def __init__(self, stepweite=0.01):
super(WrapperOpenAI, self).__init__()
# Define action and observation space
# They must be gym.spaces objects
# nur aileron
high_action_space = np.array([1.], dtype=np.float32)
self.action_space = spaces.Box(low=-high_action_space,
high=high_action_space,
dtype=np.float32)
# Zustandsraum 4 current_phi_dot, current_phi, abs(error_current_phi_target_phi), integration_error
high_observation_space = np.array([np.inf, np.inf, np.inf],
dtype=np.float32)
self.observation_space = spaces.Box(low=-high_observation_space,
high=high_observation_space,
dtype=np.float32)
# reward
self.reward_range = np.array([-np.inf, np.inf], dtype=np.float32)
# spezielle Parameter für das Enviroment FDM
self.aircraft = Aircraft_baever() # Ball oder C172
self.pid = PidRegler()
self.dynamicSystem = DynamicSystem6DoF()
self.stepweite = stepweite
self.udpClient = UdpClient('127.0.0.1', 5566)
# frage: ist das die richtige Stelle, oder besser im DDPG-Controller
self.targetValues = {
'targetPhi_grad': 0,
'targetTheta_grad': 0,
'targetPsi': 0,
'targetSpeed': 0,
'target_z_dot': 0.0
}
self.envelopeBounds = {
'phiMax_grad': 30,
'phiMin_grad': -30,
'thetaMax_grad': 30,
'thetaMin_grad': -30,
'speedMax': 72,
'speedMin': 33
}
# fuer plotten
self.plotter = PlotState()
self.anzahlSteps = 0
self.anzahlEpisoden = 0
self.observationErrorAkkumulation = np.zeros(3)
self.integration_error_stepsize_ = 0
self.servo_command = 0
self.action_servo_command_history = np.zeros(2)
self.bandbreite_servo_actions = 0
def __init__(self, stepweite=0.05):
super(WrapperOpenAI, self).__init__()
# Define action and observation space
# They must be gym.spaces objects
# nur aileron
high_action_space = np.array([1.], dtype=np.float32)
self.action_space = spaces.Box(low=-high_action_space, high=high_action_space, dtype=np.float32)
# Zustandsraum 4 current_phi_dot, current_phi, abs(error_current_phi_target_phi), integration_error
high_observation_space = np.array([np.inf, np.inf, np.inf, np.inf, np.inf], dtype=np.float32)
self.observation_space = spaces.Box(low=-high_observation_space, high=high_observation_space, dtype=np.float32)
# reward
self.reward_range = np.array([-np.inf, np.inf], dtype=np.float32)
# spezielle Parameter für das Enviroment FDM
#self.aircraft = Aircraft(config.geometrieClass) # Ball oder C172
self.aircraft_beaver = Aircraft_baever()
self.pid = PidRegler()
self.dynamicSystem = DynamicSystem6DoF()
self.umrechnungenKoordinaten = UmrechnungenKoordinaten()
self.stepweite = stepweite
self.udpClient = UdpClient('127.0.0.1', 5566)
# frage: ist das die richtige Stelle, oder besser im DDPG-Controller
self.targetValues = {'targetPhi': 0,
'targetTheta': 0,
'targetPsi': 0,
'targetSpeed': 0,
'target_z_dot': 0.0}
self.envelopeBounds = {'phiMax': 20,
'phiMin': -20,
'thetaMax': 10,
'thetaMin': -30,
'speedMax': 80,
'speedMin': 33
}
self.integration_stepsize = 3
self.error_array_u = np.zeros(self.integration_stepsize)
self.integration_error_u = 0
self.error_array_theta = np.zeros(self.integration_stepsize)
self.integration_error_theta = 0
# fuer plotten
self.plotter = PlotState()
self.anzahlSteps = 0
self.anzahlEpisoden = 0
class WrapperOpenAI (gym.Env):
"""Custom Environment that follows gym interface"""
metadata = {'render.modes': ['human']}
def __init__(self, stepweite=0.02):
super(WrapperOpenAI, self).__init__()
# Define action and observation space
# They must be gym.spaces objects
# nur aileron
high_action_space = np.array([1.], dtype=np.float32)
