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https://github.com/sunnypilot/sunnypilot.git
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2174005f05
* Initial commit
* Fix bugs
* Need more torque rate
* Cleanup cray cray control
* Write nicely
* Chiiil
* Not relevant for cray cray control
* Do some logging
* Seems like it has more torque than I thought
* Bit more feedforward
* Tune change
* Retune
* Retune
* Little more chill
* Add coroll
* Add corolla
* Give craycray a good name
* Update to proper logging
* D to the PI
* Should be in radians
* Add d
* Start oscillations
* Add D term
* Only change torque rate limits for new tune
* Add d logging
* Should be enough
* Wrong sign in D
* Downtune a little
* Needed to prevent faults
* Add lqr rav4 to tune
* Try derivative again
* Data based retune
* Data based retune
* add friction compensation
* Doesnt need too much P with friction comp
* remove lqr
* Remove kd
* Fix tests
* fix tests
* Too much error
* Get roll induced error under 1cm/deg
* Too much jitter
* Do roll comp
* Add ki
* Final update
* Update refs
* Cleanup latcontrol_torque a little more
old-commit-hash: fe0bcdaef6
80 lines
3.2 KiB
Python
80 lines
3.2 KiB
Python
import math
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from selfdrive.controls.lib.pid import PIDController
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from common.numpy_fast import interp
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from selfdrive.controls.lib.latcontrol import LatControl, MIN_STEER_SPEED
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from selfdrive.controls.lib.vehicle_model import ACCELERATION_DUE_TO_GRAVITY
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from cereal import log
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# At higher speeds (25+mph) we can assume:
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# Lateral acceleration achieved by a specific car correlates to
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# torque applied to the steering rack. It does not correlate to
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# wheel slip, or to speed.
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# This controller applies torque to achieve desired lateral
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# accelerations. To compensate for the low speed effects we
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# use a LOW_SPEED_FACTOR in the error. Additionally there is
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# friction in the steering wheel that needs to be overcome to
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# move it at all, this is compensated for too.
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LOW_SPEED_FACTOR = 200
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JERK_THRESHOLD = 0.2
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class LatControlTorque(LatControl):
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def __init__(self, CP, CI):
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super().__init__(CP, CI)
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self.pid = PIDController(CP.lateralTuning.torque.kp, CP.lateralTuning.torque.ki,
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k_f=CP.lateralTuning.torque.kf, pos_limit=1.0, neg_limit=-1.0)
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self.get_steer_feedforward = CI.get_steer_feedforward_function()
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self.steer_max = 1.0
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self.pid.pos_limit = self.steer_max
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self.pid.neg_limit = -self.steer_max
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self.use_steering_angle = CP.lateralTuning.torque.useSteeringAngle
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self.friction = CP.lateralTuning.torque.friction
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def reset(self):
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super().reset()
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self.pid.reset()
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def update(self, active, CS, CP, VM, params, last_actuators, desired_curvature, desired_curvature_rate, llk):
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pid_log = log.ControlsState.LateralTorqueState.new_message()
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if CS.vEgo < MIN_STEER_SPEED or not active:
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output_torque = 0.0
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pid_log.active = False
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self.pid.reset()
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else:
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if self.use_steering_angle:
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actual_curvature = -VM.calc_curvature(math.radians(CS.steeringAngleDeg - params.angleOffsetDeg), CS.vEgo, params.roll)
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else:
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actual_curvature = llk.angularVelocityCalibrated.value[2] / CS.vEgo
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desired_lateral_accel = desired_curvature * CS.vEgo**2
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desired_lateral_jerk = desired_curvature_rate * CS.vEgo**2
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actual_lateral_accel = actual_curvature * CS.vEgo**2
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setpoint = desired_lateral_accel + LOW_SPEED_FACTOR * desired_curvature
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measurement = actual_lateral_accel + LOW_SPEED_FACTOR * actual_curvature
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error = setpoint - measurement
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pid_log.error = error
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ff = desired_lateral_accel - params.roll * ACCELERATION_DUE_TO_GRAVITY
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output_torque = self.pid.update(error,
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override=CS.steeringPressed, feedforward=ff,
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speed=CS.vEgo,
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freeze_integrator=CS.steeringRateLimited)
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friction_compensation = interp(desired_lateral_jerk, [-JERK_THRESHOLD, JERK_THRESHOLD], [-self.friction, self.friction])
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output_torque += friction_compensation
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pid_log.active = True
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pid_log.p = self.pid.p
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pid_log.i = self.pid.i
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pid_log.d = self.pid.d
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pid_log.f = self.pid.f
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pid_log.output = -output_torque
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pid_log.saturated = self._check_saturation(self.steer_max - abs(output_torque) < 1e-3, CS)
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#TODO left is positive in this convention
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return -output_torque, 0.0, pid_log
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