Reformat python and c++ files
Change-Id: I7d7d99a2094c2a9181ed882735b55159c14db3b0
diff --git a/y2014/control_loops/python/claw.py b/y2014/control_loops/python/claw.py
index 3ae9fe9..cade03d 100755
--- a/y2014/control_loops/python/claw.py
+++ b/y2014/control_loops/python/claw.py
@@ -13,493 +13,500 @@
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 Claw(control_loop.ControlLoop):
- def __init__(self, name="RawClaw"):
- super(Claw, self).__init__(name)
- # Stall Torque in N m
- self.stall_torque = 2.42
- # Stall Current in Amps
- self.stall_current = 133
- # Free Speed in RPM
- self.free_speed = 5500.0
- # Free Current in Amps
- self.free_current = 2.7
- # Moment of inertia of the claw in kg m^2
- self.J_top = 2.8
- self.J_bottom = 3.0
- # 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) /
- (13.5 - self.R * self.free_current))
- # Torque constant
- self.Kt = self.stall_torque / self.stall_current
- # Gear ratio
- self.G = 14.0 / 48.0 * 18.0 / 32.0 * 18.0 / 66.0 * 12.0 / 60.0
- # Control loop time step
- self.dt = 0.005
+ def __init__(self, name="RawClaw"):
+ super(Claw, self).__init__(name)
+ # Stall Torque in N m
+ self.stall_torque = 2.42
+ # Stall Current in Amps
+ self.stall_current = 133
+ # Free Speed in RPM
+ self.free_speed = 5500.0
+ # Free Current in Amps
+ self.free_current = 2.7
+ # Moment of inertia of the claw in kg m^2
+ self.J_top = 2.8
+ self.J_bottom = 3.0
- # State is [bottom position, bottom velocity, top - bottom position,
- # top - bottom velocity]
- # Input is [bottom power, top power - bottom power * J_top / J_bottom]
- # Motor time constants. difference_bottom refers to the constant for how the
- # bottom velocity affects the difference of the top and bottom velocities.
- self.common_motor_constant = -self.Kt / self.Kv / (self.G * self.G * self.R)
- self.bottom_bottom = self.common_motor_constant / self.J_bottom
- self.difference_bottom = -self.common_motor_constant * (1 / self.J_bottom
- - 1 / self.J_top)
- self.difference_difference = self.common_motor_constant / self.J_top
- # State feedback matrices
+ # 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) /
+ (13.5 - self.R * self.free_current))
+ # Torque constant
+ self.Kt = self.stall_torque / self.stall_current
+ # Gear ratio
+ self.G = 14.0 / 48.0 * 18.0 / 32.0 * 18.0 / 66.0 * 12.0 / 60.0
+ # Control loop time step
+ self.dt = 0.005
- self.A_continuous = numpy.matrix(
- [[0, 0, 1, 0],
- [0, 0, 0, 1],
- [0, 0, self.bottom_bottom, 0],
- [0, 0, self.difference_bottom, self.difference_difference]])
+ # State is [bottom position, bottom velocity, top - bottom position,
+ # top - bottom velocity]
+ # Input is [bottom power, top power - bottom power * J_top / J_bottom]
+ # Motor time constants. difference_bottom refers to the constant for how the
+ # bottom velocity affects the difference of the top and bottom velocities.
+ self.common_motor_constant = -self.Kt / self.Kv / (
+ self.G * self.G * self.R)
+ self.bottom_bottom = self.common_motor_constant / self.J_bottom
+ self.difference_bottom = -self.common_motor_constant * (
+ 1 / self.J_bottom - 1 / self.J_top)
+ self.difference_difference = self.common_motor_constant / self.J_top
+ # State feedback matrices
- self.A_bottom_cont = numpy.matrix(
- [[0, 1],
- [0, self.bottom_bottom]])
+ self.A_continuous = numpy.matrix(
+ [[0, 0, 1, 0], [0, 0, 0, 1], [0, 0, self.bottom_bottom, 0],
+ [0, 0, self.difference_bottom, self.difference_difference]])
- self.A_diff_cont = numpy.matrix(
- [[0, 1],
- [0, self.difference_difference]])
+ self.A_bottom_cont = numpy.matrix([[0, 1], [0, self.bottom_bottom]])
- self.motor_feedforward = self.Kt / (self.G * self.R)
- self.motor_feedforward_bottom = self.motor_feedforward / self.J_bottom
- self.motor_feedforward_difference = self.motor_feedforward / self.J_top
- self.motor_feedforward_difference_bottom = (
- self.motor_feedforward * (1 / self.J_bottom - 1 / self.J_top))
- self.B_continuous = numpy.matrix(
- [[0, 0],
- [0, 0],
- [self.motor_feedforward_bottom, 0],
- [-self.motor_feedforward_bottom, self.motor_feedforward_difference]])
+ self.A_diff_cont = numpy.matrix([[0, 1],
+ [0, self.difference_difference]])
- glog.debug('Cont X_ss %f', self.motor_feedforward / -self.common_motor_constant)
+ self.motor_feedforward = self.Kt / (self.G * self.R)
+ self.motor_feedforward_bottom = self.motor_feedforward / self.J_bottom
+ self.motor_feedforward_difference = self.motor_feedforward / self.J_top
+ self.motor_feedforward_difference_bottom = (
+ self.motor_feedforward * (1 / self.J_bottom - 1 / self.J_top))
+ self.B_continuous = numpy.matrix([[0, 0], [0, 0],
+ [self.motor_feedforward_bottom, 0],
+ [
+ -self.motor_feedforward_bottom,
+ self.motor_feedforward_difference
+ ]])
- self.B_bottom_cont = numpy.matrix(
- [[0],
- [self.motor_feedforward_bottom]])
+ glog.debug('Cont X_ss %f',
+ self.motor_feedforward / -self.common_motor_constant)
- self.B_diff_cont = numpy.matrix(
- [[0],
- [self.motor_feedforward_difference]])
+ self.B_bottom_cont = numpy.matrix([[0],
+ [self.motor_feedforward_bottom]])
- self.C = numpy.matrix([[1, 0, 0, 0],
- [1, 1, 0, 0]])
- self.D = numpy.matrix([[0, 0],
- [0, 0]])
+ self.B_diff_cont = numpy.matrix([[0],
+ [self.motor_feedforward_difference]])
- self.A, self.B = self.ContinuousToDiscrete(
- self.A_continuous, self.B_continuous, self.dt)
+ self.C = numpy.matrix([[1, 0, 0, 0], [1, 1, 0, 0]])
+ self.D = numpy.matrix([[0, 0], [0, 0]])
- self.A_bottom, self.B_bottom = controls.c2d(
- self.A_bottom_cont, self.B_bottom_cont, self.dt)
- self.A_diff, self.B_diff = controls.c2d(
- self.A_diff_cont, self.B_diff_cont, self.dt)
+ self.A, self.B = self.ContinuousToDiscrete(self.A_continuous,
+ self.B_continuous, self.dt)
- self.K_bottom = controls.dplace(self.A_bottom, self.B_bottom,
- [0.87 + 0.05j, 0.87 - 0.05j])
- self.K_diff = controls.dplace(self.A_diff, self.B_diff,
- [0.85 + 0.05j, 0.85 - 0.05j])
+ self.A_bottom, self.B_bottom = controls.c2d(self.A_bottom_cont,
+ self.B_bottom_cont, self.dt)
+ self.A_diff, self.B_diff = controls.c2d(self.A_diff_cont,
+ self.B_diff_cont, self.dt)
- glog.debug('K_diff %s', str(self.K_diff))
- glog.debug('K_bottom %s', str(self.K_bottom))
+ self.K_bottom = controls.dplace(self.A_bottom, self.B_bottom,
+ [0.87 + 0.05j, 0.87 - 0.05j])
+ self.K_diff = controls.dplace(self.A_diff, self.B_diff,
+ [0.85 + 0.05j, 0.85 - 0.05j])
- glog.debug('A')
- glog.debug(self.A)
- glog.debug('B')
- glog.debug(self.B)
+ glog.debug('K_diff %s', str(self.K_diff))
+ glog.debug('K_bottom %s', str(self.K_bottom))
-
- self.Q = numpy.matrix([[(1.0 / (0.10 ** 2.0)), 0.0, 0.0, 0.0],
- [0.0, (1.0 / (0.06 ** 2.0)), 0.0, 0.0],
- [0.0, 0.0, 0.10, 0.0],
- [0.0, 0.0, 0.0, 0.1]])
+ glog.debug('A')
+ glog.debug(self.A)
+ glog.debug('B')
+ glog.debug(self.B)
- self.R = numpy.matrix([[(1.0 / (40.0 ** 2.0)), 0.0],
- [0.0, (1.0 / (5.0 ** 2.0))]])
- #self.K = controls.dlqr(self.A, self.B, self.Q, self.R)
+ self.Q = numpy.matrix([[(1.0 / (0.10**2.0)), 0.0, 0.0, 0.0],
+ [0.0, (1.0 / (0.06**2.0)), 0.0, 0.0],
+ [0.0, 0.0, 0.10, 0.0], [0.0, 0.0, 0.0, 0.1]])
- self.K = numpy.matrix([[self.K_bottom[0, 0], 0.0, self.K_bottom[0, 1], 0.0],
- [0.0, self.K_diff[0, 0], 0.0, self.K_diff[0, 1]]])
+ self.R = numpy.matrix([[(1.0 / (40.0**2.0)), 0.0],
+ [0.0, (1.0 / (5.0**2.0))]])
+ #self.K = controls.dlqr(self.A, self.B, self.Q, self.R)
- # Compute the feed forwards aceleration term.
