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 numpy as np
from common.realtime import DT_CTRL
from selfdrive.car.body import bodycan
from opendbc.can.packer import CANPacker
MAX_TORQUE = 500
MAX_TORQUE_RATE = 50
MAX_ANGLE_ERROR = 7
class CarController():
def __init__(self, dbc_name, CP, VM):
self.packer = CANPacker(dbc_name)
self.i_speed = 0
self.i_balance = 0
self.d_balance = 0
self.i_torque = 0
self.speed_measured = 0.
self.speed_desired = 0.
self.torque_r_filtered = 0.
self.torque_l_filtered = 0.
self.wait_counter = int(5. / DT_CTRL)
@staticmethod
def deadband_filter(torque, deadband):
if torque > 0:
torque += deadband
else:
torque -= deadband
return torque
def update(self, CC, CS):
if self.wait_counter > 0:
self.wait_counter -= 1
return CC.actuators.copy(), []
if len(CC.orientationNED) == 0 or len(CC.angularVelocity) == 0:
return CC.actuators.copy(), []
# ///////////////////////////////////////
# Steer and accel mixin. Speed should be used as a target? (speed should be in m/s! now it is in RPM)
# Mix steer into torque_diff
# self.steerRatio = 0.5
# torque_r = int(np.clip((CC.actuators.accel*1000) - (CC.actuators.steer*1000) * self.steerRatio, -1000, 1000))
# torque_l = int(np.clip((CC.actuators.accel*1000) + (CC.actuators.steer*1000) * self.steerRatio, -1000, 1000))
# ////
# Setpoint speed PID
kp_speed = 0.001
ki_speed = 0.00001
alpha_speed = 1.0
self.speed_measured = (CS.out.wheelSpeeds.fl + CS.out.wheelSpeeds.fr) / 2.
self.speed_desired = (1. - alpha_speed)*self.speed_desired
p_speed = (self.speed_desired - self.speed_measured)
self.i_speed += ki_speed * p_speed
self.i_speed = np.clip(self.i_speed, -0.1, 0.1)
set_point = p_speed * kp_speed + self.i_speed
# Balancing PID
kp_balance = 1300
ki_balance = 0
kd_balance = 280
alpha_d_balance = 1.0
# Clip angle error, this is enough to get up from stands
p_balance = np.clip((-CC.orientationNED[1]) - set_point, np.radians(-MAX_ANGLE_ERROR), np.radians(MAX_ANGLE_ERROR))
self.i_balance += CS.out.wheelSpeeds.fl + CS.out.wheelSpeeds.fr
self.d_balance = np.clip(((1. - alpha_d_balance) * self.d_balance + alpha_d_balance * -CC.angularVelocity[1]), -1., 1.)
torque = int(np.clip((p_balance*kp_balance + self.i_balance*ki_balance + self.d_balance*kd_balance), -1000, 1000))
# Positional recovery PID
kp_torque = 0.95
ki_torque = 0.1
p_torque = (CS.out.wheelSpeeds.fl - CS.out.wheelSpeeds.fr)
self.i_torque += (CS.out.wheelSpeeds.fl - CS.out.wheelSpeeds.fr)
torque_diff = int(np.clip(p_torque*kp_torque + self.i_torque*ki_torque, -100, 100))
# Combine 2 PIDs outputs
torque_r = torque + torque_diff
torque_l = torque - torque_diff
# Torque rate limits
self.torque_r_filtered = np.clip(self.deadband_filter(torque_r, 10) ,
self.torque_r_filtered - MAX_TORQUE_RATE,
self.torque_r_filtered + MAX_TORQUE_RATE)
self.torque_l_filtered = np.clip(self.deadband_filter(torque_l, 10),
self.torque_l_filtered - MAX_TORQUE_RATE,
self.torque_l_filtered + MAX_TORQUE_RATE)
torque_r = int(np.clip(self.torque_r_filtered, -MAX_TORQUE, MAX_TORQUE))
torque_l = int(np.clip(self.torque_l_filtered, -MAX_TORQUE, MAX_TORQUE))
# ///////////////////////////////////////
can_sends = []
can_sends.append(bodycan.create_control(self.packer, torque_l, torque_r))
new_actuators = CC.actuators.copy()
new_actuators.accel = torque_l
new_actuators.steer = torque_r
return new_actuators, can_sends