self.action_space = spaces.Box(low=-high_action_space, high=high_action_space, dtype=np.float32)
# Zustandsraum 4 current_phi_dot, current_phi, abs(error_current_phi_target_phi), integration_error
high_observation_space = np.array([np.inf, np.inf, np.inf, np.inf], dtype=np.float32)
self.observation_space = spaces.Box(low=-high_observation_space, high=high_observation_space, dtype=np.float32)
# reward
self.reward_range = np.array([-np.inf, np.inf], dtype=np.float32)
# spezielle Parameter für das Enviroment FDM
#self.aircraft = Aircraft(config.geometrieClass) # Ball oder C172
self.aircraft_beaver = Aircraft_baever()
self.pid = PidRegler()
self.dynamicSystem = DynamicSystem6DoF()
self.umrechnungenKoordinaten = UmrechnungenKoordinaten()
self.stepweite = stepweite
self.udpClient = UdpClient('127.0.0.1', 5566)
# frage: ist das die richtige Stelle, oder besser im DDPG-Controller
self.targetValues = {'targetPhi_grad': 0,
'targetTheta_grad': 0,
'targetPsi': 0,
'targetSpeed': 0,
'target_z_dot': 0.0}
self.envelopeBounds = {'phiMax_grad': 30,
'phiMin_grad': -30,
'thetaMax_grad': 30,
'thetaMin_grad': -30,
'speedMax': 72,
'speedMin': 33
}
self.servo_command_elevator = 0
self.action_servo_command_history_elevator = np.zeros(2)
self.bandbreite_servo_actions_elevator = 0
self.servo_command_aileron = 0
self.action_servo_command_history_aileron = np.zeros(2)
self.bandbreite_servo_actions_aileron = 0
# fuer plotten
self.plotter = PlotState()
self.anzahlSteps = 0
self.anzahlEpisoden = 0
def reset(self):
np.random.seed()
self.servo_command_elevator = 0
self.action_servo_command_history_elevator = np.zeros(2)
self.bandbreite_servo_actions_elevator = 0
self.servo_command_aileron = 0
self.action_servo_command_history_aileron = np.zeros(2)
self.bandbreite_servo_actions_aileron = 0
self.anzahlSteps = 1
self.anzahlEpisoden += 1
# set targets
self.targetValues['targetSpeed'] = np.random.uniform(40, 55) # m/s
self.targetValues['targetTheta_grad'] = np.random.uniform(0, 0) # m/s
self.targetValues['targetPhi_grad'] = np.random.uniform(0,0) # m/s
self.targetValues['target_z_dot'] = np.random.uniform(0, 0) # m/s
print('new Targets: ', self.targetValues)
# set state at initial
u_as_random = np.random.uniform(40, 55)
phi_as_random = np.deg2rad(np.random.uniform(-0, 0))
theta_as_random = np.deg2rad(np.random.uniform(-10, 10))
self.aircraft_beaver.setState(
np.array([u_as_random, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, phi_as_random, theta_as_random, 0.0]))
observation = self.user_defined_observation(self.aircraft_beaver.getState(), self.aircraft_beaver.z_dot_g_ks)
return observation # reward, done, info can't be included
def step(self, action_command):
self.servo_command_elevator = action_command[0]
self.anzahlSteps += 1
self.aircraft_beaver.delta_elevator = np.deg2rad(np.clip(self.servo_command_elevator, -1, 1) * (20))
self.aircraft_beaver.delta_aileron = self.pid._innerLoopAileron(np.deg2rad(0), self.aircraft_beaver.phi, self.aircraft_beaver.p, self.aircraft_beaver.delta_aileron)
# MultiAgent
# MultiAgent
self.servo_command_thrust = action_command[1]
self.aircraft_beaver.delta_thrust = np.interp(self.servo_command_thrust, [-1, 1], [0.0, 1]) # Übersetzung der Ausgabe KNN zu Thrust-Setting
# Headline: integrate step
for x in range(10):
solver = self.dynamicSystem.integrate(self.aircraft_beaver.getState(), self.aircraft_beaver.getForces(),
self.aircraft_beaver.getMoments(),
self.aircraft_beaver.mass, self.aircraft_beaver.inertia,
self.stepweite) # def integrate(self, state, mass, inertia, forces, moments, stepweite):
