jyoung/selfdrive/car/body/carcontroller.py

import numpy as np

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.

  @staticmethod
  def deadband_filter(torque, deadband):
    if torque > 0:
      torque += deadband
    else:
      torque -= deadband
    return torque

  def update(self, CC, CS):
    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