- self.K[1, 0] = -self.B[1, 0] * self.K[0, 0] / self.B[1, 1]
+ self.K = numpy.matrix(
+ [[self.K_bottom[0, 0], 0.0, self.K_bottom[0, 1], 0.0],
+ [0.0, self.K_diff[0, 0], 0.0, self.K_diff[0, 1]]])
- lstsq_A = numpy.identity(2)
- lstsq_A[0, :] = self.B[1, :]
- lstsq_A[1, :] = self.B[3, :]
- glog.debug('System of Equations coefficients:')
- glog.debug(str(lstsq_A))
- glog.debug('det %s', str(numpy.linalg.det(lstsq_A)))
+ # Compute the feed forwards aceleration term.
+ self.K[1, 0] = -self.B[1, 0] * self.K[0, 0] / self.B[1, 1]
- out_x = numpy.linalg.lstsq(
- lstsq_A,
- numpy.matrix([[self.A[1, 2]], [self.A[3, 2]]]))[0]
- self.K[1, 2] = -lstsq_A[0, 0] * (self.K[0, 2] - out_x[0]) / lstsq_A[0, 1] + out_x[1]
+ lstsq_A = numpy.identity(2)
+ lstsq_A[0, :] = self.B[1, :]
+ lstsq_A[1, :] = self.B[3, :]
+ glog.debug('System of Equations coefficients:')
+ glog.debug(str(lstsq_A))
+ glog.debug('det %s', str(numpy.linalg.det(lstsq_A)))
- glog.debug('K unaugmented')
- glog.debug(str(self.K))
- glog.debug('B * K unaugmented')
- glog.debug(str(self.B * self.K))
- F = self.A - self.B * self.K
- glog.debug('A - B * K unaugmented')
- glog.debug(str(F))
- glog.debug('eigenvalues')
- glog.debug(str(numpy.linalg.eig(F)[0]))
+ out_x = numpy.linalg.lstsq(
+ lstsq_A, numpy.matrix([[self.A[1, 2]], [self.A[3, 2]]]))[0]
+ self.K[1, 2] = -lstsq_A[0, 0] * (
+ self.K[0, 2] - out_x[0]) / lstsq_A[0, 1] + out_x[1]
- self.rpl = .09
- self.ipl = 0.030
- self.PlaceObserverPoles([self.rpl + 1j * self.ipl,
- self.rpl + 1j * self.ipl,
- self.rpl - 1j * self.ipl,
- self.rpl - 1j * self.ipl])
+ glog.debug('K unaugmented')
+ glog.debug(str(self.K))
+ glog.debug('B * K unaugmented')
+ glog.debug(str(self.B * self.K))
+ F = self.A - self.B * self.K
+ glog.debug('A - B * K unaugmented')
+ glog.debug(str(F))
+ glog.debug('eigenvalues')
+ glog.debug(str(numpy.linalg.eig(F)[0]))
- # 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], [12.0]])
- self.U_min = numpy.matrix([[-12.0], [-12.0]])
+ self.rpl = .09
+ self.ipl = 0.030
+ self.PlaceObserverPoles([
+ self.rpl + 1j * self.ipl, self.rpl + 1j * self.ipl,
+ self.rpl - 1j * self.ipl, self.rpl - 1j * self.ipl
+ ])
- # For the tests that check the limits, these are (upper, lower) for both
- # claws.
- self.hard_pos_limits = None
- self.pos_limits = None
+ # 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], [12.0]])
+ self.U_min = numpy.matrix([[-12.0], [-12.0]])
- # Compute the steady state velocities for a given applied voltage.
- # The top and bottom of the claw should spin at the same rate if the
- # physics is right.
- X_ss = numpy.matrix([[0], [0], [0.0], [0]])
-
- U = numpy.matrix([[1.0],[1.0]])
- A = self.A
- B = self.B
- #X_ss[2, 0] = X_ss[2, 0] * A[2, 2] + B[2, 0] * U[0, 0]
- X_ss[2, 0] = 1 / (1 - A[2, 2]) * B[2, 0] * U[0, 0]
- #X_ss[3, 0] = X_ss[3, 0] * A[3, 3] + X_ss[2, 0] * A[3, 2] + B[3, 0] * U[0, 0] + B[3, 1] * U[1, 0]
- #X_ss[3, 0] * (1 - A[3, 3]) = X_ss[2, 0] * A[3, 2] + B[3, 0] * U[0, 0] + B[3, 1] * U[1, 0]
- X_ss[3, 0] = 1 / (1 - A[3, 3]) * (X_ss[2, 0] * A[3, 2] + B[3, 1] * U[1, 0] + B[3, 0] * U[0, 0])
- #X_ss[3, 0] = 1 / (1 - A[3, 3]) / (1 - A[2, 2]) * B[2, 0] * U[0, 0] * A[3, 2] + B[3, 0] * U[0, 0] + B[3, 1] * U[1, 0]
- X_ss[0, 0] = A[0, 2] * X_ss[2, 0] + B[0, 0] * U[0, 0]
- X_ss[1, 0] = A[1, 2] * X_ss[2, 0] + A[1, 3] * X_ss[3, 0] + B[1, 0] * U[0, 0] + B[1, 1] * U[1, 0]
+ # For the tests that check the limits, these are (upper, lower) for both
+ # claws.
+ self.hard_pos_limits = None
+ self.pos_limits = None
- glog.debug('X_ss %s', str(X_ss))
+ # Compute the steady state velocities for a given applied voltage.
+ # The top and bottom of the claw should spin at the same rate if the
+ # physics is right.
