Reformat python and c++ files
Change-Id: I7d7d99a2094c2a9181ed882735b55159c14db3b0
diff --git a/y2017/control_loops/python/hood.py b/y2017/control_loops/python/hood.py
index fb5aa4e..58bd15e 100755
--- a/y2017/control_loops/python/hood.py
+++ b/y2017/control_loops/python/hood.py
@@ -12,329 +12,339 @@
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 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
- self.free_speed_rpm = 13180.0
- # Free Speed in rotations/second.
- self.free_speed = self.free_speed_rpm / 60
- # 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 * 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)
- # The last gear reduction (encoder -> hood angle)
- self.last_G = (20.0 / 345.0)
- # Gear ratio
- self.G = (12.0 / 60.0) * (14.0 / 36.0) * (14.0 / 36.0) * self.last_G
+ 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
+ self.free_speed_rpm = 13180.0
+ # Free Speed in rotations/second.
+ self.free_speed = self.free_speed_rpm / 60
+ # Free Current in Amps
+ self.free_current = 1.8
- # 36 tooth gear inertia in kg * m^2
- self.big_gear_inertia = 0.5 * 0.039 * ((36.0 / 40.0 * 0.025) ** 2)
+ # Resistance of the motor
+ self.R = 12.0 / self.stall_current
+ # Motor velocity constant
+ self.Kv = ((self.free_speed * 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)
+ # The last gear reduction (encoder -> hood angle)
+ self.last_G = (20.0 / 345.0)
+ # Gear ratio
+ self.G = (12.0 / 60.0) * (14.0 / 36.0) * (14.0 / 36.0) * self.last_G
- # Motor inertia in kg * m^2
- self.motor_inertia = 0.000006
- glog.debug(self.big_gear_inertia)
+ # 36 tooth gear inertia in kg * m^2
+ self.big_gear_inertia = 0.5 * 0.039 * ((36.0 / 40.0 * 0.025)**2)
- # Moment of inertia, measured in CAD.
- # Extra mass to compensate for friction is added on.
- self.J = 0.08 + 2.3 + \
- 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)
+ # Motor inertia in kg * m^2
+ self.motor_inertia = 0.000006
+ glog.debug(self.big_gear_inertia)
- # Control loop time step
- self.dt = 0.005
+ # Moment of inertia, measured in CAD.
+ # Extra mass to compensate for friction is added on.
+ self.J = 0.08 + 2.3 + \
+ 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)
- # State is [position, velocity]
- # Input is [Voltage]
+ # Control loop time step
+ self.dt = 0.005
- C1 = self.Kt / (self.R * self.J * self.Kv * self.G * self.G)
- C2 = self.Kt / (self.J * self.R * self.G)
+ # State is [position, velocity]
+ # Input is [Voltage]
- self.A_continuous = numpy.matrix(
- [[0, 1],
- [0, -C1]])
+ C1 = self.Kt / (self.R * self.J * self.Kv * self.G * self.G)
+ C2 = self.Kt / (self.J * self.R * self.G)
- # Start with the unmodified input
- self.B_continuous = numpy.matrix(
- [[0],
- [C2]])
+ self.A_continuous = numpy.matrix([[0, 1], [0, -C1]])
- self.C = numpy.matrix([[1, 0]])
- self.D = numpy.matrix([[0]])
+ # Start with the unmodified input
+ self.B_continuous = numpy.matrix([[0], [C2]])
- self.A, self.B = self.ContinuousToDiscrete(
- self.A_continuous, self.B_continuous, self.dt)
+ self.C = numpy.matrix([[1, 0]])
+ self.D = numpy.matrix([[0]])
- controllability = controls.ctrb(self.A, self.B)
+ self.A, self.B = self.ContinuousToDiscrete(self.A_continuous,
+ self.B_continuous, self.dt)
- glog.debug('Free speed is %f',
- -self.B_continuous[1, 0] / self.A_continuous[1, 1] * 12.0)
- glog.debug(repr(self.A_continuous))
+ controllability = controls.ctrb(self.A, self.B)
- # Calculate the LQR controller gain
- q_pos = 0.015
- q_vel = 0.40
- self.Q = numpy.matrix([[(1.0 / (q_pos ** 2.0)), 0.0],
- [0.0, (1.0 / (q_vel ** 2.0))]])
+ glog.debug('Free speed is %f',
