| #!/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 y2014.control_loops.python import polydrivetrain |
| 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 |
| |
| |
| 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 |
| |
| # 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_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_bottom_cont = numpy.matrix([[0, 1], [0, self.bottom_bottom]]) |
| |
| self.A_diff_cont = numpy.matrix([[0, 1], |
| [0, self.difference_difference]]) |
| |
| 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 |
| ]]) |
| |
| glog.debug('Cont X_ss %f', |
| self.motor_feedforward / -self.common_motor_constant) |
| |
| self.B_bottom_cont = numpy.matrix([[0], |
| [self.motor_feedforward_bottom]]) |
| |
| self.B_diff_cont = numpy.matrix([[0], |
| [self.motor_feedforward_difference]]) |
| |
| self.C = numpy.matrix([[1, 0, 0, 0], [1, 1, 0, 0]]) |
| self.D = numpy.matrix([[0, 0], [0, 0]]) |
| |
| self.A, self.B = self.ContinuousToDiscrete(self.A_continuous, |
| self.B_continuous, self.dt) |
| |
| 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.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('K_diff %s', str(self.K_diff)) |
| glog.debug('K_bottom %s', str(self.K_bottom)) |
| |
| glog.debug('A') |
| glog.debug(self.A) |
| glog.debug('B') |
| glog.debug(self.B) |
| |
| 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.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.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]]]) |
| |
| # Compute the feed forwards aceleration term. |
| self.K[1, 0] = -self.B[1, 0] * self.K[0, 0] / self.B[1, 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))) |
| |
| 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] |
| |
| 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])) |
| |
| 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 |
| ]) |
| |
| # 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]]) |
| |
| # For the tests that check the limits, these are (upper, lower) for both |
| # claws. |
| self.hard_pos_limits = None |
| self.pos_limits = None |
| |
| # 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]) |
| |
| 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] |
| |
| 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.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.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.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.K = numpy.matrix([[50.0, 0.0, 10.0, 0.0, 1.0], |
| [50.0, 0.0, 10.0, 0.0, 1.0]]) |
| |
| controllability = controls.ctrb(self.A, self.B) |
| glog.debug('Rank of augmented controllability matrix: %d', |
| numpy.linalg.matrix_rank(controllability)) |
| |
| 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] |
| ])) |
| |
| 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 |
| |
| #print "Placed observer poles are" |
| #print numpy.linalg.eig(self.A - self.L * self.C)[0] |
| |
| 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. |
| |
| Args: |
| 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] |
| |
| 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]): |
| |
| 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. |
| |
| 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]]) |
| |
| 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 |
| 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 |
| |
| #U = Kpos * poserror + Kvel * velerror |
| |
| #scalar = claw.U_max[0, 0] / max(numpy.abs(top_u), numpy.abs(bottom_u)) |
| |
| #top_u *= scalar |
| #bottom_u *= scalar |
| |
| 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.X = initial_X |
| |
| # Various lists for graphing things. |
| t = [] |
| x_bottom = [] |
| x_top = [] |
| u_bottom = [] |
| u_top = [] |
| x_separation = [] |
| |
| 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 |
| |
| 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.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) |
| |
| 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)) |
| |
| return tests_passed |
| |
| |
| def main(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) |
| |
| # 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 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 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 |
| |
| # 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)) |