+ X_ss = numpy.matrix([[0], [0], [0.0], [0]])
- self.InitializeState()
+ U = numpy.matrix([[1.0], [1.0]])
+ A = self.A
+ B = self.B
+ #X_ss[2, 0] = X_ss[2, 0] * A[2, 2] + B[2, 0] * U[0, 0]
+ X_ss[2, 0] = 1 / (1 - A[2, 2]) * B[2, 0] * U[0, 0]
+ #X_ss[3, 0] = X_ss[3, 0] * A[3, 3] + X_ss[2, 0] * A[3, 2] + B[3, 0] * U[0, 0] + B[3, 1] * U[1, 0]
+ #X_ss[3, 0] * (1 - A[3, 3]) = X_ss[2, 0] * A[3, 2] + B[3, 0] * U[0, 0] + B[3, 1] * U[1, 0]
+ X_ss[3, 0] = 1 / (1 - A[3, 3]) * (
+ X_ss[2, 0] * A[3, 2] + B[3, 1] * U[1, 0] + B[3, 0] * U[0, 0])
+ #X_ss[3, 0] = 1 / (1 - A[3, 3]) / (1 - A[2, 2]) * B[2, 0] * U[0, 0] * A[3, 2] + B[3, 0] * U[0, 0] + B[3, 1] * U[1, 0]
+ X_ss[0, 0] = A[0, 2] * X_ss[2, 0] + B[0, 0] * U[0, 0]
+ X_ss[1, 0] = (A[1, 2] * X_ss[2, 0]) + (A[1, 3] * X_ss[3, 0]) + (
+ B[1, 0] * U[0, 0]) + (B[1, 1] * U[1, 0])
+
+ glog.debug('X_ss %s', str(X_ss))
+
+ self.InitializeState()
class ClawDeltaU(Claw):
- def __init__(self, name="Claw"):
- super(ClawDeltaU, self).__init__(name)
- A_unaugmented = self.A
- B_unaugmented = self.B
- C_unaugmented = self.C
- self.A = numpy.matrix([[0.0, 0.0, 0.0, 0.0, 0.0],
- [0.0, 0.0, 0.0, 0.0, 0.0],
- [0.0, 0.0, 0.0, 0.0, 0.0],
- [0.0, 0.0, 0.0, 0.0, 0.0],
- [0.0, 0.0, 0.0, 0.0, 1.0]])
- self.A[0:4, 0:4] = A_unaugmented
- self.A[0:4, 4] = B_unaugmented[0:4, 0]
+ def __init__(self, name="Claw"):
+ super(ClawDeltaU, self).__init__(name)
+ A_unaugmented = self.A
+ B_unaugmented = self.B
+ C_unaugmented = self.C
- self.B = numpy.matrix([[0.0, 0.0],
- [0.0, 0.0],
- [0.0, 0.0],
- [0.0, 0.0],
- [1.0, 0.0]])
- self.B[0:4, 1] = B_unaugmented[0:4, 1]
+ self.A = numpy.matrix([[0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.0, 1.0]])
+ self.A[0:4, 0:4] = A_unaugmented
+ self.A[0:4, 4] = B_unaugmented[0:4, 0]
- self.C = numpy.concatenate((C_unaugmented, numpy.matrix([[0.0], [0.0]])),
- axis=1)
- self.D = numpy.matrix([[0.0, 0.0],
- [0.0, 0.0]])
+ self.B = numpy.matrix([[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0],
+ [1.0, 0.0]])
+ self.B[0:4, 1] = B_unaugmented[0:4, 1]
- #self.PlaceControllerPoles([0.55, 0.35, 0.55, 0.35, 0.80])
- self.Q = numpy.matrix([[(1.0 / (0.04 ** 2.0)), 0.0, 0.0, 0.0, 0.0],
- [0.0, (1.0 / (0.01 ** 2)), 0.0, 0.0, 0.0],
- [0.0, 0.0, 0.01, 0.0, 0.0],
- [0.0, 0.0, 0.0, 0.08, 0.0],
- [0.0, 0.0, 0.0, 0.0, (1.0 / (10.0 ** 2))]])
+ self.C = numpy.concatenate(
+ (C_unaugmented, numpy.matrix([[0.0], [0.0]])), axis=1)
+ self.D = numpy.matrix([[0.0, 0.0], [0.0, 0.0]])
- self.R = numpy.matrix([[0.000001, 0.0],
- [0.0, 1.0 / (10.0 ** 2.0)]])
- self.K = controls.dlqr(self.A, self.B, self.Q, self.R)
+ #self.PlaceControllerPoles([0.55, 0.35, 0.55, 0.35, 0.80])
+ self.Q = numpy.matrix([[(1.0 / (0.04**2.0)), 0.0, 0.0, 0.0, 0.0],
+ [0.0, (1.0 / (0.01**2)), 0.0, 0.0, 0.0],
+ [0.0, 0.0, 0.01, 0.0, 0.0],
+ [0.0, 0.0, 0.0, 0.08, 0.0],
+ [0.0, 0.0, 0.0, 0.0, (1.0 / (10.0**2))]])
- self.K = numpy.matrix([[50.0, 0.0, 10.0, 0.0, 1.0],
- [50.0, 0.0, 10.0, 0.0, 1.0]])
+ self.R = numpy.matrix([[0.000001, 0.0], [0.0, 1.0 / (10.0**2.0)]])
+ self.K = controls.dlqr(self.A, self.B, self.Q, self.R)
- controllability = controls.ctrb(self.A, self.B)
- glog.debug('Rank of augmented controllability matrix: %d',
- numpy.linalg.matrix_rank(controllability))
+ self.K = numpy.matrix([[50.0, 0.0, 10.0, 0.0, 1.0],
+ [50.0, 0.0, 10.0, 0.0, 1.0]])
- glog.debug('K')
- glog.debug(str(self.K))
- glog.debug('Placed controller poles are')
- glog.debug(str(numpy.linalg.eig(self.A - self.B * self.K)[0]))
- glog.debug(str([numpy.abs(x) for x in
- numpy.linalg.eig(self.A - self.B * self.K)[0]]))
+ controllability = controls.ctrb(self.A, self.B)
+ glog.debug('Rank of augmented controllability matrix: %d',
+ numpy.linalg.matrix_rank(controllability))
- self.rpl = .05
- self.ipl = 0.008
- self.PlaceObserverPoles([self.rpl + 1j * self.ipl, 0.10, 0.09,
- self.rpl - 1j * self.ipl, 0.90])
- #print "A is"
- #print self.A
- #print "L is"
- #print self.L
- #print "C is"
- #print self.C
- #print "A - LC is"
- #print self.A - self.L * self.C
+ glog.debug('K')
+ glog.debug(str(self.K))
+ glog.debug('Placed controller poles are')
+ glog.debug(str(numpy.linalg.eig(self.A - self.B * self.K)[0]))
+ glog.debug(
+ str([
+ numpy.abs(x)
+ for x in numpy.linalg.eig(self.A - self.B * self.K)[0]
+ ]))
- #print "Placed observer poles are"
- #print numpy.linalg.eig(self.A - self.L * self.C)[0]
+ self.rpl = .05
+ self.ipl = 0.008
+ self.PlaceObserverPoles([
+ self.rpl + 1j * self.ipl, 0.10, 0.09, self.rpl - 1j * self.ipl, 0.90
+ ])
+ #print "A is"
+ #print self.A
+ #print "L is"
+ #print self.L
+ #print "C is"
+ #print self.C
+ #print "A - LC is"
+ #print self.A - self.L * self.C
- self.U_max = numpy.matrix([[12.0], [12.0]])
- self.U_min = numpy.matrix([[-12.0], [-12.0]])
+ #print "Placed observer poles are"
+ #print numpy.linalg.eig(self.A - self.L * self.C)[0]
- self.InitializeState()
+ self.U_max = numpy.matrix([[12.0], [12.0]])
+ self.U_min = numpy.matrix([[-12.0], [-12.0]])
+
+ self.InitializeState()
+
def ScaleU(claw, U, K, error):
- """Clips U as necessary.
+ """Clips U as necessary.
Args:
- claw: claw object containing moments of inertia and U limits.
- U: Input matrix to clip as necessary.
- """
+ claw: claw object containing moments of inertia and U limits.
+ U: Input matrix to clip as necessary.