# State1 in f_ks
self.aircraft_beaver.setState(np.array(
[solver.y[0][0], solver.y[1][0], solver.y[2][0], solver.y[3][0], solver.y[4][0], solver.y[5][0],
solver.y[6][0], solver.y[7][0], solver.y[8][0], solver.y[9][0], solver.y[10][0], solver.y[11][0]]))
#set Values for g_ks
self.aircraft_beaver.x_dot_g_ks, self.aircraft_beaver.y_dot_g_ks, self.aircraft_beaver.z_dot_g_ks = self.umrechnungenKoordinaten.flug2geo(
[self.aircraft_beaver.u, self.aircraft_beaver.v, self.aircraft_beaver.w], self.aircraft_beaver.phi, self.aircraft_beaver.theta, self.aircraft_beaver.psi)
observation = self.user_defined_observation(self.aircraft_beaver.getState(), self.aircraft_beaver.z_dot_g_ks)
reward = self.compute_reward(self.aircraft_beaver.getState(), self.aircraft_beaver.z_dot_g_ks)
done = self.check_done(self.aircraft_beaver.getState())
# Headline: ab hier für plotten
self.plotter.addData(self.aircraft_beaver.getState(), self.aircraft_beaver.getForces(), self.aircraft_beaver.getMoments(),
self.aircraft_beaver.alpha, self.aircraft_beaver.beta,
np.rad2deg(
self.aircraft_beaver.getSteuerflaechenUndMotorStellung()),
self.anzahlSteps + self.anzahlEpisoden * 1000) # Headline ist anzupassen
self.plotter.add_data_xyz([self.aircraft_beaver.x_geo, self.aircraft_beaver.y_geo, self.aircraft_beaver.z_geo], self.aircraft_beaver.z_dot_g_ks, self.anzahlSteps + self.anzahlEpisoden * 1000)
self.plotter.add_data_Ziel(self.targetValues['targetSpeed'], self.anzahlSteps + self.anzahlEpisoden * 400)
return observation, reward, done, {}
def render(self, mode='human'):
self.udpClient.send((struct.pack('fff', self.aircraft_beaver.phi, 0, 0)))
def close(self):
pass
def seed(self, seed=None):
return
def user_defined_observation(self, aircraft_state_f_ks, z_dot_g_ks):
current_u = aircraft_state_f_ks[0]
error_current_u_to_target_u = current_u - self.targetValues['targetSpeed']
current_theta_grad = np.rad2deg(aircraft_state_f_ks[10])
user_defined_observation = np.asarray(
[current_u, error_current_u_to_target_u, z_dot_g_ks, current_theta_grad])
return user_defined_observation
def compute_reward(self, aircraft_state_f_ks, z_dot_g_ks):
reward0 = self.reward_elevator(aircraft_state_f_ks, z_dot_g_ks)
reward1 = self.reward_thrust(aircraft_state_f_ks)
return reward0, reward1
def reward_elevator(self, aircraft_state_f_ks, z_dot_g_ks):
current_theta_grad = np.rad2deg(aircraft_state_f_ks[10])
reward0 = 0
# out of bounds
if current_theta_grad < self.envelopeBounds['thetaMin_grad'] or current_theta_grad > self.envelopeBounds[
'thetaMax_grad']:
reward0 += -1000
# Zielgröße Sinken/steigen
if np.abs(self.targetValues['target_z_dot'] - z_dot_g_ks) > 1:
reward0 += -1
else:
reward0 += 100
return reward0
def reward_thrust(self, aircraft_state_f_ks):
current_u = aircraft_state_f_ks[0]
reward1 = 0
# out of bounds
if current_u < self.envelopeBounds['speedMin'] or current_u > self.envelopeBounds[
'speedMax']:
reward1 += -1000
# Zielgröße Sinken/steigen
if np.abs(self.targetValues['targetSpeed'] - current_u) > 1:
reward1 += -1
else:
reward1 += 100
return reward1
def check_done(self, observation):
done = 0
# conditions_if_reset = all( [30 <= observation[0] <= 50, 30 <= observation[1] <= 50])
if observation[0] < self.envelopeBounds['speedMin'] or observation[0] > self.envelopeBounds['speedMax']:
print("speed limits", observation[0])
done = 1
# conditions_if_reset_speed = any([observation[0] < 30, observation[0] > 50])
if np.rad2deg(observation[9]) < self.envelopeBounds['phiMin_grad'] or np.rad2deg(observation[9]) > \
