def __init__(self, chapter):
self.chapter = chapter
# The current state of the UAV
self.state = np.array([
[P.Pn0], # (0)
[P.Pe0], # (1)
[P.Pd0], # (2)
[P.u0], # (3)
[P.v0], # (4)
[P.w0], # (5)
[P.e0], # (6)
[P.e1], # (7)
[P.e2], # (8)
[P.e3], # (9)
[P.p0], # (10)
[P.q0], # (11)
[P.r0]
]) # (12)
# The flight state of the UAV
self.flight_state = np.array([[P.Va0], [P.alpha0], [P.beta0],
[P.path_angle0], [P.heading0]])
# The forces that are applied to the body of the UAV
self.bodyForces = np.array([[P.Fx], [P.Fy], [P.Fz], [P.Ml], [P.Mm],
[P.Mn]])
# The state of the control surfaces
self.controlState = np.array([[P.deltaA0], [P.deltaE0], [P.deltaR0],
[P.deltaT0]])
# The commanded aircraft path following values
self.autopilot = np.array([[P.Va0], [P.altitude0], [P.heading0]])
# self.autopilot = np.matrix([[P.Va0],
# [P.altitude0],
# [3.14159]])
# The attitude of the UAV in euler angles (phi, theta, psi)
self.eulerAttitude = Quaternion2Euler(self.state[6:10])
self.euler_state = self.euler_states_array()
self.kinematics = Dynamics()
self.windsim = WindSimulation()
self.forces = Forces()
self.lat_control = lateral_control()
self.lon_control = longitudinal_control()
self.sensors = Sensors()
self.gps = Gps()
self.va = np.array([[P.u0], [P.v0], [P.w0]])
self.wind = np.array([[0], [0], [0]])
self.sensor_values = np.array([[0], [0], [0], [0], [0], [0], [0], [0]])
self.measured_gps = np.array([[0], [0], [0], [0], [0]])
self.kalman_attitude = self.eulerAttitude[0:2]
self.attitude_filter = AttitudeFilter(self.kalman_attitude)
self.kalman_loc = np.array([[0], [0], [P.u0], [0], [1], [1], [0]])
self.loc_filter = GPSFilter(self.kalman_loc)
self.sensor_LPF = SensorFilter(
np.array([[0.1], [0.1], [0.1], [0.81], [0.81], [0.81], [0.51],
[0.81]]))
def __init__(self, parent=None):
super(MAVModel, self).__init__(parent)
# QtCore.QThread.__init__(self)
# The current state of the UAV
# self.viewer = viewer
self.view_thread = Viewer()
# self.view_thread.draw_mav(self.Output())
self.view_thread.start()
self.state = np.array([
[P.Pn0], # (0)
[P.Pe0], # (1)
[P.Pd0], # (2)
[P.u0], # (3)
[P.v0], # (4)
[P.w0], # (5)
[P.e0], # (6)
[P.e1], # (7)
[P.e2], # (8)
[P.e3], # (9)
[P.p0], # (10)
[P.q0], # (11)
[P.r0]
]) # (12)
# The flight state of the UAV
self.flight_state = np.array([[P.Va0], [P.alpha0], [P.beta0],
[P.path_angle0], [P.heading0]])
# The forces that are applied to the body of the UAV
self.bodyForces = np.array([[P.Fx], [P.Fy], [P.Fz], [P.Ml], [P.Mm],
[P.Mn]])
# The state of the control surfaces
self.controlState = np.array([[P.deltaA0], [P.deltaE0], [P.deltaR0],
[P.deltaT0]])
# The commanded aircraft path following values
self.autopilot = np.array([[P.Va0], [P.altitude0], [P.heading0]])
# self.autopilot = np.matrix([[P.Va0],
# [P.altitude0],
# [3.14159]])
# The attitude of the UAV in euler angles (phi, theta, psi)
self.eulerAttitude = Quaternion2Euler(self.state[6:10])
self.euler_state = self.euler_states_array()
self.kinematics = Dynamics()
self.windsim = WindSimulation()
self.forces = Forces()
self.lat_control = lateral_control()
self.lon_control = longitudinal_control()
self.sensors = Sensors()
self.gps = Gps()
self.va = np.array([[P.u0], [P.v0], [P.w0]])
self.wind = np.array([[0], [0], [0]])
self.sensor_values = np.array([[0], [0], [0], [0], [0], [0], [0], [0]])
self.measured_gps = np.array([[0], [0], [0], [0], [0]])
self.kalman_attitude = self.eulerAttitude[0:2]
self.attitude_filter = AttitudeFilter(self.kalman_attitude)
self.kalman_loc = np.array([[0], [0], [P.u0], [0], [1], [1], [0]])
self.loc_filter = GPSFilter(self.kalman_loc)
self.sensor_LPF = SensorFilter(