+ """
- bottom_u = U[0, 0]
- top_u = U[1, 0]
- position_error = error[0:2, 0]
- velocity_error = error[2:, 0]
+ bottom_u = U[0, 0]
+ top_u = U[1, 0]
+ position_error = error[0:2, 0]
+ velocity_error = error[2:, 0]
- U_poly = polytope.HPolytope(
- numpy.matrix([[1, 0],
- [-1, 0],
- [0, 1],
- [0, -1]]),
- numpy.matrix([[12],
- [12],
- [12],
- [12]]))
+ U_poly = polytope.HPolytope(
+ numpy.matrix([[1, 0], [-1, 0], [0, 1], [0, -1]]),
+ numpy.matrix([[12], [12], [12], [12]]))
- if (bottom_u > claw.U_max[0, 0] or bottom_u < claw.U_min[0, 0] or
- top_u > claw.U_max[0, 0] or top_u < claw.U_min[0, 0]):
+ if (bottom_u > claw.U_max[0, 0] or bottom_u < claw.U_min[0, 0] or
+ top_u > claw.U_max[0, 0] or top_u < claw.U_min[0, 0]):
- position_K = K[:, 0:2]
- velocity_K = K[:, 2:]
+ position_K = K[:, 0:2]
+ velocity_K = K[:, 2:]
- # H * U <= k
- # U = UPos + UVel
- # H * (UPos + UVel) <= k
- # H * UPos <= k - H * UVel
- #
- # Now, we can do a coordinate transformation and say the following.
- #
- # UPos = position_K * position_error
- # (H * position_K) * position_error <= k - H * UVel
- #
- # Add in the constraint that 0 <= t <= 1
- # Now, there are 2 ways this can go. Either we have a region, or we don't
- # have a region. If we have a region, then pick the largest t and go for it.
- # If we don't have a region, we need to pick a good comprimise.
+ # H * U <= k
+ # U = UPos + UVel
+ # H * (UPos + UVel) <= k
+ # H * UPos <= k - H * UVel
+ #
+ # Now, we can do a coordinate transformation and say the following.
+ #
+ # UPos = position_K * position_error
+ # (H * position_K) * position_error <= k - H * UVel
+ #
+ # Add in the constraint that 0 <= t <= 1
+ # Now, there are 2 ways this can go. Either we have a region, or we don't
+ # have a region. If we have a region, then pick the largest t and go for it.
+ # If we don't have a region, we need to pick a good comprimise.
- pos_poly = polytope.HPolytope(
- U_poly.H * position_K,
- U_poly.k - U_poly.H * velocity_K * velocity_error)
+ pos_poly = polytope.HPolytope(
+ U_poly.H * position_K,
+ U_poly.k - U_poly.H * velocity_K * velocity_error)
- # The actual angle for the line we call 45.
- angle_45 = numpy.matrix([[numpy.sqrt(3), 1]])
- if claw.pos_limits and claw.hard_pos_limits and claw.X[0, 0] + claw.X[1, 0] > claw.pos_limits[1]:
- angle_45 = numpy.matrix([[1, 1]])
+ # The actual angle for the line we call 45.
+ angle_45 = numpy.matrix([[numpy.sqrt(3), 1]])
+ if claw.pos_limits and claw.hard_pos_limits and (
+ claw.X[0, 0] + claw.X[1, 0]) > claw.pos_limits[1]:
+ angle_45 = numpy.matrix([[1, 1]])
- P = position_error
- L45 = numpy.multiply(numpy.matrix([[numpy.sign(P[1, 0]), -numpy.sign(P[0, 0])]]), angle_45)
- if L45[0, 1] == 0:
- L45[0, 1] = 1
- if L45[0, 0] == 0:
- L45[0, 0] = 1
- w45 = numpy.matrix([[0]])
+ P = position_error
+ L45 = numpy.multiply(
+ numpy.matrix([[numpy.sign(P[1, 0]), -numpy.sign(P[0, 0])]]),
+ angle_45)
+ if L45[0, 1] == 0:
+ L45[0, 1] = 1
+ if L45[0, 0] == 0:
+ L45[0, 0] = 1
+ w45 = numpy.matrix([[0]])
- if numpy.abs(P[0, 0]) > numpy.abs(P[1, 0]):
- LH = numpy.matrix([[0, 1]])
- else:
- LH = numpy.matrix([[1, 0]])
- wh = LH * P
- standard = numpy.concatenate((L45, LH))
- W = numpy.concatenate((w45, wh))
- intersection = numpy.linalg.inv(standard) * W
- adjusted_pos_error_h, is_inside_h = polydrivetrain.DoCoerceGoal(pos_poly,
- LH, wh, position_error)
- adjusted_pos_error_45, is_inside_45 = polydrivetrain.DoCoerceGoal(pos_poly,
- L45, w45, intersection)
- if pos_poly.IsInside(intersection):
- adjusted_pos_error = adjusted_pos_error_h
- else:
- if is_inside_h:
- if numpy.linalg.norm(adjusted_pos_error_h) > numpy.linalg.norm(adjusted_pos_error_45):
- adjusted_pos_error = adjusted_pos_error_h
+ if numpy.abs(P[0, 0]) > numpy.abs(P[1, 0]):
+ LH = numpy.matrix([[0, 1]])
else:
- adjusted_pos_error = adjusted_pos_error_45
- else:
- adjusted_pos_error = adjusted_pos_error_45
- #print adjusted_pos_error
+ LH = numpy.matrix([[1, 0]])
+ wh = LH * P
+ standard = numpy.concatenate((L45, LH))
+ W = numpy.concatenate((w45, wh))
+ intersection = numpy.linalg.inv(standard) * W
+ adjusted_pos_error_h, is_inside_h = polydrivetrain.DoCoerceGoal(
+ pos_poly, LH, wh, position_error)
+ adjusted_pos_error_45, is_inside_45 = polydrivetrain.DoCoerceGoal(
+ pos_poly, L45, w45, intersection)
+ if pos_poly.IsInside(intersection):
+ adjusted_pos_error = adjusted_pos_error_h
+ else:
+ if is_inside_h:
+ if numpy.linalg.norm(adjusted_pos_error_h) > numpy.linalg.norm(
+ adjusted_pos_error_45):
+ adjusted_pos_error = adjusted_pos_error_h
+ else:
+ adjusted_pos_error = adjusted_pos_error_45
+ else:
+ adjusted_pos_error = adjusted_pos_error_45
+ #print adjusted_pos_error
- #print "Actual power is ", velocity_K * velocity_error + position_K * adjusted_pos_error
- return velocity_K * velocity_error + position_K * adjusted_pos_error
+ #print "Actual power is ", velocity_K * velocity_error + position_K * adjusted_pos_error
+ return velocity_K * velocity_error + position_K * adjusted_pos_error
- #U = Kpos * poserror + Kvel * velerror
-
- #scalar = claw.U_max[0, 0] / max(numpy.abs(top_u), numpy.abs(bottom_u))
+ #U = Kpos * poserror + Kvel * velerror
- #top_u *= scalar
- #bottom_u *= scalar
+ #scalar = claw.U_max[0, 0] / max(numpy.abs(top_u), numpy.abs(bottom_u))
- return numpy.matrix([[bottom_u], [top_u]])
+ #top_u *= scalar
+ #bottom_u *= scalar
-def run_test(claw, initial_X, goal, max_separation_error=0.01, show_graph=True, iterations=200):
- """Runs the claw plant on a given claw (claw) with an initial condition (initial_X) and goal (goal).
+ return numpy.matrix([[bottom_u], [top_u]])
+
+
+def run_test(claw,
+ initial_X,
+ goal,
+ max_separation_error=0.01,
+ show_graph=True,
+ iterations=200):
+ """Runs the claw plant on a given claw (claw) with an initial condition (initial_X) and goal (goal).
The tests themselves are not terribly sophisticated; I just test for
whether the goal has been reached and whether the separation goes
outside of the initial and goal values by more than max_separation_error.
Prints out something for a failure of either condition and returns
False if tests fail.
+
Args:
- claw: claw object to use.
- initial_X: starting state.
- goal: goal state.
- show_graph: Whether or not to display a graph showing the changing
- states and voltages.
- iterations: Number of timesteps to run the model for."""
+ claw: claw object to use.
+ initial_X: starting state.
+ goal: goal state.