self.envelopeBounds['phiMax_grad']:
print("roll limits", np.rad2deg(observation[9]))
done = 1
# conditions_if_reset_phi = any([self.envelopeBounds['phiMin'] > np.rad2deg(observation[9]), np.rad2deg(observation[9]) > self.envelopeBounds['phiMax']])
if np.rad2deg(observation[10]) < self.envelopeBounds['thetaMin_grad'] or np.rad2deg(observation[10]) > \
self.envelopeBounds['thetaMax_grad']:
print("pitch limits", np.rad2deg(observation[10]))
done = 1
return done
class WrapperOpenAI(gym.Env):
"""Custom Environment that follows gym interface"""
metadata = {'render.modes': ['human']}
def __init__(self, stepweite=0.02):
super(WrapperOpenAI, self).__init__()
# Define action and observation space
# They must be gym.spaces objects
# nur aileron
high_action_space = np.array([1.], dtype=np.float32)
self.action_space = spaces.Box(low=-high_action_space,
high=high_action_space,
dtype=np.float32)
# Zustandsraum 4 current_phi_dot, current_phi, abs(error_current_phi_target_phi), integration_error
high_observation_space = np.array([np.inf, np.inf], dtype=np.float32)
self.observation_space = spaces.Box(low=-high_observation_space,
high=high_observation_space,
dtype=np.float32)
# reward
self.reward_range = np.array([-np.inf, np.inf], dtype=np.float32)
# spezielle Parameter für das Enviroment FDM
self.aircraft_beaver = Aircraft_baever()
self.pid = PidRegler()
self.dynamicSystem = DynamicSystem6DoF()
self.stepweite = stepweite
self.udpClient = UdpClient('127.0.0.1', 5566)
# frage: ist das die richtige Stelle, oder besser im DDPG-Controller
self.targetValues = {
'targetPhi': 0,
'targetTheta': 0,
'targetPsi': 0,
'targetSpeed': 0
}
self.envelopeBounds = {
'phiMax': 20,
'phiMin': -20,
'thetaMax': 30,
'thetaMin': -30,
'speedMax': 72,
'speedMin': 30
}
self.observationErrorAkkumulation = np.zeros(3)
self.integration_error_stepsize_ = 0
# fuer plotten
self.plotter = PlotState()
self.anzahlSteps = 0
self.anzahlEpisoden = 0
self.servo_command = 0
self.action_servo_command_history = np.zeros(2)
self.bandbreite_servo_actions = 0
def reset(self):
self.observationErrorAkkumulation = np.zeros(3)
self.integration_error_stepsize_ = 0
self.servo_command = 0
self.action_servo_command_history = np.zeros(2)
self.bandbreite_servo_actions = 0
self.anzahlSteps = 1
self.anzahlEpisoden += 1
# set targets
self.targetValues['targetSpeed'] = np.random.uniform(47, 63)
print('new Target: ', self.targetValues)
# set state at initial
phi_as_random = np.deg2rad(np.random.uniform(0, 0))
theta_as_random = np.deg2rad(np.random.uniform(2.5, 3.5))
self.aircraft_beaver.setState(
np.array([
40.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, phi_as_random,
theta_as_random, 0.0
]))
observation = self.user_defined_observation(
self.aircraft_beaver.getState())
return observation # reward, done, info can't be included
def step(self, actionThrust):
self.anzahlSteps += 1
# action im Intervall [-1,1]
# mapping auf Begrenzung der Steuerflächen
self.servo_command = actionThrust[0]
self.aircraft_beaver.delta_thrust = np.interp(
actionThrust[0], [-1, 1],
[0.0, 1]) # Übersetzung der Ausgabe KNN zu Thrust-Setting
# Headline: theta wird mit PID-Regler stabilisiert
self.aircraft_beaver.delta_elevator = self.pid._innerLoopElevator(
np.deg2rad(0), self.aircraft_beaver.theta, self.aircraft_beaver.q,
self.aircraft_beaver.delta_elevator)
# Headline: phi wird mit PID-Regler stabilisiert
self.aircraft_beaver.delta_aileron = self.pid._innerLoopAileron(
np.deg2rad(0), self.aircraft_beaver.phi, self.aircraft_beaver.p,
self.aircraft_beaver.delta_aileron)
