Campbell Crowley | 33e0e3d | 2017-12-27 17:55:40 -0800 | [diff] [blame^] | 1 | #!/usr/bin/python |
| 2 | |
| 3 | from frc971.control_loops.python import control_loop |
| 4 | from frc971.control_loops.python import controls |
| 5 | import numpy |
| 6 | import sys |
| 7 | from matplotlib import pylab |
| 8 | import glog |
| 9 | |
| 10 | class DrivetrainParams(object): |
| 11 | def __init__(self, J, mass, robot_radius, wheel_radius, G_high, G_low, |
| 12 | q_pos_low, q_pos_high, q_vel_low, q_vel_high, |
| 13 | motor_type = control_loop.CIM(), num_motors = 2, dt = 0.00505, |
| 14 | controller_poles=[0.90, 0.90], observer_poles=[0.02, 0.02]): |
| 15 | """Defines all constants of a drivetrain. |
| 16 | |
| 17 | Args: |
| 18 | J: float, Moment of inertia of drivetrain in kg m^2 |
| 19 | mass: float, Mass of the robot in kg. |
| 20 | robot_radius: float, Radius of the robot, in meters (requires tuning by |
| 21 | hand). |
| 22 | wheel_radius: float, Radius of the wheels, in meters. |
| 23 | G_high: float, Gear ratio for high gear. |
| 24 | G_low: float, Gear ratio for low gear. |
| 25 | dt: float, Control loop time step. |
| 26 | q_pos_low: float, q position low gear. |
| 27 | q_pos_high: float, q position high gear. |
| 28 | q_vel_low: float, q velocity low gear. |
| 29 | q_vel_high: float, q velocity high gear. |
| 30 | motor_type: object, class of values defining the motor in drivetrain. |
| 31 | num_motors: int, number of motors on one side of drivetrain. |
| 32 | controller_poles: array, An array of poles. (See control_loop.py) |
| 33 | observer_poles: array, An array of poles. (See control_loop.py) |
| 34 | """ |
| 35 | |
| 36 | self.J = J |
| 37 | self.mass = mass |
| 38 | self.robot_radius = robot_radius |
| 39 | self.wheel_radius = wheel_radius |
| 40 | self.G_high = G_high |
| 41 | self.G_low = G_low |
| 42 | self.dt = dt |
| 43 | self.q_pos_low = q_pos_low |
| 44 | self.q_pos_high = q_pos_high |
| 45 | self.q_vel_low = q_vel_low |
| 46 | self.q_vel_high = q_vel_high |
| 47 | self.motor_type = motor_type |
| 48 | self.num_motors = num_motors |
| 49 | self.controller_poles = controller_poles |
| 50 | self.observer_poles = observer_poles |
| 51 | |
| 52 | class Drivetrain(control_loop.ControlLoop): |
| 53 | def __init__(self, drivetrain_params, name="Drivetrain", left_low=True, |
| 54 | right_low=True): |
| 55 | """Defines a base drivetrain for a robot. |
| 56 | |
| 57 | Args: |
| 58 | drivetrain_params: DrivetrainParams, class of values defining the drivetrain. |
| 59 | name: string, Name of this drivetrain. |
| 60 | left_low: bool, Whether the left is in high gear. |
| 61 | right_low: bool, Whether the right is in high gear. |
| 62 | """ |
| 63 | super(Drivetrain, self).__init__(name) |
| 64 | |
| 65 | # Moment of inertia of the drivetrain in kg m^2 |
| 66 | self.J = drivetrain_params.J |
| 67 | # Mass of the robot, in kg. |
| 68 | self.mass = drivetrain_params.mass |
| 69 | # Radius of the robot, in meters (requires tuning by hand) |
| 70 | self.robot_radius = drivetrain_params.robot_radius |
| 71 | # Radius of the wheels, in meters. |
| 72 | self.r = drivetrain_params.wheel_radius |
| 73 | |
| 74 | # Gear ratios |