+ show_graph: Whether or not to display a graph showing the changing
+ states and voltages.
+ iterations: Number of timesteps to run the model for."""
- claw.X = initial_X
+ claw.X = initial_X
- # Various lists for graphing things.
- t = []
- x_bottom = []
- x_top = []
- u_bottom = []
- u_top = []
- x_separation = []
+ # Various lists for graphing things.
+ t = []
+ x_bottom = []
+ x_top = []
+ u_bottom = []
+ u_top = []
+ x_separation = []
- tests_passed = True
+ tests_passed = True
- # Bounds which separation should not exceed.
- lower_bound = (initial_X[1, 0] if initial_X[1, 0] < goal[1, 0]
- else goal[1, 0]) - max_separation_error
- upper_bound = (initial_X[1, 0] if initial_X[1, 0] > goal[1, 0]
- else goal[1, 0]) + max_separation_error
+ # Bounds which separation should not exceed.
+ lower_bound = (initial_X[1, 0] if initial_X[1, 0] < goal[1, 0] else
+ goal[1, 0]) - max_separation_error
+ upper_bound = (initial_X[1, 0] if initial_X[1, 0] > goal[1, 0] else
+ goal[1, 0]) + max_separation_error
- for i in xrange(iterations):
- U = claw.K * (goal - claw.X)
- U = ScaleU(claw, U, claw.K, goal - claw.X)
- claw.Update(U)
+ for i in xrange(iterations):
+ U = claw.K * (goal - claw.X)
+ U = ScaleU(claw, U, claw.K, goal - claw.X)
+ claw.Update(U)
- if claw.X[1, 0] > upper_bound or claw.X[1, 0] < lower_bound:
- tests_passed = False
- glog.info('Claw separation was %f', claw.X[1, 0])
- glog.info("Should have been between", lower_bound, "and", upper_bound)
+ if claw.X[1, 0] > upper_bound or claw.X[1, 0] < lower_bound:
+ tests_passed = False
+ glog.info('Claw separation was %f', claw.X[1, 0])
+ glog.info("Should have been between", lower_bound, "and",
+ upper_bound)
- if claw.hard_pos_limits and \
- (claw.X[0, 0] > claw.hard_pos_limits[1] or
- claw.X[0, 0] < claw.hard_pos_limits[0] or
- claw.X[0, 0] + claw.X[1, 0] > claw.hard_pos_limits[1] or
- claw.X[0, 0] + claw.X[1, 0] < claw.hard_pos_limits[0]):
- tests_passed = False
- glog.info('Claws at %f and %f', claw.X[0, 0], claw.X[0, 0] + claw.X[1, 0])
- glog.info("Both should be in %s, definitely %s",
- claw.pos_limits, claw.hard_pos_limits)
+ if claw.hard_pos_limits and \
+ (claw.X[0, 0] > claw.hard_pos_limits[1] or
+ claw.X[0, 0] < claw.hard_pos_limits[0] or
+ claw.X[0, 0] + claw.X[1, 0] > claw.hard_pos_limits[1] or
+ claw.X[0, 0] + claw.X[1, 0] < claw.hard_pos_limits[0]):
+ tests_passed = False
+ glog.info('Claws at %f and %f', claw.X[0, 0],
+ claw.X[0, 0] + claw.X[1, 0])
+ glog.info("Both should be in %s, definitely %s", claw.pos_limits,
+ claw.hard_pos_limits)
- t.append(i * claw.dt)
- x_bottom.append(claw.X[0, 0] * 10.0)
- x_top.append((claw.X[1, 0] + claw.X[0, 0]) * 10.0)
- u_bottom.append(U[0, 0])
- u_top.append(U[1, 0])
- x_separation.append(claw.X[1, 0] * 10.0)
+ t.append(i * claw.dt)
+ x_bottom.append(claw.X[0, 0] * 10.0)
+ x_top.append((claw.X[1, 0] + claw.X[0, 0]) * 10.0)
+ u_bottom.append(U[0, 0])
+ u_top.append(U[1, 0])
+ x_separation.append(claw.X[1, 0] * 10.0)
- if show_graph:
- pylab.plot(t, x_bottom, label='x bottom * 10')
- pylab.plot(t, x_top, label='x top * 10')
- pylab.plot(t, u_bottom, label='u bottom')
- pylab.plot(t, u_top, label='u top')
- pylab.plot(t, x_separation, label='separation * 10')
- pylab.legend()
- pylab.show()
+ if show_graph:
+ pylab.plot(t, x_bottom, label='x bottom * 10')
+ pylab.plot(t, x_top, label='x top * 10')
+ pylab.plot(t, u_bottom, label='u bottom')
+ pylab.plot(t, u_top, label='u top')
+ pylab.plot(t, x_separation, label='separation * 10')
+ pylab.legend()
+ pylab.show()
- # Test to make sure that we are near the goal.
- if numpy.max(abs(claw.X - goal)) > 1e-4:
- tests_passed = False
- glog.error('X was %s Expected %s', str(claw.X), str(goal))
+ # Test to make sure that we are near the goal.
+ if numpy.max(abs(claw.X - goal)) > 1e-4:
+ tests_passed = False
+ glog.error('X was %s Expected %s', str(claw.X), str(goal))
- return tests_passed
+ return tests_passed
+
def main(argv):
- argv = FLAGS(argv)
+ argv = FLAGS(argv)
- claw = Claw()
- if FLAGS.plot:
- # Test moving the claw with constant separation.
- initial_X = numpy.matrix([[-1.0], [0.0], [0.0], [0.0]])
- R = numpy.matrix([[1.0], [0.0], [0.0], [0.0]])
- run_test(claw, initial_X, R)
+ claw = Claw()
+ if FLAGS.plot:
+ # Test moving the claw with constant separation.
+ initial_X = numpy.matrix([[-1.0], [0.0], [0.0], [0.0]])
+ R = numpy.matrix([[1.0], [0.0], [0.0], [0.0]])
+ run_test(claw, initial_X, R)
- # Test just changing separation.
- initial_X = numpy.matrix([[0.0], [0.0], [0.0], [0.0]])
- R = numpy.matrix([[0.0], [1.0], [0.0], [0.0]])
- run_test(claw, initial_X, R)
+ # Test just changing separation.
+ initial_X = numpy.matrix([[0.0], [0.0], [0.0], [0.0]])
+ R = numpy.matrix([[0.0], [1.0], [0.0], [0.0]])
+ run_test(claw, initial_X, R)
- # Test changing both separation and position at once.
- initial_X = numpy.matrix([[0.0], [0.0], [0.0], [0.0]])
- R = numpy.matrix([[1.0], [1.0], [0.0], [0.0]])
- run_test(claw, initial_X, R)
+ # Test changing both separation and position at once.
+ initial_X = numpy.matrix([[0.0], [0.0], [0.0], [0.0]])
+ R = numpy.matrix([[1.0], [1.0], [0.0], [0.0]])
+ run_test(claw, initial_X, R)
- # Test a small separation error and a large position one.
- initial_X = numpy.matrix([[0.0], [0.0], [0.0], [0.0]])
- R = numpy.matrix([[2.0], [0.05], [0.0], [0.0]])
- run_test(claw, initial_X, R)
+ # Test a small separation error and a large position one.
+ initial_X = numpy.matrix([[0.0], [0.0], [0.0], [0.0]])
+ R = numpy.matrix([[2.0], [0.05], [0.0], [0.0]])
+ run_test(claw, initial_X, R)
- # Test a small separation error and a large position one.
- initial_X = numpy.matrix([[0.0], [0.0], [0.0], [0.0]])
- R = numpy.matrix([[-0.5], [1.0], [0.0], [0.0]])
- run_test(claw, initial_X, R)
+ # Test a small separation error and a large position one.
+ initial_X = numpy.matrix([[0.0], [0.0], [0.0], [0.0]])
+ R = numpy.matrix([[-0.5], [1.0], [0.0], [0.0]])
+ run_test(claw, initial_X, R)
- # Test opening with the top claw at the limit.