np.array([[0.1], [0.1], [0.1], [0.81], [0.81], [0.81], [0.51],
[0.81]]))
self.signal_gen = Signals()
self.t_gps = 0.0
self.t_plot = 0.0
self.t_control = 0.0
self.t_elapse = 0.0
self.view_thread.init_plots(self.EulerStates(),
self.get_flight_state(),
np.linalg.norm(self.va), self.wind[0:2],
self.autopilot, self.sensor_values[3:8],
self.kalman_attitude, self.kalman_loc,
self.measured_gps)
class MAVModel(QWidget):
def __init__(self, parent=None):
super(MAVModel, self).__init__(parent)
# QtCore.QThread.__init__(self)
# The current state of the UAV
# self.viewer = viewer
self.view_thread = Viewer()
# self.view_thread.draw_mav(self.Output())
self.view_thread.start()
self.state = np.array([
[P.Pn0], # (0)
[P.Pe0], # (1)
[P.Pd0], # (2)
[P.u0], # (3)
[P.v0], # (4)
[P.w0], # (5)
[P.e0], # (6)
[P.e1], # (7)
[P.e2], # (8)
[P.e3], # (9)
[P.p0], # (10)
[P.q0], # (11)
[P.r0]
]) # (12)
# The flight state of the UAV
self.flight_state = np.array([[P.Va0], [P.alpha0], [P.beta0],
[P.path_angle0], [P.heading0]])
# The forces that are applied to the body of the UAV
self.bodyForces = np.array([[P.Fx], [P.Fy], [P.Fz], [P.Ml], [P.Mm],
[P.Mn]])
# The state of the control surfaces
self.controlState = np.array([[P.deltaA0], [P.deltaE0], [P.deltaR0],
[P.deltaT0]])
# The commanded aircraft path following values
self.autopilot = np.array([[P.Va0], [P.altitude0], [P.heading0]])
# self.autopilot = np.matrix([[P.Va0],
# [P.altitude0],
# [3.14159]])
# The attitude of the UAV in euler angles (phi, theta, psi)
self.eulerAttitude = Quaternion2Euler(self.state[6:10])
self.euler_state = self.euler_states_array()
self.kinematics = Dynamics()
self.windsim = WindSimulation()
self.forces = Forces()
self.lat_control = lateral_control()
self.lon_control = longitudinal_control()
self.sensors = Sensors()
self.gps = Gps()
self.va = np.array([[P.u0], [P.v0], [P.w0]])
self.wind = np.array([[0], [0], [0]])
self.sensor_values = np.array([[0], [0], [0], [0], [0], [0], [0], [0]])
self.measured_gps = np.array([[0], [0], [0], [0], [0]])
self.kalman_attitude = self.eulerAttitude[0:2]
self.attitude_filter = AttitudeFilter(self.kalman_attitude)
self.kalman_loc = np.array([[0], [0], [P.u0], [0], [1], [1], [0]])
self.loc_filter = GPSFilter(self.kalman_loc)
self.sensor_LPF = SensorFilter(
np.array([[0.1], [0.1], [0.1], [0.81], [0.81], [0.81], [0.51],
[0.81]]))
self.signal_gen = Signals()
self.t_gps = 0.0
self.t_plot = 0.0
self.t_control = 0.0
self.t_elapse = 0.0
self.view_thread.init_plots(self.EulerStates(),
self.get_flight_state(),
np.linalg.norm(self.va), self.wind[0:2],
self.autopilot, self.sensor_values[3:8],
self.kalman_attitude, self.kalman_loc,
self.measured_gps)
def calculate_loop(self):
if self.t_gps >= P.ts_gps:
self.update_gps()
self.t_gps = 0.0
if self.t_plot >= P.ts_plotting:
self.view_thread.updatePlots()
self.t_plot = 0.0
if self.t_control >= P.ts_control:
self.autopilot = self.signal_gen.getRefInputs(self.t_elapse)
self.control()
self.t_control = 0.0
for i in range(P.num_steps):
self.kalman_attitude = self.attitude_filter.predict(
P.ts_simulation / P.num_steps, self.sensor_values)
self.kalman_loc = self.loc_filter.predict(
P.ts_simulation / P.num_steps,
np.array([
self.sensor_values[7], self.sensor_values[4],
self.sensor_values[5], self.kalman_attitude[0],
self.kalman_attitude[1]
]),
)
self.sensor_values = self.sensors.sensor_values(
self.euler_state, self.bodyForces, self.va)
self.kalman_attitude = self.attitude_filter.update(
P.ts_simulation / P.num_steps, self.sensor_values)
self.update_wind()
self.update_flight_state()
self.bodyForces = self.forces.getForces(self.euler_state,
self.flight_state.item(0),