| 75 | self.G_low = drivetrain_params.G_low |
| 76 | self.G_high = drivetrain_params.G_high |
| 77 | if left_low: |
| 78 | self.Gl = self.G_low |
| 79 | else: |
| 80 | self.Gl = self.G_high |
| 81 | if right_low: |
| 82 | self.Gr = self.G_low |
| 83 | else: |
| 84 | self.Gr = self.G_high |
| 85 | |
| 86 | # Control loop time step |
| 87 | self.dt = drivetrain_params.dt |
| 88 | |
| 89 | self.BuildDrivetrain(drivetrain_params.motor_type, drivetrain_params.num_motors); |
| 90 | |
| 91 | if left_low or right_low: |
| 92 | q_pos = drivetrain_params.q_pos_low |
| 93 | q_vel = drivetrain_params.q_vel_low |
| 94 | else: |
| 95 | q_pos = drivetrain_params.q_pos_high |
| 96 | q_vel = drivetrain_params.q_vel_high |
| 97 | |
| 98 | self.BuildDrivetrainController(q_pos, q_vel) |
| 99 | |
| 100 | self.InitializeState() |
| 101 | |
| 102 | def BuildDrivetrain(self, motor, num_motors_per_side): |
| 103 | self.motor = motor |
| 104 | # Number of motors per side |
| 105 | self.num_motors = num_motors_per_side |
| 106 | # Stall Torque in N m |
| 107 | self.stall_torque = motor.stall_torque * self.num_motors * 0.60 |
| 108 | # Stall Current in Amps |
| 109 | self.stall_current = motor.stall_current * self.num_motors |
| 110 | # Free Speed in rad/s |
| 111 | self.free_speed = motor.free_speed |
| 112 | # Free Current in Amps |
| 113 | self.free_current = motor.free_current * self.num_motors |
| 114 | |
| 115 | # Effective motor resistance in ohms. |
| 116 | self.resistance = 12.0 / self.stall_current |
| 117 | |
| 118 | # Resistance of the motor, divided by the number of motors. |
| 119 | # Motor velocity constant |
| 120 | self.Kv = (self.free_speed / (12.0 - self.resistance * self.free_current)) |
| 121 | # Torque constant |
| 122 | self.Kt = self.stall_torque / self.stall_current |
| 123 | |
| 124 | # These describe the way that a given side of a robot will be influenced |
| 125 | # by the other side. Units of 1 / kg. |
| 126 | self.msp = 1.0 / self.mass + self.robot_radius * self.robot_radius / self.J |
| 127 | self.msn = 1.0 / self.mass - self.robot_radius * self.robot_radius / self.J |
| 128 | # The calculations which we will need for A and B. |
| 129 | self.tcl = self.Kt / self.Kv / (self.Gl * self.Gl * self.resistance * self.r * self.r) |
| 130 | self.tcr = self.Kt / self.Kv / (self.Gr * self.Gr * self.resistance * self.r * self.r) |
| 131 | self.mpl = self.Kt / (self.Gl * self.resistance * self.r) |
| 132 | self.mpr = self.Kt / (self.Gr * self.resistance * self.r) |
| 133 | |
| 134 | # State feedback matrices |
| 135 | # X will be of the format |
| 136 | # [[positionl], [velocityl], [positionr], velocityr]] |
| 137 | self.A_continuous = numpy.matrix( |
| 138 | [[0, 1, 0, 0], |
| 139 | [0, -self.msp * self.tcl, 0, -self.msn * self.tcr], |
| 140 | [0, 0, 0, 1], |
| 141 | [0, -self.msn * self.tcl, 0, -self.msp * self.tcr]]) |
| 142 | self.B_continuous = numpy.matrix( |
| 143 | [[0, 0], |
| 144 | [self.msp * self.mpl, self.msn * self.mpr], |
| 145 | [0, 0], |
| 146 | [self.msn * self.mpl, self.msp * self.mpr]]) |
| 147 | self.C = numpy.matrix([[1, 0, 0, 0], |
| 148 | [0, 0, 1, 0]]) |
| 149 | self.D = numpy.matrix([[0, 0], |
| 150 | [0, 0]]) |
| 151 | |