- initial_X = numpy.matrix([[0.0], [0.0], [0.0], [0.0]])
- R = numpy.matrix([[-1.5], [1.5], [0.0], [0.0]])
- claw.hard_pos_limits = (-1.6, 0.1)
- claw.pos_limits = (-1.5, 0.0)
- run_test(claw, initial_X, R)
- claw.pos_limits = None
+ # Test opening with the top claw at the limit.
+ initial_X = numpy.matrix([[0.0], [0.0], [0.0], [0.0]])
+ R = numpy.matrix([[-1.5], [1.5], [0.0], [0.0]])
+ claw.hard_pos_limits = (-1.6, 0.1)
+ claw.pos_limits = (-1.5, 0.0)
+ run_test(claw, initial_X, R)
+ claw.pos_limits = None
- # Test opening with the bottom claw at the limit.
- initial_X = numpy.matrix([[0.0], [0.0], [0.0], [0.0]])
- R = numpy.matrix([[0], [1.5], [0.0], [0.0]])
- claw.hard_pos_limits = (-0.1, 1.6)
- claw.pos_limits = (0.0, 1.6)
- run_test(claw, initial_X, R)
- claw.pos_limits = None
+ # Test opening with the bottom claw at the limit.
+ initial_X = numpy.matrix([[0.0], [0.0], [0.0], [0.0]])
+ R = numpy.matrix([[0], [1.5], [0.0], [0.0]])
+ claw.hard_pos_limits = (-0.1, 1.6)
+ claw.pos_limits = (0.0, 1.6)
+ run_test(claw, initial_X, R)
+ claw.pos_limits = None
- # Write the generated constants out to a file.
- if len(argv) != 3:
- glog.fatal('Expected .h file name and .cc file name for the claw.')
- else:
- namespaces = ['y2014', 'control_loops', 'claw']
- claw = Claw('Claw')
- loop_writer = control_loop.ControlLoopWriter('Claw', [claw],
- namespaces=namespaces)
- loop_writer.AddConstant(control_loop.Constant('kClawMomentOfInertiaRatio',
- '%f', claw.J_top / claw.J_bottom))
- loop_writer.AddConstant(control_loop.Constant('kDt', '%f',
- claw.dt))
- loop_writer.Write(argv[1], argv[2])
+ # Write the generated constants out to a file.
+ if len(argv) != 3:
+ glog.fatal('Expected .h file name and .cc file name for the claw.')
+ else:
+ namespaces = ['y2014', 'control_loops', 'claw']
+ claw = Claw('Claw')
+ loop_writer = control_loop.ControlLoopWriter(
+ 'Claw', [claw], namespaces=namespaces)
+ loop_writer.AddConstant(
+ control_loop.Constant('kClawMomentOfInertiaRatio', '%f',
+ claw.J_top / claw.J_bottom))
+ loop_writer.AddConstant(control_loop.Constant('kDt', '%f', claw.dt))
+ loop_writer.Write(argv[1], argv[2])
+
if __name__ == '__main__':
- sys.exit(main(sys.argv))
+ sys.exit(main(sys.argv))
diff --git a/y2014/control_loops/python/shooter.py b/y2014/control_loops/python/shooter.py
index 6a6bb3e..9287dae 100755
--- a/y2014/control_loops/python/shooter.py
+++ b/y2014/control_loops/python/shooter.py
@@ -13,272 +13,271 @@
gflags.DEFINE_bool('plot', False, 'If true, plot the loop response.')
+
class SprungShooter(control_loop.ControlLoop):
- def __init__(self, name="RawSprungShooter"):
- super(SprungShooter, self).__init__(name)
- # Stall Torque in N m
- self.stall_torque = .4982
- # Stall Current in Amps
- self.stall_current = 85
- # Free Speed in RPM
- self.free_speed = 19300.0
- # Free Current in Amps
- self.free_current = 1.2
- # Effective mass of the shooter in kg.
- # This rough estimate should about include the effect of the masses
- # of the gears. If this number is too low, the eigen values of self.A
- # will start to become extremely small.
- self.J = 200
- # Resistance of the motor, divided by the number of motors.
- self.R = 12.0 / self.stall_current / 2.0
- # 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
- # Spring constant for the springs, N/m
- self.Ks = 2800.0
- # Maximum extension distance (Distance from the 0 force point on the
- # spring to the latch position.)
- self.max_extension = 0.32385
- # Gear ratio multiplied by radius of final sprocket.
- self.G = 10.0 / 40.0 * 20.0 / 54.0 * 24.0 / 54.0 * 20.0 / 84.0 * 16.0 * (3.0 / 8.0) / (2.0 * numpy.pi) * 0.0254
- # Control loop time step
- self.dt = 0.005
+ def __init__(self, name="RawSprungShooter"):
+ super(SprungShooter, self).__init__(name)
+ # Stall Torque in N m
+ self.stall_torque = .4982
+ # Stall Current in Amps
+ self.stall_current = 85
+ # Free Speed in RPM
+ self.free_speed = 19300.0
+ # Free Current in Amps
+ self.free_current = 1.2
+ # Effective mass of the shooter in kg.
+ # This rough estimate should about include the effect of the masses
+ # of the gears. If this number is too low, the eigen values of self.A
+ # will start to become extremely small.
+ self.J = 200
+ # Resistance of the motor, divided by the number of motors.
+ self.R = 12.0 / self.stall_current / 2.0
+ # 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
+ # Spring constant for the springs, N/m
+ self.Ks = 2800.0
+ # Maximum extension distance (Distance from the 0 force point on the
+ # spring to the latch position.)
+ self.max_extension = 0.32385
+ # Gear ratio multiplied by radius of final sprocket.