# Headline: integrate step
solver = self.dynamicSystem.integrate(
self.aircraft_beaver.getState(), self.aircraft_beaver.getForces(),
self.aircraft_beaver.getMoments(), self.aircraft_beaver.mass,
self.aircraft_beaver.inertia, self.stepweite
) # def integrate(self, state, mass, inertia, forces, moments, stepweite):
# State1
self.aircraft_beaver.setState(
np.array([
solver.y[0][0], solver.y[1][0], solver.y[2][0], solver.y[3][0],
solver.y[4][0], solver.y[5][0], solver.y[6][0], solver.y[7][0],
solver.y[8][0], solver.y[9][0], solver.y[10][0],
solver.y[11][0]
]))
observation = self.user_defined_observation(
self.aircraft_beaver.getState())
reward = self.compute_reward(self.aircraft_beaver.getState())
done = self.check_done(self.aircraft_beaver.getState())
# Headline: ab hier für plotten
self.plotter.addData(
self.aircraft_beaver.getState(), self.aircraft_beaver.getForces(),
self.aircraft_beaver.getMoments(), self.aircraft_beaver.alpha,
self.aircraft_beaver.beta,
(self.aircraft_beaver.getSteuerflaechenUndMotorStellung()),
self.anzahlSteps +
self.anzahlEpisoden * 1000) #Headline ist anzupassen
return observation, reward, done, {}
def render(self, mode='human'):
self.udpClient.send((struct.pack('fff', self.aircraft_beaver.phi, 0,
0)))
def close(self):
pass
def seed(self, seed=None):
return
def user_defined_observation(self, aircraft_state_f_ks):
current_speed = aircraft_state_f_ks[0]
error_current_speed_to_target_speed = aircraft_state_f_ks[
0] - self.targetValues['targetSpeed']
self.action_servo_command_history = np.roll(
self.action_servo_command_history,
len(self.action_servo_command_history) - 1)
self.action_servo_command_history[-1] = self.servo_command
self.bandbreite_servo_actions = np.abs(
np.min(self.action_servo_command_history) -
np.max(self.action_servo_command_history))
aircraft_state_f_ks = np.asarray(
[current_speed, error_current_speed_to_target_speed])
return aircraft_state_f_ks
def compute_reward(self, aircraft_state_f_ks):
current_u = aircraft_state_f_ks[0]
reward1 = 0
if current_u < self.envelopeBounds[
'speedMin'] or current_u > self.envelopeBounds['speedMax']:
reward1 += -1000
if np.abs(current_u - self.targetValues['targetSpeed']) > 1:
reward1 += -1
else:
reward1 += 10
return reward1
def check_done(self, observation):
done = 0
#conditions_if_reset = all( [30 <= observation[0] <= 50, 30 <= observation[1] <= 50])
if observation[0] < 30 or observation[0] > 80:
print("speed limits")
done = 1
#conditions_if_reset_speed = any([observation[0] < 30, observation[0] > 50])
if self.envelopeBounds['phiMin'] > np.rad2deg(
observation[9]) or np.rad2deg(
observation[9]) > self.envelopeBounds['phiMax']:
print("roll limits", np.rad2deg(observation[9]))
done = 1
# conditions_if_reset_phi = any([self.envelopeBounds['phiMin'] > np.rad2deg(observation[9]), np.rad2deg(observation[9]) > self.envelopeBounds['phiMax']])
if self.envelopeBounds['thetaMin'] > np.rad2deg(
observation[10]) or np.rad2deg(
observation[10]) > self.envelopeBounds['thetaMax']:
print("pitch limits", np.rad2deg(observation[10]))
done = 1
return done
class WrapperOpenAI(gym.Env):
"""Custom Environment that follows gym interface"""
metadata = {'render.modes': ['human']}
def __init__(self, stepweite=0.01):
super(WrapperOpenAI, self).__init__()
# Define action and observation space
# They must be gym.spaces objects
# nur aileron
high_action_space = np.array([1.], dtype=np.float32)
self.action_space = spaces.Box(low=-high_action_space,
high=high_action_space,
dtype=np.float32)