| 152 | self.A, self.B = self.ContinuousToDiscrete( |
| 153 | self.A_continuous, self.B_continuous, self.dt) |
| 154 | |
| 155 | def BuildDrivetrainController(self, q_pos, q_vel): |
| 156 | # Tune the LQR controller |
| 157 | self.Q = numpy.matrix([[(1.0 / (q_pos ** 2.0)), 0.0, 0.0, 0.0], |
| 158 | [0.0, (1.0 / (q_vel ** 2.0)), 0.0, 0.0], |
| 159 | [0.0, 0.0, (1.0 / (q_pos ** 2.0)), 0.0], |
| 160 | [0.0, 0.0, 0.0, (1.0 / (q_vel ** 2.0))]]) |
| 161 | |
| 162 | self.R = numpy.matrix([[(1.0 / (12.0 ** 2.0)), 0.0], |
| 163 | [0.0, (1.0 / (12.0 ** 2.0))]]) |
| 164 | self.K = controls.dlqr(self.A, self.B, self.Q, self.R) |
| 165 | |
| 166 | glog.debug('DT q_pos %f q_vel %s %s', q_pos, q_vel, self._name) |
| 167 | glog.debug(str(numpy.linalg.eig(self.A - self.B * self.K)[0])) |
| 168 | glog.debug('K %s', repr(self.K)) |
| 169 | |
| 170 | self.hlp = 0.3 |
| 171 | self.llp = 0.4 |
| 172 | self.PlaceObserverPoles([self.hlp, self.hlp, self.llp, self.llp]) |
| 173 | |
| 174 | self.U_max = numpy.matrix([[12.0], [12.0]]) |
| 175 | self.U_min = numpy.matrix([[-12.0], [-12.0]]) |
| 176 | |
| 177 | class KFDrivetrain(Drivetrain): |
| 178 | def __init__(self, drivetrain_params, name="KFDrivetrain", |
| 179 | left_low=True, right_low=True): |
| 180 | """Kalman filter values of a drivetrain. |
| 181 | |
| 182 | Args: |
| 183 | drivetrain_params: DrivetrainParams, class of values defining the drivetrain. |
| 184 | name: string, Name of this drivetrain. |
| 185 | left_low: bool, Whether the left is in high gear. |
| 186 | right_low: bool, Whether the right is in high gear. |
| 187 | """ |
| 188 | super(KFDrivetrain, self).__init__(drivetrain_params, name, left_low, right_low) |
| 189 | |
| 190 | self.unaugmented_A_continuous = self.A_continuous |
| 191 | self.unaugmented_B_continuous = self.B_continuous |
| 192 | |
| 193 | # The practical voltage applied to the wheels is |
| 194 | # V_left = U_left + left_voltage_error |
| 195 | # |
| 196 | # The states are |
| 197 | # [left position, left velocity, right position, right velocity, |
| 198 | # left voltage error, right voltage error, angular_error] |
| 199 | # |
| 200 | # The left and right positions are filtered encoder positions and are not |
| 201 | # adjusted for heading error. |
| 202 | # The turn velocity as computed by the left and right velocities is |
| 203 | # adjusted by the gyro velocity. |
| 204 | # The angular_error is the angular velocity error between the wheel speed |
| 205 | # and the gyro speed. |
| 206 | self.A_continuous = numpy.matrix(numpy.zeros((7, 7))) |
| 207 | self.B_continuous = numpy.matrix(numpy.zeros((7, 2))) |
| 208 | self.A_continuous[0:4,0:4] = self.unaugmented_A_continuous |
| 209 | self.A_continuous[0:4,4:6] = self.unaugmented_B_continuous |
| 210 | self.B_continuous[0:4,0:2] = self.unaugmented_B_continuous |
| 211 | self.A_continuous[0,6] = 1 |
| 212 | self.A_continuous[2,6] = -1 |
| 213 | |
| 214 | self.A, self.B = self.ContinuousToDiscrete( |
| 215 | self.A_continuous, self.B_continuous, self.dt) |
| 216 | |
| 217 | self.C = numpy.matrix([[1, 0, 0, 0, 0, 0, 0], |
| 218 | [0, 0, 1, 0, 0, 0, 0], |
| 219 | [0, -0.5 / drivetrain_params.robot_radius, 0, 0.5 / drivetrain_params.robot_radius, 0, 0, 0]]) |