+ self.G = 10.0 / 40.0 * 20.0 / 54.0 * 24.0 / 54.0 * 20.0 / 84.0 * 16.0 * (
+ 3.0 / 8.0) / (2.0 * numpy.pi) * 0.0254
- # State feedback matrices
- self.A_continuous = numpy.matrix(
- [[0, 1],
- [-self.Ks / self.J,
- -self.Kt / self.Kv / (self.J * self.G * self.G * self.R)]])
- self.B_continuous = numpy.matrix(
- [[0],
- [self.Kt / (self.J * self.G * self.R)]])
- self.C = numpy.matrix([[1, 0]])
- self.D = numpy.matrix([[0]])
+ # Control loop time step
+ self.dt = 0.005
- self.A, self.B = self.ContinuousToDiscrete(
- self.A_continuous, self.B_continuous, self.dt)
+ # State feedback matrices
+ self.A_continuous = numpy.matrix(
+ [[0, 1],
+ [
+ -self.Ks / self.J,
+ -self.Kt / self.Kv / (self.J * self.G * self.G * self.R)
+ ]])
+ self.B_continuous = numpy.matrix(
+ [[0], [self.Kt / (self.J * self.G * self.R)]])
+ self.C = numpy.matrix([[1, 0]])
+ self.D = numpy.matrix([[0]])
- self.PlaceControllerPoles([0.45, 0.45])
+ self.A, self.B = self.ContinuousToDiscrete(self.A_continuous,
+ self.B_continuous, self.dt)
- self.rpl = .05
- self.ipl = 0.008
- self.PlaceObserverPoles([self.rpl,
- self.rpl])
+ self.PlaceControllerPoles([0.45, 0.45])
- self.U_max = numpy.matrix([[12.0]])
- self.U_min = numpy.matrix([[-12.0]])
+ self.rpl = .05
+ self.ipl = 0.008
+ self.PlaceObserverPoles([self.rpl, self.rpl])
- self.InitializeState()
+ self.U_max = numpy.matrix([[12.0]])
+ self.U_min = numpy.matrix([[-12.0]])
+
+ self.InitializeState()
class Shooter(SprungShooter):
- def __init__(self, name="RawShooter"):
- super(Shooter, self).__init__(name)
- # State feedback matrices
- self.A_continuous = numpy.matrix(
- [[0, 1],
- [0, -self.Kt / self.Kv / (self.J * self.G * self.G * self.R)]])
- self.B_continuous = numpy.matrix(
- [[0],
- [self.Kt / (self.J * self.G * self.R)]])
+ def __init__(self, name="RawShooter"):
+ super(Shooter, self).__init__(name)
- self.A, self.B = self.ContinuousToDiscrete(
- self.A_continuous, self.B_continuous, self.dt)
+ # State feedback matrices
+ self.A_continuous = numpy.matrix(
+ [[0, 1],
+ [0, -self.Kt / self.Kv / (self.J * self.G * self.G * self.R)]])
+ self.B_continuous = numpy.matrix(
+ [[0], [self.Kt / (self.J * self.G * self.R)]])
- self.PlaceControllerPoles([0.45, 0.45])
+ self.A, self.B = self.ContinuousToDiscrete(self.A_continuous,
+ self.B_continuous, self.dt)
- self.rpl = .05
- self.ipl = 0.008
- self.PlaceObserverPoles([self.rpl,
- self.rpl])
+ self.PlaceControllerPoles([0.45, 0.45])
- self.U_max = numpy.matrix([[12.0]])
- self.U_min = numpy.matrix([[-12.0]])
+ self.rpl = .05
+ self.ipl = 0.008
+ self.PlaceObserverPoles([self.rpl, self.rpl])
- self.InitializeState()
+ self.U_max = numpy.matrix([[12.0]])
+ self.U_min = numpy.matrix([[-12.0]])
+
+ self.InitializeState()
class SprungShooterDeltaU(SprungShooter):
- def __init__(self, name="SprungShooter"):
- super(SprungShooterDeltaU, self).__init__(name)
- A_unaugmented = self.A
- B_unaugmented = self.B
- A_continuous_unaugmented = self.A_continuous
- B_continuous_unaugmented = self.B_continuous
+ def __init__(self, name="SprungShooter"):
+ super(SprungShooterDeltaU, self).__init__(name)
+ A_unaugmented = self.A
+ B_unaugmented = self.B
- self.A_continuous = numpy.matrix(numpy.zeros((3, 3)))
- self.A_continuous[0:2, 0:2] = A_continuous_unaugmented
- self.A_continuous[0:2, 2] = B_continuous_unaugmented
+ A_continuous_unaugmented = self.A_continuous
+ B_continuous_unaugmented = self.B_continuous
- self.B_continuous = numpy.matrix(numpy.zeros((3, 1)))
- self.B_continuous[2, 0] = 1.0 / self.dt
+ self.A_continuous = numpy.matrix(numpy.zeros((3, 3)))
+ self.A_continuous[0:2, 0:2] = A_continuous_unaugmented
+ self.A_continuous[0:2, 2] = B_continuous_unaugmented
- self.A = numpy.matrix([[0.0, 0.0, 0.0],
- [0.0, 0.0, 0.0],
- [0.0, 0.0, 1.0]])
- self.A[0:2, 0:2] = A_unaugmented
- self.A[0:2, 2] = B_unaugmented
+ self.B_continuous = numpy.matrix(numpy.zeros((3, 1)))
+ self.B_continuous[2, 0] = 1.0 / self.dt
- self.B = numpy.matrix([[0.0],
- [0.0],
- [1.0]])
+ self.A = numpy.matrix([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0],
+ [0.0, 0.0, 1.0]])
+ self.A[0:2, 0:2] = A_unaugmented
+ self.A[0:2, 2] = B_unaugmented
- self.C = numpy.matrix([[1.0, 0.0, 0.0]])
- self.D = numpy.matrix([[0.0]])
+ self.B = numpy.matrix([[0.0], [0.0], [1.0]])
- self.PlaceControllerPoles([0.50, 0.35, 0.80])
+ self.C = numpy.matrix([[1.0, 0.0, 0.0]])
+ self.D = numpy.matrix([[0.0]])
- glog.debug('K')
- glog.debug(str(self.K))
- glog.debug('Placed controller poles are')
- glog.debug(str(numpy.linalg.eig(self.A - self.B * self.K)[0]))
+ self.PlaceControllerPoles([0.50, 0.35, 0.80])
- self.rpl = .05
- self.ipl = 0.008
- self.PlaceObserverPoles([self.rpl + 1j * self.ipl,
- self.rpl - 1j * self.ipl, 0.90])
- glog.debug('Placed observer poles are')
- glog.debug(str(numpy.linalg.eig(self.A - self.L * self.C)[0]))
+ glog.debug('K')
+ glog.debug(str(self.K))
+ glog.debug('Placed controller poles are')
+ glog.debug(str(numpy.linalg.eig(self.A - self.B * self.K)[0]))
- self.U_max = numpy.matrix([[12.0]])
- self.U_min = numpy.matrix([[-12.0]])
+ self.rpl = .05
+ self.ipl = 0.008
+ self.PlaceObserverPoles(
+ [self.rpl + 1j * self.ipl, self.rpl - 1j * self.ipl, 0.90])
+ glog.debug('Placed observer poles are')
+ glog.debug(str(numpy.linalg.eig(self.A - self.L * self.C)[0]))
- self.InitializeState()
+ self.U_max = numpy.matrix([[12.0]])
+ self.U_min = numpy.matrix([[-12.0]])
+
+ self.InitializeState()
class ShooterDeltaU(Shooter):
- def __init__(self, name="Shooter"):
- super(ShooterDeltaU, self).__init__(name)
- A_unaugmented = self.A
- B_unaugmented = self.B
- A_continuous_unaugmented = self.A_continuous
- B_continuous_unaugmented = self.B_continuous
+ def __init__(self, name="Shooter"):
+ super(ShooterDeltaU, self).__init__(name)
+ A_unaugmented = self.A
+ B_unaugmented = self.B
- self.A_continuous = numpy.matrix(numpy.zeros((3, 3)))
- self.A_continuous[0:2, 0:2] = A_continuous_unaugmented
- self.A_continuous[0:2, 2] = B_continuous_unaugmented
+ A_continuous_unaugmented = self.A_continuous
+ B_continuous_unaugmented = self.B_continuous
- self.B_continuous = numpy.matrix(numpy.zeros((3, 1)))
- self.B_continuous[2, 0] = 1.0 / self.dt
+ self.A_continuous = numpy.matrix(numpy.zeros((3, 3)))
+ self.A_continuous[0:2, 0:2] = A_continuous_unaugmented
+ self.A_continuous[0:2, 2] = B_continuous_unaugmented
- self.A = numpy.matrix([[0.0, 0.0, 0.0],
- [0.0, 0.0, 0.0],
- [0.0, 0.0, 1.0]])
- self.A[0:2, 0:2] = A_unaugmented
- self.A[0:2, 2] = B_unaugmented
+ self.B_continuous = numpy.matrix(numpy.zeros((3, 1)))
+ self.B_continuous[2, 0] = 1.0 / self.dt
- self.B = numpy.matrix([[0.0],
- [0.0],
- [1.0]])
+ self.A = numpy.matrix([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0],
+ [0.0, 0.0, 1.0]])
+ self.A[0:2, 0:2] = A_unaugmented
+ self.A[0:2, 2] = B_unaugmented
- self.C = numpy.matrix([[1.0, 0.0, 0.0]])
- self.D = numpy.matrix([[0.0]])
+ self.B = numpy.matrix([[0.0], [0.0], [1.0]])
- self.PlaceControllerPoles([0.55, 0.45, 0.80])
+ self.C = numpy.matrix([[1.0, 0.0, 0.0]])
+ self.D = numpy.matrix([[0.0]])
- glog.debug('K')
- glog.debug(str(self.K))
- glog.debug('Placed controller poles are')
- glog.debug(str(numpy.linalg.eig(self.A - self.B * self.K)[0]))
+ self.PlaceControllerPoles([0.55, 0.45, 0.80])
- self.rpl = .05
- self.ipl = 0.008
- self.PlaceObserverPoles([self.rpl + 1j * self.ipl,
- self.rpl - 1j * self.ipl, 0.90])
- glog.debug('Placed observer poles are')
- glog.debug(str(numpy.linalg.eig(self.A - self.L * self.C)[0]))
+ glog.debug('K')
+ glog.debug(str(self.K))
+ glog.debug('Placed controller poles are')
+ glog.debug(str(numpy.linalg.eig(self.A - self.B * self.K)[0]))
- self.U_max = numpy.matrix([[12.0]])
- self.U_min = numpy.matrix([[-12.0]])
+ self.rpl = .05
+ self.ipl = 0.008
+ self.PlaceObserverPoles(
+ [self.rpl + 1j * self.ipl, self.rpl - 1j * self.ipl, 0.90])
+ glog.debug('Placed observer poles are')
+ glog.debug(str(numpy.linalg.eig(self.A - self.L * self.C)[0]))
- self.InitializeState()
+ self.U_max = numpy.matrix([[12.0]])
+ self.U_min = numpy.matrix([[-12.0]])
+
+ self.InitializeState()
def ClipDeltaU(shooter, old_voltage, delta_u):
- old_u = old_voltage
- new_u = numpy.clip(old_u + delta_u, shooter.U_min, shooter.U_max)
- return new_u - old_u
+ old_u = old_voltage
+ new_u = numpy.clip(old_u + delta_u, shooter.U_min, shooter.U_max)
+ return new_u - old_u
+
def main(argv):
- argv = FLAGS(argv)
+ argv = FLAGS(argv)
- # Simulate the response of the system to a goal.