# Zustandsraum 4 current_phi_dot, current_phi, abs(error_current_phi_target_phi), integration_error
high_observation_space = np.array([np.inf, np.inf, np.inf],
dtype=np.float32)
self.observation_space = spaces.Box(low=-high_observation_space,
high=high_observation_space,
dtype=np.float32)
# reward
self.reward_range = np.array([-np.inf, np.inf], dtype=np.float32)
# spezielle Parameter für das Enviroment FDM
self.aircraft = Aircraft_baever() # Ball oder C172
self.pid = PidRegler()
self.dynamicSystem = DynamicSystem6DoF()
self.stepweite = stepweite
self.udpClient = UdpClient('127.0.0.1', 5566)
# frage: ist das die richtige Stelle, oder besser im DDPG-Controller
self.targetValues = {
'targetPhi_grad': 0,
'targetTheta_grad': 0,
'targetPsi': 0,
'targetSpeed': 0,
'target_z_dot': 0.0
}
self.envelopeBounds = {
'phiMax_grad': 30,
'phiMin_grad': -30,
'thetaMax_grad': 30,
'thetaMin_grad': -30,
'speedMax': 72,
'speedMin': 33
}
# fuer plotten
self.plotter = PlotState()
self.anzahlSteps = 0
self.anzahlEpisoden = 0
self.observationErrorAkkumulation = np.zeros(3)
self.integration_error_stepsize_ = 0
self.servo_command = 0
self.action_servo_command_history = np.zeros(2)
self.bandbreite_servo_actions = 0
def reset(self):
np.random.seed()
self.observationErrorAkkumulation = np.zeros(3)
self.integration_error_stepsize_ = 0
self.servo_command = 0
self.action_servo_command_history = np.zeros(2)
self.bandbreite_servo_actions = 0
self.anzahlSteps = 1
self.anzahlEpisoden += 1
self.targetValues['targetPhi_grad'] = np.random.uniform(-20, 20)
print('new Target (deg): ', self.targetValues)
phi_as_random = np.deg2rad(np.random.uniform(-25, 25))
self.aircraft.setState(
np.array([
40.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, phi_as_random,
np.deg2rad(-1), 0.0
]))
observation = self.user_defined_observation(self.aircraft.getState())
return observation # reward, done, info can't be included
def step(self, actionAileron):
self.anzahlSteps += 1
# action im Intervall [-1,1]
# mapping auf Begrenzung der Steuerflächen
self.servo_command = actionAileron[0]
self.aircraft.delta_aileron = np.deg2rad(
np.clip(self.servo_command, -1, 1) *
(-15)) # im FDM Beaver ist das
# Headline: pitch wird mit PID-Regler stabilisiert
self.aircraft.delta_elevator = self.pid._innerLoopElevator(
np.deg2rad(6), self.aircraft.theta, self.aircraft.q,
self.aircraft.delta_elevator)
# Headline: integrate steps
for x in range(10):
solver = self.dynamicSystem.integrate(
self.aircraft.getState(), self.aircraft.getForces(),
self.aircraft.getMoments(), self.aircraft.mass,
self.aircraft.inertia, self.stepweite
) # def integrate(self, state, mass, inertia, forces, moments, stepweite):
# State1
self.aircraft.setState(
np.array([
solver.y[0][0], solver.y[1][0], solver.y[2][0],
solver.y[3][0], solver.y[4][0], solver.y[5][0],
solver.y[6][0], solver.y[7][0], solver.y[8][0],
solver.y[9][0], solver.y[10][0], solver.y[11][0]
]))
observation = self.user_defined_observation(self.aircraft.getState())
reward = self.compute_reward(self.aircraft.getState())
done = self.check_done(self.aircraft.getState())
# Headline: ab hier für plotten
self.plotter.addData(
self.aircraft.getState(), self.aircraft.getForces(),
self.aircraft.getMoments(), self.aircraft.alpha,
self.aircraft.beta,
np.rad2deg(self.aircraft.getSteuerflaechenUndMotorStellung()),
self.anzahlSteps +
self.anzahlEpisoden * 400) #Headline ist anzupassen
self.plotter.add_data_Ziel(
self.targetValues['targetPhi_grad'],
self.anzahlSteps + self.anzahlEpisoden * 400)
return observation, reward, done, {}