| 220 | |
| 221 | self.D = numpy.matrix([[0, 0], |
| 222 | [0, 0], |
| 223 | [0, 0]]) |
| 224 | |
| 225 | q_pos = 0.05 |
| 226 | q_vel = 1.00 |
| 227 | q_voltage = 10.0 |
| 228 | q_encoder_uncertainty = 2.00 |
| 229 | |
| 230 | self.Q = numpy.matrix([[(q_pos ** 2.0), 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], |
| 231 | [0.0, (q_vel ** 2.0), 0.0, 0.0, 0.0, 0.0, 0.0], |
| 232 | [0.0, 0.0, (q_pos ** 2.0), 0.0, 0.0, 0.0, 0.0], |
| 233 | [0.0, 0.0, 0.0, (q_vel ** 2.0), 0.0, 0.0, 0.0], |
| 234 | [0.0, 0.0, 0.0, 0.0, (q_voltage ** 2.0), 0.0, 0.0], |
| 235 | [0.0, 0.0, 0.0, 0.0, 0.0, (q_voltage ** 2.0), 0.0], |
| 236 | [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, (q_encoder_uncertainty ** 2.0)]]) |
| 237 | |
| 238 | r_pos = 0.0001 |
| 239 | r_gyro = 0.000001 |
| 240 | self.R = numpy.matrix([[(r_pos ** 2.0), 0.0, 0.0], |
| 241 | [0.0, (r_pos ** 2.0), 0.0], |
| 242 | [0.0, 0.0, (r_gyro ** 2.0)]]) |
| 243 | |
| 244 | # Solving for kf gains. |
| 245 | self.KalmanGain, self.Q_steady = controls.kalman( |
| 246 | A=self.A, B=self.B, C=self.C, Q=self.Q, R=self.R) |
| 247 | |
| 248 | self.L = self.A * self.KalmanGain |
| 249 | |
| 250 | unaug_K = self.K |
| 251 | |
| 252 | # Implement a nice closed loop controller for use by the closed loop |
| 253 | # controller. |
| 254 | self.K = numpy.matrix(numpy.zeros((self.B.shape[1], self.A.shape[0]))) |
| 255 | self.K[0:2, 0:4] = unaug_K |
| 256 | self.K[0, 4] = 1.0 |
| 257 | self.K[1, 5] = 1.0 |
| 258 | |
| 259 | self.Qff = numpy.matrix(numpy.zeros((4, 4))) |
| 260 | qff_pos = 0.005 |
| 261 | qff_vel = 1.00 |
| 262 | self.Qff[0, 0] = 1.0 / qff_pos ** 2.0 |
| 263 | self.Qff[1, 1] = 1.0 / qff_vel ** 2.0 |
| 264 | self.Qff[2, 2] = 1.0 / qff_pos ** 2.0 |
| 265 | self.Qff[3, 3] = 1.0 / qff_vel ** 2.0 |
| 266 | self.Kff = numpy.matrix(numpy.zeros((2, 7))) |
| 267 | self.Kff[0:2, 0:4] = controls.TwoStateFeedForwards(self.B[0:4,:], self.Qff) |
| 268 | |
| 269 | self.InitializeState() |
| 270 | |
| 271 | |
| 272 | def WriteDrivetrain(drivetrain_files, kf_drivetrain_files, year_namespace, |
| 273 | drivetrain_params): |
| 274 | |
| 275 | # Write the generated constants out to a file. |
| 276 | drivetrain_low_low = Drivetrain(name="DrivetrainLowLow", |
| 277 | left_low=True, right_low=True, drivetrain_params=drivetrain_params) |
| 278 | drivetrain_low_high = Drivetrain(name="DrivetrainLowHigh", |
| 279 | left_low=True, right_low=False, drivetrain_params=drivetrain_params) |
| 280 | drivetrain_high_low = Drivetrain(name="DrivetrainHighLow", |
| 281 | left_low=False, right_low=True, drivetrain_params=drivetrain_params) |
| 282 | drivetrain_high_high = Drivetrain(name="DrivetrainHighHigh", |
| 283 | left_low=False, right_low=False, drivetrain_params=drivetrain_params) |
| 284 | |
| 285 | kf_drivetrain_low_low = KFDrivetrain(name="KFDrivetrainLowLow", |
| 286 | left_low=True, right_low=True, drivetrain_params=drivetrain_params) |
| 287 | kf_drivetrain_low_high = KFDrivetrain(name="KFDrivetrainLowHigh", |
| 288 | left_low=True, right_low=False, drivetrain_params=drivetrain_params) |
| 289 | kf_drivetrain_high_low = KFDrivetrain(name="KFDrivetrainHighLow", |
| 290 | left_low=False, right_low=True, drivetrain_params=drivetrain_params) |