+ -self.B_continuous[1, 0] / self.A_continuous[1, 1] * 12.0)
+ glog.debug(repr(self.A_continuous))
- self.R = numpy.matrix([[(5.0 / (12.0 ** 2.0))]])
- self.K = controls.dlqr(self.A, self.B, self.Q, self.R)
+ # Calculate the LQR controller gain
+ q_pos = 0.015
+ q_vel = 0.40
+ self.Q = numpy.matrix([[(1.0 / (q_pos**2.0)), 0.0],
+ [0.0, (1.0 / (q_vel**2.0))]])
- # 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.R = numpy.matrix([[(5.0 / (12.0**2.0))]])
+ self.K = controls.dlqr(self.A, self.B, self.Q, self.R)
- self.Kff = controls.TwoStateFeedForwards(self.B, self.Qff)
+ # 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))]])
- glog.debug('K %s', repr(self.K))
- glog.debug('Poles are %s',
- repr(numpy.linalg.eig(self.A - self.B * self.K)[0]))
+ self.Kff = controls.TwoStateFeedForwards(self.B, self.Qff)
- q_pos = 0.10
- q_vel = 1.65
- self.Q = numpy.matrix([[(q_pos ** 2.0), 0.0],
- [0.0, (q_vel ** 2.0)]])
+ glog.debug('K %s', repr(self.K))
+ glog.debug('Poles are %s',
+ repr(numpy.linalg.eig(self.A - self.B * self.K)[0]))
- r_volts = 0.025
- self.R = numpy.matrix([[(r_volts ** 2.0)]])
+ q_pos = 0.10
+ q_vel = 1.65
+ self.Q = numpy.matrix([[(q_pos**2.0), 0.0], [0.0, (q_vel**2.0)]])
- self.KalmanGain, self.Q_steady = controls.kalman(
- A=self.A, B=self.B, C=self.C, Q=self.Q, R=self.R)
+ r_volts = 0.025
+ self.R = numpy.matrix([[(r_volts**2.0)]])
- glog.debug('Kal %s', repr(self.KalmanGain))
- self.L = self.A * self.KalmanGain
- glog.debug('KalL is %s', repr(self.L))
+ self.KalmanGain, self.Q_steady = controls.kalman(
+ A=self.A, B=self.B, C=self.C, Q=self.Q, R=self.R)
- # 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]])
+ glog.debug('Kal %s', repr(self.KalmanGain))
+ self.L = self.A * self.KalmanGain
+ glog.debug('KalL is %s', repr(self.L))
- self.InitializeState()
+ # 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
+ def __init__(self, name='IntegralHood'):
+ super(IntegralHood, self).__init__(name=name)
- 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.A_continuous_unaugmented = self.A_continuous
+ self.B_continuous_unaugmented = self.B_continuous
- self.B_continuous = numpy.matrix(numpy.zeros((3, 1)))
- self.B_continuous[0:2, 0] = self.B_continuous_unaugmented
+ 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.C_unaugmented = self.C
- self.C = numpy.matrix(numpy.zeros((1, 3)))
- self.C[0:1, 0:2] = self.C_unaugmented
+ self.B_continuous = numpy.matrix(numpy.zeros((3, 1)))
+ self.B_continuous[0:2, 0] = self.B_continuous_unaugmented
- self.A, self.B = self.ContinuousToDiscrete(
- self.A_continuous, self.B_continuous, self.dt)
+ self.C_unaugmented = self.C
+ self.C = numpy.matrix(numpy.zeros((1, 3)))
+ self.C[0:1, 0:2] = self.C_unaugmented
- q_pos = 0.01
- q_vel = 4.0
- q_voltage = 4.0
- 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)]])
+ self.A, self.B = self.ContinuousToDiscrete(self.A_continuous,
+ self.B_continuous, self.dt)
- r_pos = 0.001
- self.R = numpy.matrix([[(r_pos ** 2.0)]])
+ q_pos = 0.01
+ q_vel = 4.0
+ q_voltage = 4.0
+ 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)]])
- 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 = 0.001
+ self.R = numpy.matrix([[(r_pos**2.0)]])
- 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.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.Kff = numpy.concatenate((self.Kff, numpy.matrix(numpy.zeros((1, 1)))), axis=1)
+ 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.InitializeState()
+ 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.v_hat = []
- 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.
