openpilot is an open source driver assistance system. openpilot performs the functions of Automated Lane Centering and Adaptive Cruise Control for over 200 supported car makes and models.
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import math
import numpy as np
from common.numpy_fast import clip
from common.realtime import DT_CTRL
from cereal import log
from selfdrive.controls.lib.drive_helpers import get_steer_max
class LatControlLQR():
def __init__(self, CP):
self.scale = CP.lateralTuning.lqr.scale
self.ki = CP.lateralTuning.lqr.ki
self.A = np.array(CP.lateralTuning.lqr.a).reshape((2, 2))
self.B = np.array(CP.lateralTuning.lqr.b).reshape((2, 1))
self.C = np.array(CP.lateralTuning.lqr.c).reshape((1, 2))
self.K = np.array(CP.lateralTuning.lqr.k).reshape((1, 2))
self.L = np.array(CP.lateralTuning.lqr.l).reshape((2, 1))
self.dc_gain = CP.lateralTuning.lqr.dcGain
self.x_hat = np.array([[0], [0]])
self.i_unwind_rate = 0.3 * DT_CTRL
self.i_rate = 1.0 * DT_CTRL
self.sat_count_rate = 1.0 * DT_CTRL
self.sat_limit = CP.steerLimitTimer
self.reset()
def reset(self):
self.i_lqr = 0.0
self.sat_count = 0.0
def _check_saturation(self, control, check_saturation, limit):
saturated = abs(control) == limit
if saturated and check_saturation:
self.sat_count += self.sat_count_rate
else:
self.sat_count -= self.sat_count_rate
self.sat_count = clip(self.sat_count, 0.0, 1.0)
return self.sat_count > self.sat_limit
def update(self, active, CS, CP, VM, params, desired_curvature, desired_curvature_rate):
lqr_log = log.ControlsState.LateralLQRState.new_message()
steers_max = get_steer_max(CP, CS.vEgo)
torque_scale = (0.45 + CS.vEgo / 60.0)**2 # Scale actuator model with speed
# Subtract offset. Zero angle should correspond to zero torque
steering_angle_no_offset = CS.steeringAngleDeg - params.angleOffsetAverageDeg
desired_angle = math.degrees(VM.get_steer_from_curvature(-desired_curvature, CS.vEgo))
instant_offset = params.angleOffsetDeg - params.angleOffsetAverageDeg
desired_angle += instant_offset # Only add offset that originates from vehicle model errors
# Update Kalman filter
angle_steers_k = float(self.C.dot(self.x_hat))
e = steering_angle_no_offset - angle_steers_k
self.x_hat = self.A.dot(self.x_hat) + self.B.dot(CS.steeringTorqueEps / torque_scale) + self.L.dot(e)
if CS.vEgo < 0.3 or not active:
lqr_log.active = False
lqr_output = 0.
output_steer = 0.
self.reset()
else:
lqr_log.active = True
# LQR
u_lqr = float(desired_angle / self.dc_gain - self.K.dot(self.x_hat))
lqr_output = torque_scale * u_lqr / self.scale
# Integrator
if CS.steeringPressed:
self.i_lqr -= self.i_unwind_rate * float(np.sign(self.i_lqr))
else:
error = desired_angle - angle_steers_k
i = self.i_lqr + self.ki * self.i_rate * error
control = lqr_output + i
if (error >= 0 and (control <= steers_max or i < 0.0)) or \
(error <= 0 and (control >= -steers_max or i > 0.0)):
self.i_lqr = i
output_steer = lqr_output + self.i_lqr
output_steer = clip(output_steer, -steers_max, steers_max)
check_saturation = (CS.vEgo > 10) and not CS.steeringRateLimited and not CS.steeringPressed
saturated = self._check_saturation(output_steer, check_saturation, steers_max)
lqr_log.steeringAngleDeg = angle_steers_k + params.angleOffsetAverageDeg
lqr_log.i = self.i_lqr
lqr_log.output = output_steer
lqr_log.lqrOutput = lqr_output
lqr_log.saturated = saturated
return output_steer, desired_angle, lqr_log