| 291 | kf_drivetrain_high_high = KFDrivetrain(name="KFDrivetrainHighHigh", |
| 292 | left_low=False, right_low=False, drivetrain_params=drivetrain_params) |
| 293 | |
| 294 | namespaces = [year_namespace, 'control_loops', 'drivetrain'] |
| 295 | dog_loop_writer = control_loop.ControlLoopWriter( |
| 296 | "Drivetrain", [drivetrain_low_low, drivetrain_low_high, |
| 297 | drivetrain_high_low, drivetrain_high_high], |
| 298 | namespaces = namespaces) |
| 299 | dog_loop_writer.AddConstant(control_loop.Constant("kDt", "%f", |
| 300 | drivetrain_low_low.dt)) |
| 301 | dog_loop_writer.AddConstant(control_loop.Constant("kStallTorque", "%f", |
| 302 | drivetrain_low_low.stall_torque)) |
| 303 | dog_loop_writer.AddConstant(control_loop.Constant("kStallCurrent", "%f", |
| 304 | drivetrain_low_low.stall_current)) |
| 305 | dog_loop_writer.AddConstant(control_loop.Constant("kFreeSpeed", "%f", |
| 306 | drivetrain_low_low.free_speed)) |
| 307 | dog_loop_writer.AddConstant(control_loop.Constant("kFreeCurrent", "%f", |
| 308 | drivetrain_low_low.free_current)) |
| 309 | dog_loop_writer.AddConstant(control_loop.Constant("kJ", "%f", |
| 310 | drivetrain_low_low.J)) |
| 311 | dog_loop_writer.AddConstant(control_loop.Constant("kMass", "%f", |
| 312 | drivetrain_low_low.mass)) |
| 313 | dog_loop_writer.AddConstant(control_loop.Constant("kRobotRadius", "%f", |
| 314 | drivetrain_low_low.robot_radius)) |
| 315 | dog_loop_writer.AddConstant(control_loop.Constant("kWheelRadius", "%f", |
| 316 | drivetrain_low_low.r)) |
| 317 | dog_loop_writer.AddConstant(control_loop.Constant("kR", "%f", |
| 318 | drivetrain_low_low.resistance)) |
| 319 | dog_loop_writer.AddConstant(control_loop.Constant("kV", "%f", |
| 320 | drivetrain_low_low.Kv)) |
| 321 | dog_loop_writer.AddConstant(control_loop.Constant("kT", "%f", |
| 322 | drivetrain_low_low.Kt)) |
| 323 | dog_loop_writer.AddConstant(control_loop.Constant("kLowGearRatio", "%f", |
| 324 | drivetrain_low_low.G_low)) |
| 325 | dog_loop_writer.AddConstant(control_loop.Constant("kHighGearRatio", "%f", |
| 326 | drivetrain_high_high.G_high)) |
| 327 | dog_loop_writer.AddConstant(control_loop.Constant("kHighOutputRatio", "%f", |
| 328 | drivetrain_high_high.G_high * drivetrain_high_high.r)) |
| 329 | |
| 330 | dog_loop_writer.Write(drivetrain_files[0], drivetrain_files[1]) |
| 331 | |
| 332 | kf_loop_writer = control_loop.ControlLoopWriter( |
| 333 | "KFDrivetrain", [kf_drivetrain_low_low, kf_drivetrain_low_high, |
| 334 | kf_drivetrain_high_low, kf_drivetrain_high_high], |
| 335 | namespaces = namespaces) |
| 336 | kf_loop_writer.Write(kf_drivetrain_files[0], kf_drivetrain_files[1]) |
| 337 | |
| 338 | def PlotDrivetrainMotions(drivetrain_params): |
| 339 | # Simulate the response of the system to a step input. |
| 340 | drivetrain = Drivetrain(left_low=False, right_low=False, drivetrain_params=drivetrain_params) |
| 341 | simulated_left = [] |
| 342 | simulated_right = [] |
| 343 | for _ in xrange(100): |
| 344 | drivetrain.Update(numpy.matrix([[12.0], [12.0]])) |
| 345 | simulated_left.append(drivetrain.X[0, 0]) |
| 346 | simulated_right.append(drivetrain.X[2, 0]) |
| 347 | |
| 348 | pylab.rc('lines', linewidth=4) |