+ def __init__(self):
+ # Various lists for graphing things.
+ self.t = []
+ self.x = []
+ self.v = []
+ self.v_hat = []
+ self.a = []
+ self.x_hat = []
+ self.u = []
+ self.offset = []
- 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.
- """
+ def run_test(self,
+ hood,
+ end_goal,
+ controller_hood,
+ observer_hood=None,
+ iterations=200):
+ """Runs the hood plant with an initial condition and goal.
- if controller_hood is None:
- controller_hood = hood
+ 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.
+ """
- vbat = 12.0
+ if controller_hood is None:
+ controller_hood = hood
- if self.t:
- initial_t = self.t[-1] + hood.dt
- else:
- initial_t = 0
+ vbat = 12.0
- goal = numpy.concatenate((hood.X, numpy.matrix(numpy.zeros((1, 1)))), axis=0)
+ if self.t:
+ initial_t = self.t[-1] + hood.dt
+ else:
+ initial_t = 0
- profile = TrapezoidProfile(hood.dt)
- profile.set_maximum_acceleration(10.0)
- profile.set_maximum_velocity(1.0)
- profile.SetGoal(goal[0, 0])
+ goal = numpy.concatenate((hood.X, numpy.matrix(numpy.zeros((1, 1)))),
+ axis=0)
- U_last = numpy.matrix(numpy.zeros((1, 1)))
- for i in xrange(iterations):
- observer_hood.Y = hood.Y
- observer_hood.CorrectObserver(U_last)
+ profile = TrapezoidProfile(hood.dt)
+ profile.set_maximum_acceleration(10.0)
+ profile.set_maximum_velocity(1.0)
+ profile.SetGoal(goal[0, 0])
- self.offset.append(observer_hood.X_hat[2, 0])
- self.x_hat.append(observer_hood.X_hat[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)
- next_goal = numpy.concatenate(
- (profile.Update(end_goal[0, 0], end_goal[1, 0]),
- numpy.matrix(numpy.zeros((1, 1)))),
- axis=0)
+ self.offset.append(observer_hood.X_hat[2, 0])
+ self.x_hat.append(observer_hood.X_hat[0, 0])
- ff_U = controller_hood.Kff * (next_goal - observer_hood.A * goal)
+ next_goal = numpy.concatenate(
+ (profile.Update(end_goal[0, 0], end_goal[1, 0]),
+ numpy.matrix(numpy.zeros((1, 1)))),
+ axis=0)
- 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])
+ ff_U = controller_hood.Kff * (next_goal - observer_hood.A * goal)
- if self.v:
- last_v = self.v[-1]
- else:
- last_v = 0
+ 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])
- self.v.append(hood.X[1, 0])
- self.a.append((self.v[-1] - last_v) / hood.dt)
- self.v_hat.append(observer_hood.X_hat[1, 0])
+ if self.v:
+ last_v = self.v[-1]
+ else:
+ last_v = 0
- offset = 0.0
- if i > 100:
- offset = 2.0
- hood.Update(U + offset)
+ self.v.append(hood.X[1, 0])
+ self.a.append((self.v[-1] - last_v) / hood.dt)
+ self.v_hat.append(observer_hood.X_hat[1, 0])
- observer_hood.PredictObserver(U)
+ offset = 0.0
+ if i > 100:
+ offset = 2.0
+ hood.Update(U + offset)
- self.t.append(initial_t + i * hood.dt)
- self.u.append(U[0, 0])
+ observer_hood.PredictObserver(U)
- ff_U -= U_uncapped - U
- goal = controller_hood.A * goal + controller_hood.B * ff_U
