y2017/control_loops/python/indexer.py

#!/usr/bin/python3

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

try:
    gflags.DEFINE_bool('plot', False, 'If true, plot the loop response.')
except gflags.DuplicateFlagError:
    pass

gflags.DEFINE_bool('stall', False, 'If true, stall the indexer.')


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
        self.free_speed_rpm = 18730.0
        # Free Speed in rotations/second.
        self.free_speed = self.free_speed_rpm / 60.0
        # 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) * (20.0 / 34.0) * (18.0 / 36.0) * (12.0 /
                                                                        84.0)
        self.G_outer = (12.0 / 48.0) * (20.0 / 34.0) * (18.0 / 36.0) * (24.0 /
                                                                        420.0)

        # Motor inertia in kg m^2
        self.motor_inertia = 0.00001187

        # 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('Indexer J is %f', self.J)
        self.G = self.G_inner

        # Resistance of the motor, divided by 2 to account for the 2 motors
        self.resistance = 12.0 / self.stall_current
        # Motor velocity constant
        self.Kv = ((self.free_speed * 2.0 * numpy.pi) /
                   (12.0 - self.resistance * 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.resistance)
        ]])
        self.B_continuous = numpy.matrix(
            [[self.Kt / (self.J * self.G * self.resistance)]])
        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([.75])

        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", voltage_error_noise=None):
        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 = 0.01
        q_vel = 2.0
        q_voltage = 0.6
        if voltage_error_noise is not None:
            q_voltage = voltage_error_noise

        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.stall_ratio = []
        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 range(iterations):
            X_hat = indexer.X

            if observer_indexer is not None:
                X_hat = observer_indexer.X_hat
                observer_indexer.Y = indexer.Y
                observer_indexer.CorrectObserver(numpy.matrix([[0.0]]))
                self.x_hat.append(observer_indexer.X_hat[1, 0])
                self.offset.append(observer_indexer.X_hat[2, 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)

            applied_U = U.copy()
            if i >= 40:
                applied_U -= 2

            if FLAGS.stall and i >= 40:
                indexer.X[1, 0] = 0.0
            else:
                indexer.Update(applied_U)

            if observer_indexer is not None:
                clipped_u = U[0, 0]
                clip_u_value = 3.0
                if clipped_u < 0:
                    clipped_u = min(clipped_u, -clip_u_value)
                else:
                    clipped_u = max(clipped_u, clip_u_value)

                self.stall_ratio.append(10 * (-self.offset[-1] / clipped_u))

                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.plot(self.t, self.stall_ratio, label='stall_ratio')
        pylab.plot(self.t,
                   [10.0 if x > 6.0 else 0.0 for x in self.stall_ratio],
                   label='is_stalled')
        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()

    scenario_plotter = ScenarioPlotter()

    indexer = Indexer()
    indexer_controller = IntegralIndexer(voltage_error_noise=1.5)
    observer_indexer = IntegralIndexer(voltage_error_noise=1.5)

    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) != 7:
        glog.fatal('Expected .h file name and .cc file names')
    else:
        namespaces = ['y2017', 'control_loops', 'superstructure', 'indexer']
        indexer = Indexer('Indexer')
        loop_writer = control_loop.ControlLoopWriter('Indexer', [indexer],
                                                     namespaces=namespaces)
        loop_writer.AddConstant(
            control_loop.Constant('kFreeSpeed', '%f', indexer.free_speed))
        loop_writer.AddConstant(
            control_loop.Constant('kOutputRatio', '%f', indexer.G))
        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])

        stuck_integral_indexer = IntegralIndexer('StuckIntegralIndexer',
                                                 voltage_error_noise=1.5)
        stuck_integral_loop_writer = control_loop.ControlLoopWriter(
            'StuckIntegralIndexer', [stuck_integral_indexer],
            namespaces=namespaces)
        stuck_integral_loop_writer.Write(argv[5], argv[6])


if __name__ == '__main__':
    argv = FLAGS(sys.argv)
    glog.init()
    sys.exit(main(argv))