| 349 | pylab.plot(range(100), simulated_left, label='left position') |
| 350 | pylab.plot(range(100), simulated_right, 'r--', label='right position') |
| 351 | pylab.suptitle('Acceleration Test\n12 Volt Step Input') |
| 352 | pylab.legend(loc='lower right') |
| 353 | pylab.show() |
| 354 | |
| 355 | # Simulate forwards motion. |
| 356 | drivetrain = Drivetrain(left_low=False, right_low=False, drivetrain_params=drivetrain_params) |
| 357 | close_loop_left = [] |
| 358 | close_loop_right = [] |
| 359 | left_power = [] |
| 360 | right_power = [] |
| 361 | R = numpy.matrix([[1.0], [0.0], [1.0], [0.0]]) |
| 362 | for _ in xrange(300): |
| 363 | U = numpy.clip(drivetrain.K * (R - drivetrain.X_hat), |
| 364 | drivetrain.U_min, drivetrain.U_max) |
| 365 | drivetrain.UpdateObserver(U) |
| 366 | drivetrain.Update(U) |
| 367 | close_loop_left.append(drivetrain.X[0, 0]) |
| 368 | close_loop_right.append(drivetrain.X[2, 0]) |
| 369 | left_power.append(U[0, 0]) |
| 370 | right_power.append(U[1, 0]) |
| 371 | |
| 372 | pylab.plot(range(300), close_loop_left, label='left position') |
| 373 | pylab.plot(range(300), close_loop_right, 'm--', label='right position') |
| 374 | pylab.plot(range(300), left_power, label='left power') |
| 375 | pylab.plot(range(300), right_power, '--', label='right power') |
| 376 | pylab.suptitle('Linear Move\nLeft and Right Position going to 1') |
| 377 | pylab.legend() |
| 378 | pylab.show() |
| 379 | |
| 380 | # Try turning in place |
| 381 | drivetrain = Drivetrain(drivetrain_params=drivetrain_params) |
| 382 | close_loop_left = [] |
| 383 | close_loop_right = [] |
| 384 | R = numpy.matrix([[-1.0], [0.0], [1.0], [0.0]]) |
| 385 | for _ in xrange(200): |
| 386 | U = numpy.clip(drivetrain.K * (R - drivetrain.X_hat), |
| 387 | drivetrain.U_min, drivetrain.U_max) |
| 388 | drivetrain.UpdateObserver(U) |
| 389 | drivetrain.Update(U) |
| 390 | close_loop_left.append(drivetrain.X[0, 0]) |
| 391 | close_loop_right.append(drivetrain.X[2, 0]) |
| 392 | |
| 393 | pylab.plot(range(200), close_loop_left, label='left position') |
| 394 | pylab.plot(range(200), close_loop_right, label='right position') |
| 395 | pylab.suptitle('Angular Move\nLeft position going to -1 and right position going to 1') |
| 396 | pylab.legend(loc='center right') |
| 397 | pylab.show() |
| 398 | |
| 399 | # Try turning just one side. |
| 400 | drivetrain = Drivetrain(drivetrain_params=drivetrain_params) |
| 401 | close_loop_left = [] |
| 402 | close_loop_right = [] |
| 403 | R = numpy.matrix([[0.0], [0.0], [1.0], [0.0]]) |
| 404 | for _ in xrange(300): |
| 405 | U = numpy.clip(drivetrain.K * (R - drivetrain.X_hat), |
| 406 | drivetrain.U_min, drivetrain.U_max) |
| 407 | drivetrain.UpdateObserver(U) |
| 408 | drivetrain.Update(U) |
| 409 | close_loop_left.append(drivetrain.X[0, 0]) |
| 410 | close_loop_right.append(drivetrain.X[2, 0]) |
| 411 | |
| 412 | pylab.plot(range(300), close_loop_left, label='left position') |
| 413 | pylab.plot(range(300), close_loop_right, label='right position') |
| 414 | pylab.suptitle('Pivot\nLeft position not changing and right position going to 1') |
| 415 | pylab.legend(loc='center right') |
| 416 | pylab.show() |