+ self.t.append(initial_t + i * hood.dt)
+ self.u.append(U[0, 0])
- if U[0, 0] != U_uncapped[0, 0]:
- profile.MoveCurrentState(
- numpy.matrix([[goal[0, 0]], [goal[1, 0]]]))
+ ff_U -= U_uncapped - U
+ goal = controller_hood.A * goal + controller_hood.B * ff_U
- 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))
+ if U[0, 0] != U_uncapped[0, 0]:
+ profile.MoveCurrentState(
+ numpy.matrix([[goal[0, 0]], [goal[1, 0]]]))
- 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.plot(self.t, self.v, label='v')
- pylab.plot(self.t, self.v_hat, label='v_hat')
- pylab.legend()
+ 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))
- pylab.subplot(3, 1, 2)
- pylab.plot(self.t, self.u, label='u')
- pylab.plot(self.t, self.offset, label='voltage_offset')
- pylab.legend()
+ 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.plot(self.t, self.v, label='v')
+ pylab.plot(self.t, self.v_hat, label='v_hat')
+ pylab.legend()
- pylab.subplot(3, 1, 3)
- pylab.plot(self.t, self.a, label='a')
- 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.show()
+ pylab.subplot(3, 1, 3)
+ pylab.plot(self.t, self.a, label='a')
+ pylab.legend()
+
+ pylab.show()
def main(argv):
- scenario_plotter = ScenarioPlotter()
+ scenario_plotter = ScenarioPlotter()
- hood = Hood()
- hood_controller = IntegralHood()
- observer_hood = IntegralHood()
+ 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/4.0], [0.0], [0.0]])
- scenario_plotter.run_test(hood, end_goal=R,
- controller_hood=hood_controller,
- observer_hood=observer_hood, iterations=200)
+ # Test moving the hood with constant separation.
+ initial_X = numpy.matrix([[0.0], [0.0]])
+ R = numpy.matrix([[numpy.pi / 4.0], [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()
+ 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.AddConstant(control_loop.Constant(
- 'kFreeSpeed', '%f', hood.free_speed))
- loop_writer.AddConstant(control_loop.Constant(
- 'kOutputRatio', '%f', hood.G))
- loop_writer.Write(argv[1], argv[2])
+ # 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.AddConstant(
+ control_loop.Constant('kFreeSpeed', '%f', hood.free_speed))
+ loop_writer.AddConstant(
+ control_loop.Constant('kOutputRatio', '%f', hood.G))
+ 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.AddConstant(control_loop.Constant('kLastReduction', '%f',
- integral_hood.last_G))
- integral_loop_writer.Write(argv[3], argv[4])
+ integral_hood = IntegralHood('IntegralHood')
+ integral_loop_writer = control_loop.ControlLoopWriter(
+ 'IntegralHood', [integral_hood], namespaces=namespaces)
+ integral_loop_writer.AddConstant(
+ control_loop.Constant('kLastReduction', '%f', integral_hood.last_G))
+ integral_loop_writer.Write(argv[3], argv[4])
if __name__ == '__main__':
- argv = FLAGS(sys.argv)
- glog.init()
- sys.exit(main(argv))
+ argv = FLAGS(sys.argv)
+ glog.init()
+ sys.exit(main(argv))