def render(self, mode='human'):
self.udpClient.send((struct.pack('fff', self.aircraft.phi, 0, 0)))
def close(self):
pass
def seed(self, seed=None):
return
def user_defined_observation(self, observation):
# return: current_phi_dot, current_phi, abs(error_current_phi_target_phi), integration_error
current_phi_dot = observation[6]
current_phi = np.rad2deg(observation[9])
error_current_phi_to_target_phi = current_phi - self.targetValues[
'targetPhi_grad']
self.observationErrorAkkumulation = np.roll(
self.observationErrorAkkumulation, 1)
self.observationErrorAkkumulation[-1] = (
error_current_phi_to_target_phi)
self.integration_error_stepsize_ = np.add.reduce(
self.observationErrorAkkumulation)
self.action_servo_command_history = np.roll(
self.action_servo_command_history,
len(self.action_servo_command_history) - 1)
self.action_servo_command_history[-1] = self.servo_command
command_minimum = np.min(self.action_servo_command_history)
command_maximum = np.max(self.action_servo_command_history)
self.bandbreite_servo_actions = np.abs(command_minimum -
command_maximum)
observation = np.asarray(
[current_phi_dot, current_phi, error_current_phi_to_target_phi])
return observation
def compute_reward(self, observation):
reward = 0
# exceeds bounds [-20, 20] -> -100
if np.rad2deg(observation[9]
) > self.envelopeBounds['phiMax_grad'] or np.rad2deg(
observation[9]) < self.envelopeBounds['phiMin_grad']:
reward += -1000
# Abweichung abs(target-current) > 1 -> -1
if (np.abs(
np.rad2deg(observation[9]) -
self.targetValues['targetPhi_grad'])) > 1.0:
reward += -1
if (np.abs(
np.rad2deg(observation[9]) -
self.targetValues['targetPhi_grad'])) < 1.0:
reward += 100
return reward
def check_done(self, observation):
done = 0
# conditions_if_reset = all( [30 <= observation[0] <= 50, 30 <= observation[1] <= 50])
if observation[0] < self.envelopeBounds['speedMin'] or observation[
0] > self.envelopeBounds['speedMax']:
print("speed limits", observation[0])
done = 1
# conditions_if_reset_speed = any([observation[0] < 30, observation[0] > 50])
if np.rad2deg(observation[9]) < self.envelopeBounds['phiMin_grad'] or np.rad2deg(observation[9]) > \
self.envelopeBounds['phiMax_grad']:
print("roll limits", np.rad2deg(observation[9]))
done = 1
# conditions_if_reset_phi = any([self.envelopeBounds['phiMin'] > np.rad2deg(observation[9]), np.rad2deg(observation[9]) > self.envelopeBounds['phiMax']])
if np.rad2deg(observation[10]) < self.envelopeBounds['thetaMin_grad'] or np.rad2deg(observation[10]) > \
self.envelopeBounds['thetaMax_grad']:
print("pitch limits", np.rad2deg(observation[10]))
done = 1
return done
from FDM.physikFDM.aircraft import Aircraft
from FDM.lfz_controlFDM.pid_regler import PidRegler
from FDM.servicesFDM.umrechnungen_koordinaten import UmrechnungenKoordinaten
from FDM.servicesFDM.plot_state import PlotState
from FDM.physikFDM.dynamic_system6DoF import *
from FDM.config import *
from FDM.servicesFDM.udp_client import UdpClient
from FDM.physikFDM.aircraft_beaver import Aircraft_baever
config = Config()
umrechnungenKoordinaten = UmrechnungenKoordinaten()
plotter = PlotState()
# todo: config anpassen
#aircraft = Aircraft(config.geometrieClass) # Ball oder C172
aircraft = Aircraft_baever()
pid = PidRegler()
dynamicSystem = DynamicSystem6DoF()
udpClient = UdpClient('127.0.0.1', 5566)
#zeitmessung für develop
import time
summe = 0
start = time.time()
#zeitmessung für develop
#todo: echtzeitanpassung über time und pause...
stepweite = 0.02
steps = 2000
# set initial conditions of a/c