- sprung_shooter = SprungShooterDeltaU()
- raw_sprung_shooter = SprungShooter()
- close_loop_x = []
- close_loop_u = []
- goal_position = -0.3
- R = numpy.matrix([[goal_position],
- [0.0],
- [-sprung_shooter.A[1, 0] / sprung_shooter.A[1, 2] *
- goal_position]])
- voltage = numpy.matrix([[0.0]])
- for _ in xrange(500):
- U = sprung_shooter.K * (R - sprung_shooter.X_hat)
- U = ClipDeltaU(sprung_shooter, voltage, U)
- sprung_shooter.Y = raw_sprung_shooter.Y + 0.01
- sprung_shooter.UpdateObserver(U)
- voltage += U
- raw_sprung_shooter.Update(voltage)
- close_loop_x.append(raw_sprung_shooter.X[0, 0] * 10)
- close_loop_u.append(voltage[0, 0])
+ # Simulate the response of the system to a goal.
+ sprung_shooter = SprungShooterDeltaU()
+ raw_sprung_shooter = SprungShooter()
+ close_loop_x = []
+ close_loop_u = []
+ goal_position = -0.3
+ R = numpy.matrix(
+ [[goal_position], [0.0],
+ [-sprung_shooter.A[1, 0] / sprung_shooter.A[1, 2] * goal_position]])
+ voltage = numpy.matrix([[0.0]])
+ for _ in xrange(500):
+ U = sprung_shooter.K * (R - sprung_shooter.X_hat)
+ U = ClipDeltaU(sprung_shooter, voltage, U)
+ sprung_shooter.Y = raw_sprung_shooter.Y + 0.01
+ sprung_shooter.UpdateObserver(U)
+ voltage += U
+ raw_sprung_shooter.Update(voltage)
+ close_loop_x.append(raw_sprung_shooter.X[0, 0] * 10)
+ close_loop_u.append(voltage[0, 0])
- if FLAGS.plot:
- pylab.plot(range(500), close_loop_x)
- pylab.plot(range(500), close_loop_u)
- pylab.show()
+ if FLAGS.plot:
+ pylab.plot(range(500), close_loop_x)
+ pylab.plot(range(500), close_loop_u)
+ pylab.show()
- shooter = ShooterDeltaU()
- raw_shooter = Shooter()
- close_loop_x = []
- close_loop_u = []
- goal_position = -0.3
- R = numpy.matrix([[goal_position], [0.0], [-shooter.A[1, 0] / shooter.A[1, 2] * goal_position]])
- voltage = numpy.matrix([[0.0]])
- for _ in xrange(500):
- U = shooter.K * (R - shooter.X_hat)
- U = ClipDeltaU(shooter, voltage, U)
- shooter.Y = raw_shooter.Y + 0.01
- shooter.UpdateObserver(U)
- voltage += U
- raw_shooter.Update(voltage)
- close_loop_x.append(raw_shooter.X[0, 0] * 10)
- close_loop_u.append(voltage[0, 0])
+ shooter = ShooterDeltaU()
+ raw_shooter = Shooter()
+ close_loop_x = []
+ close_loop_u = []
+ goal_position = -0.3
+ R = numpy.matrix([[goal_position], [0.0],
+ [-shooter.A[1, 0] / shooter.A[1, 2] * goal_position]])
+ voltage = numpy.matrix([[0.0]])
+ for _ in xrange(500):
+ U = shooter.K * (R - shooter.X_hat)
+ U = ClipDeltaU(shooter, voltage, U)
+ shooter.Y = raw_shooter.Y + 0.01
+ shooter.UpdateObserver(U)
+ voltage += U
+ raw_shooter.Update(voltage)
+ close_loop_x.append(raw_shooter.X[0, 0] * 10)
+ close_loop_u.append(voltage[0, 0])
- if FLAGS.plot:
- pylab.plot(range(500), close_loop_x)
- pylab.plot(range(500), close_loop_u)
- pylab.show()
+ if FLAGS.plot:
+ pylab.plot(range(500), close_loop_x)
+ pylab.plot(range(500), close_loop_u)
+ pylab.show()
- # Write the generated constants out to a file.
- unaug_sprung_shooter = SprungShooter("RawSprungShooter")
- unaug_shooter = Shooter("RawShooter")
- namespaces = ['y2014', 'control_loops', 'shooter']
- unaug_loop_writer = control_loop.ControlLoopWriter("RawShooter",
- [unaug_sprung_shooter,
- unaug_shooter],
- namespaces=namespaces)
- unaug_loop_writer.Write(argv[4], argv[3])
+ # Write the generated constants out to a file.
+ unaug_sprung_shooter = SprungShooter("RawSprungShooter")
+ unaug_shooter = Shooter("RawShooter")
+ namespaces = ['y2014', 'control_loops', 'shooter']
+ unaug_loop_writer = control_loop.ControlLoopWriter(
+ "RawShooter", [unaug_sprung_shooter, unaug_shooter],
+ namespaces=namespaces)
+ unaug_loop_writer.Write(argv[4], argv[3])
- sprung_shooter = SprungShooterDeltaU()
- shooter = ShooterDeltaU()
- loop_writer = control_loop.ControlLoopWriter("Shooter",
- [sprung_shooter, shooter],
- namespaces=namespaces)
+ sprung_shooter = SprungShooterDeltaU()
+ shooter = ShooterDeltaU()
+ loop_writer = control_loop.ControlLoopWriter(
+ "Shooter", [sprung_shooter, shooter], namespaces=namespaces)
- loop_writer.AddConstant(control_loop.Constant("kMaxExtension", "%f",
- sprung_shooter.max_extension))
- loop_writer.AddConstant(control_loop.Constant("kSpringConstant", "%f",
- sprung_shooter.Ks))
- loop_writer.AddConstant(control_loop.Constant("kDt", "%f",
- sprung_shooter.dt))
- loop_writer.Write(argv[2], argv[1])
+ loop_writer.AddConstant(
+ control_loop.Constant("kMaxExtension", "%f",
+ sprung_shooter.max_extension))
+ loop_writer.AddConstant(
+ control_loop.Constant("kSpringConstant", "%f", sprung_shooter.Ks))
+ loop_writer.AddConstant(
+ control_loop.Constant("kDt", "%f", sprung_shooter.dt))
+ loop_writer.Write(argv[2], argv[1])
+
if __name__ == '__main__':
- sys.exit(main(sys.argv))
+ sys.exit(main(sys.argv))