+ control_loop.Constant('kTurretOutputRatio', '%f', column.turret.G))
+ loop_writer.Write(argv[1], argv[2])
- # IntegralColumn controller 1 will disable the indexer.
- integral_column = IntegralColumn('IntegralColumn')
- disabled_integral_column = IntegralColumn('DisabledIntegralColumn',
- disable_indexer=True)
- integral_loop_writer = control_loop.ControlLoopWriter(
- 'IntegralColumn', [integral_column, disabled_integral_column],
- namespaces=namespaces)
- integral_loop_writer.Write(argv[3], argv[4])
+ # IntegralColumn controller 1 will disable the indexer.
+ integral_column = IntegralColumn('IntegralColumn')
+ disabled_integral_column = IntegralColumn(
+ 'DisabledIntegralColumn', disable_indexer=True)
+ integral_loop_writer = control_loop.ControlLoopWriter(
+ 'IntegralColumn', [integral_column, disabled_integral_column],
+ namespaces=namespaces)
+ integral_loop_writer.Write(argv[3], argv[4])
- stuck_integral_column = IntegralColumn('StuckIntegralColumn', voltage_error_noise=8.0)
- stuck_integral_loop_writer = control_loop.ControlLoopWriter(
- 'StuckIntegralColumn', [stuck_integral_column], namespaces=namespaces)
- stuck_integral_loop_writer.Write(argv[5], argv[6])
+ stuck_integral_column = IntegralColumn(
+ 'StuckIntegralColumn', voltage_error_noise=8.0)
+ stuck_integral_loop_writer = control_loop.ControlLoopWriter(
+ 'StuckIntegralColumn', [stuck_integral_column],
+ namespaces=namespaces)
+ stuck_integral_loop_writer.Write(argv[5], argv[6])
if __name__ == '__main__':
- argv = FLAGS(sys.argv)
- glog.init()
- sys.exit(main(argv))
+ argv = FLAGS(sys.argv)
+ glog.init()
+ sys.exit(main(argv))