| #!/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)) |