self.flight_state.item(1),
self.flight_state.item(2),
self.controlState)
for i in range(P.num_steps):
self.state = self.kinematics.PropagateDynamics(
self.state, self.bodyForces)
self.eulerAttitude = Quaternion2Euler(self.state[6:10])
self.euler_state = self.euler_states_array()
self.view_thread.update(self.Output())
self.view_thread.update_plot_data(self.EulerStates(),
self.get_flight_state(),
np.linalg.norm(self.va),
self.wind[0:2], self.autopilot,
self.sensor_values[3:8],
self.kalman_attitude,
self.kalman_loc, self.measured_gps)
self.t_gps += P.ts_simulation
self.t_plot += P.ts_simulation
self.t_control += P.ts_simulation
self.t_elapse += P.ts_simulation
def update_wind(self):
"""
Update the state of the wind in the simulation
:return:
"""
self.wind = self.windsim.getWind(self.state)
# self.wind = np.matrix([[0], [0], [0]])
# self.va = np.subtract(self.state[3:6], self.wind)
def control(self):
"""
Update the control surface commandes to obtain the commanded autopilot values
uses the controllers in the control file. PID on the lateral control and total
energy control on the longitudinal control
:return:
"""
measured_states = np.array([[self.kalman_loc[0]], [self.kalman_loc[1]],
[-self.sensor_values[6]], [0], [0], [0],
[self.kalman_attitude[0]],
[self.kalman_attitude[1]], [0],
[self.sensor_values[3]],
[self.sensor_values[4]],
[self.sensor_values[5]]])
measured_flight_state = np.array([
[self.sensor_values[7]], #va*
[0], #alpha
[0], #beta*
[0], #path angle
[self.kalman_loc[6]]
]) #course angle*
[delta_a, delta_r
] = self.lat_control.lateral_control_loop(measured_states,
self.autopilot.item(2),
measured_flight_state)
[delta_e, delta_t] = self.lon_control.longitudinal_control_loop(
measured_states, measured_flight_state, self.autopilot.item(1),
self.autopilot.item(0))
# [delta_a, delta_r] = self.lat_control.lateral_control_loop(self.euler_states_array(), self.autopilot.item(2),
# self.flight_state)
# [delta_e, delta_t] = self.lon_control.longitudinal_control_loop(self.euler_states_array(), self.flight_state,
# self.autopilot.item(1), self.autopilot.item(0))
self.controlState.itemset(0, delta_a)
self.controlState.itemset(1, delta_e)
self.controlState.itemset(2, delta_r)
self.controlState.itemset(3, delta_t)
# print(self.controlState.T)
def update_flight_state(self):
self.va = np.subtract(self.state[3:6], self.wind)
# print(np.linalg.norm(self.va))
# va_ground = rotations.body_2_vehicle(self.va, phi, theta, psi)
va_ground = rotations.body_2_inertial_quaternion(
self.va, self.state[6:10])
va_ground_mag = np.linalg.norm(va_ground)
# wind_ground = rotations.body_2_vehicle(self.wind, phi, theta, psi)
wind_ground = rotations.body_2_inertial_quaternion(
self.wind, self.state[6:10])
v_ground = va_ground + wind_ground
v_ground_mag = np.linalg.norm(v_ground)
ur = self.va.item(0)
vr = self.va.item(1)
wr = self.va.item(2)
alpha = np.arctan(wr / ur)
beta = np.arcsin(vr / va_ground_mag)
course_angle_ground = np.arctan2(v_ground.item(1), v_ground.item(0))
path_angle_ground = np.arcsin(
v_ground.item(2) / np.linalg.norm(v_ground))
self.flight_state = np.array([[v_ground_mag], [alpha], [beta],
[path_angle_ground],
[course_angle_ground]])
def update_gps(self):
self.measured_gps = self.gps.sensor_value(
[self.euler_state, self.wind, self.va])
self.kalman_loc = self.loc_filter.update(
np.array([
self.sensor_values[7], self.sensor_values[4],
self.sensor_values[5], self.kalman_attitude[0],
self.kalman_attitude[1]
]),
np.array([
self.measured_gps[0], self.measured_gps[1],
self.measured_gps[4], self.measured_gps[3], [1], [1]
]))
def Output(self):
"""
