Add python code for the superstructure.
Change-Id: Iba34fa2c7d1f17db6bc157bc3c534d53f48cda9c
diff --git a/y2016/control_loops/python/intake.py b/y2016/control_loops/python/intake.py
new file mode 100755
index 0000000..b5e61c1
--- /dev/null
+++ b/y2016/control_loops/python/intake.py
@@ -0,0 +1,287 @@
+#!/usr/bin/python
+
+from frc971.control_loops.python import control_loop
+from frc971.control_loops.python import controls
+from frc971.control_loops.python import polytope
+from y2016.control_loops.python import polydrivetrain
+import numpy
+import sys
+import matplotlib
+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
+
+class Intake(control_loop.ControlLoop):
+ def __init__(self, name="Intake", mass=None):
+ super(Intake, self).__init__(name)
+ # TODO(constants): Update all of these & retune poles.
+ # Stall Torque in N m
+ self.stall_torque = 0.476
+ # Stall Current in Amps
+ self.stall_current = 80.730
+ # Free Speed in RPM
+ self.free_speed = 13906.0
+ # Free Current in Amps
+ self.free_current = 5.820
+ # Mass of the intake
+ if mass is None:
+ self.mass = 5.0
+ else:
+ self.mass = mass
+
+ # 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) /
+ (12.0 - self.R * self.free_current))
+ # Torque constant
+ self.Kt = self.stall_torque / self.stall_current
+ # Gear ratio
+ self.G = (56.0 / 12.0) * (54.0 / 14.0) * (64.0 / 14.0) * (72.0 / 18.0)
+ # Intake length
+ self.r = 18 * 0.0254
+
+ self.J = self.r * self.mass
+
+ # Control loop time step
+ self.dt = 0.005
+
+ # State is [position, velocity]
+ # Input is [Voltage]
+
+ C1 = self.G * self.G * self.Kt / (self.R * self.J * self.Kv)
+ C2 = self.Kt * self.G / (self.J * self.R)
+
+ self.A_continuous = numpy.matrix(
+ [[0, 1],
+ [0, -C1]])
+
+ # Start with the unmodified input
+ self.B_continuous = numpy.matrix(
+ [[0],
+ [C2]])
+
+ 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)
+
+ controllability = controls.ctrb(self.A, self.B)
+
+ print "Free speed is", self.free_speed * numpy.pi * 2.0 / 60.0 / self.G
+
+ q_pos = 0.15
+ q_vel = 2.5
+ self.Q = numpy.matrix([[(1.0 / (q_pos ** 2.0)), 0.0],
+ [0.0, (1.0 / (q_vel ** 2.0))]])
+
+ self.R = numpy.matrix([[(1.0 / (12.0 ** 2.0))]])
+ self.K = controls.dlqr(self.A, self.B, self.Q, self.R)
+
+ print 'K', self.K
+ print 'Poles are', numpy.linalg.eig(self.A - self.B * self.K)[0]
+
+ self.rpl = 0.30
+ self.ipl = 0.10
+ self.PlaceObserverPoles([self.rpl + 1j * self.ipl,
+ self.rpl - 1j * self.ipl])
+
+ print 'L is', self.L
+
+ q_pos = 0.05
+ q_vel = 2.65
+ self.Q = numpy.matrix([[(q_pos ** 2.0), 0.0],
+ [0.0, (q_vel ** 2.0)]])
+
+ r_volts = 0.025
+ self.R = numpy.matrix([[(r_volts ** 2.0)]])
+
+ self.KalmanGain, self.Q_steady = controls.kalman(
+ A=self.A, B=self.B, C=self.C, Q=self.Q, R=self.R)
+
+ print 'Kal', self.KalmanGain
+ self.L = self.A * self.KalmanGain
+ print 'KalL is', self.L
+
+ # 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 IntegralIntake(Intake):
+ def __init__(self, name="IntegralIntake", mass=None):
+ super(IntegralIntake, self).__init__(name=name, mass=mass)
+
+ 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 = 0.08
+ q_vel = 4.00
+ q_voltage = 6.0
+ 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 = 0.05
+ 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.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 = []
+
+ def run_test(self, intake, goal, iterations=200, controller_intake=None,
+ observer_intake=None):
+ """Runs the intake plant with an initial condition and goal.
+
+ 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:
+ intake: intake object to use.
+ goal: goal state.
+ iterations: Number of timesteps to run the model for.
+ controller_intake: Intake object to get K from, or None if we should
+ use intake.
+ observer_intake: Intake object to use for the observer, or None if we should
+ use the actual state.
+ """
+
+ if controller_intake is None:
+ controller_intake = intake
+
+ vbat = 12.0
+
+ if self.t:
+ initial_t = self.t[-1] + intake.dt
+ else:
+ initial_t = 0
+
+ for i in xrange(iterations):
+ X_hat = intake.X
+
+ if observer_intake is not None:
+ X_hat = observer_intake.X_hat
+ self.x_hat.append(observer_intake.X_hat[0, 0])
+
+ U = controller_intake.K * (goal - X_hat)
+ U[0, 0] = numpy.clip(U[0, 0], -vbat, vbat)
+ self.x.append(intake.X[0, 0])
+
+ if self.v:
+ last_v = self.v[-1]
+ else:
+ last_v = 0
+
+ self.v.append(intake.X[1, 0])
+ self.a.append((self.v[-1] - last_v) / intake.dt)
+
+ if observer_intake is not None:
+ observer_intake.Y = intake.Y
+ observer_intake.CorrectObserver(U)
+
+ intake.Update(U)
+
+ if observer_intake is not None:
+ observer_intake.PredictObserver(U)
+
+ self.t.append(initial_t + i * intake.dt)
+ self.u.append(U[0, 0])
+
+ glog.debug('Time: %f', self.t[-1])
+
+ 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.legend()
+
+ pylab.subplot(3, 1, 2)
+ pylab.plot(self.t, self.u, label='u')
+
+ pylab.subplot(3, 1, 3)
+ pylab.plot(self.t, self.a, label='a')
+
+ pylab.legend()
+ pylab.show()
+
+
+def main(argv):
+ argv = FLAGS(argv)
+
+ base_mass = 4
+ load_mass = 0
+
+ scenario_plotter = ScenarioPlotter()
+
+ intake = Intake(mass=base_mass + load_mass)
+ intake_controller = IntegralIntake(mass=base_mass + load_mass)
+ observer_intake = IntegralIntake(mass=base_mass + load_mass)
+
+ # Test moving the intake with constant separation.
+ initial_X = numpy.matrix([[0.0], [0.0]])
+ R = numpy.matrix([[1.0], [0.0], [0.0]])
+ scenario_plotter.run_test(intake, goal=R, controller_intake=intake_controller,
+ observer_intake=observer_intake, iterations=200)
+
+ 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 intake and integral intake.')
+ else:
+ namespaces = ['y2016', 'control_loops', 'superstructure']
+ intake = Intake("Intake")
+ loop_writer = control_loop.ControlLoopWriter('Intake', [intake],
+ namespaces=namespaces)
+ loop_writer.Write(argv[1], argv[2])
+
+ integral_intake = IntegralIntake("IntegralIntake", mass=base_mass + load_mass)
+ integral_loop_writer = control_loop.ControlLoopWriter("IntegralIntake", [integral_intake],
+ namespaces=['y2016', 'control_loops', 'superstructure'])
+ integral_loop_writer.Write(argv[3], argv[4])
+
+if __name__ == '__main__':
+ sys.exit(main(sys.argv))