:return: The states that define the location and alltitude of the UAV in a list
"""
x = self.state.item(0)
y = self.state.item(1)
z = self.state.item(2)
roll = self.eulerAttitude.item(0)
pitch = self.eulerAttitude.item(1)
yaw = self.eulerAttitude.item(2)
return [x, y, z, roll, pitch, yaw]
def EulerStates(self):
"""
:return: list of the current state of the UAV in euler states
"""
set1 = self.state[0:6]
# set2 = Quaternion2Euler(self.state[6:10])
set2 = self.eulerAttitude
set3 = self.state[10:13]
ret = np.vstack((set1, set2, set3)).T.tolist()[0]
# print(ret)
return ret
def euler_states_array(self):
"""
Calculate the euler states for the UAV and return them in a numpy matrix
:return: matrix of the current state of the UAV in euler states
"""
set1 = self.state[0:6]
# set2 = Quaternion2Euler(self.state[6:10])
set2 = self.eulerAttitude
set3 = self.state[10:13]
ret = np.vstack((set1, set2, set3))
return ret
def get_flight_state(self):
"""
:return: the actual flight state
[va_mag, altitude, course_angle]
of the uav.
"""
flight_state = [
self.flight_state.item(0), -self.state.item(2),
self.flight_state.item(4)
]
return flight_state
def run(self):
# self.viewer.draw_mav(self.Output())
# self.view_thread.draw_mav(self.Output())
self.calculate_loop()
self.exec_()
class MAVModel:
def __init__(self, chapter):
self.chapter = chapter
# The current state of the UAV
self.state = np.array([
[P.Pn0], # (0)
[P.Pe0], # (1)
[P.Pd0], # (2)
[P.u0], # (3)
[P.v0], # (4)
[P.w0], # (5)
[P.e0], # (6)
[P.e1], # (7)
[P.e2], # (8)
[P.e3], # (9)
[P.p0], # (10)
[P.q0], # (11)
[P.r0]
]) # (12)
# The flight state of the UAV
self.flight_state = np.array([[P.Va0], [P.alpha0], [P.beta0],
[P.path_angle0], [P.heading0]])
# The forces that are applied to the body of the UAV
self.bodyForces = np.array([[P.Fx], [P.Fy], [P.Fz], [P.Ml], [P.Mm],
[P.Mn]])
# The state of the control surfaces
self.controlState = np.array([[P.deltaA0], [P.deltaE0], [P.deltaR0],
[P.deltaT0]])
# The commanded aircraft path following values
self.autopilot = np.array([[P.Va0], [P.altitude0], [P.heading0]])
# self.autopilot = np.matrix([[P.Va0],
# [P.altitude0],
# [3.14159]])
# The attitude of the UAV in euler angles (phi, theta, psi)
self.eulerAttitude = Quaternion2Euler(self.state[6:10])
self.euler_state = self.euler_states_array()
self.kinematics = Dynamics()
self.windsim = WindSimulation()
self.forces = Forces()
self.lat_control = lateral_control()
self.lon_control = longitudinal_control()
self.sensors = Sensors()
self.gps = Gps()
self.va = np.array([[P.u0], [P.v0], [P.w0]])
self.wind = np.array([[0], [0], [0]])
self.sensor_values = np.array([[0], [0], [0], [0], [0], [0], [0], [0]])
self.measured_gps = np.array([[0], [0], [0], [0], [0]])
self.kalman_attitude = self.eulerAttitude[0:2]
self.attitude_filter = AttitudeFilter(self.kalman_attitude)
self.kalman_loc = np.array([[0], [0], [P.u0], [0], [1], [1], [0]])
self.loc_filter = GPSFilter(self.kalman_loc)
self.sensor_LPF = SensorFilter(
np.array([[0.1], [0.1], [0.1], [0.81], [0.81], [0.81], [0.51],
[0.81]]))
def update_wind(self):
"""
Update the state of the wind in the simulation
:return:
"""
# self.wind = self.windsim.getWind(self.state)
self.wind = np.matrix([[0], [0], [0]])
# self.va = np.subtract(self.state[3:6], self.wind)
def control(self):
"""
Update the control surface commandes to obtain the commanded autopilot values
uses the controllers in the control file. PID on the lateral control and total
energy control on the longitudinal control
:return:
"""
[delta_a, delta_r
] = self.lat_control.lateral_control_loop(self.euler_states_array(),
self.autopilot.item(2),
self.flight_state)
[delta_e, delta_t] = self.lon_control.longitudinal_control_loop(
self.euler_states_array(), self.flight_state,
self.autopilot.item(1), self.autopilot.item(0))
# measured_states = np.array([[self.measured_gps[0]], [self.measured_gps[1]], [-self.sensor_values[6]],
# [0], [0], [0],
# [self.kalman_attitude[0]], [self.kalman_attitude[1]], [0],
# [self.sensor_values[3]], [self.sensor_values[4]], [self.sensor_values[5]]])
# measured_flight_state = np.array([[self.sensor_values[7]], #va*
# [0], #alpha
# [0], #beta*
# [0], #path angle
# [self.measured_gps[3]]]) #course angle*
# [delta_a, delta_r] = self.lat_control.lateral_control_loop(measured_states, self.autopilot.item(2),measured_flight_state)
# [delta_e, delta_t] = self.lon_control.longitudinal_control_loop(measured_states, measured_flight_state, self.autopilot.item(1), self.autopilot.item(0))
self.controlState.itemset(0, delta_a)
self.controlState.itemset(1, delta_e)
self.controlState.itemset(2, delta_r)
self.controlState.itemset(3, delta_t)
# print(self.controlState.T)
def UpdateState(self):
"""
Perform all necessary calculations to determine the next state of the uav
:return: nothing
"""
if self.chapter >= 7:
for i in range(P.num_steps):
self.kalman_attitude = self.attitude_filter.predict(
P.ts_simulation / P.num_steps, self.sensor_values)
self.kalman_loc = self.loc_filter.predict(
P.ts_simulation / P.num_steps,
np.array([
self.sensor_values[7], self.sensor_values[4],
self.sensor_values[5], self.kalman_attitude[0],
self.kalman_attitude[1]
]),
)
self.sensor_values = self.sensors.sensor_values(
self.euler_state, self.bodyForces, self.va)
self.kalman_attitude = self.attitude_filter.update(
P.ts_simulation / P.num_steps, self.sensor_values)
# self.sensor_values = self.sensor_LPF.filter(self.sensor_values)
if self.chapter >= 4:
# Chapter 4: calculate forces based on the control surface positions
# eulerState = np.vstack((self.state[0:6], self.eulerAttitude, self.state[10:]))
# wind = self.wind.getWind(eulerState)
self.update_wind()
self.update_flight_state()
self.bodyForces = self.forces.getForces(self.euler_state,
self.flight_state.item(0),
self.flight_state.item(1),
self.flight_state.item(2),
self.controlState)
# self.bodyForces = self.forces.getForces(eulerState, airspeed, alpha, beta, self.controlState)
if self.chapter >= 3:
# Chapter 3: Propagate the dynamics
for i in range(P.num_steps):
self.state = self.kinematics.PropagateDynamics(
self.state, self.bodyForces)
self.eulerAttitude = Quaternion2Euler(self.state[6:10])
self.euler_state = self.euler_states_array()
# self.kalman_attitude = self.attitude_filter.predict_next_step(P.ts_simulation/P.num_steps,
# self.sensor_values[0:4])
# Chapter 2: Do nothing
return
def update_flight_state(self):
self.va = np.subtract(self.state[3:6], self.wind)
# print(self.va.T)
# va_ground = rotations.body_2_vehicle(self.va, phi, theta, psi)
va_ground = rotations.body_2_inertial_quaternion(
self.va, self.state[6:10])
va_ground_mag = np.linalg.norm(va_ground)
# wind_ground = rotations.body_2_vehicle(self.wind, phi, theta, psi)
wind_ground = rotations.body_2_inertial_quaternion(
self.wind, self.state[6:10])
v_ground = va_ground + wind_ground
v_ground_mag = np.linalg.norm(v_ground)
ur = self.va.item(0)
vr = self.va.item(1)
wr = self.va.item(2)
alpha = np.arctan(wr / ur)
beta = np.arcsin(vr / va_ground_mag)
course_angle_ground = np.arctan2(v_ground.item(1), v_ground.item(0))
path_angle_ground = np.arcsin(
v_ground.item(2) / np.linalg.norm(v_ground))
self.flight_state = np.array([[v_ground_mag], [alpha], [beta],
[path_angle_ground],
[course_angle_ground]])
def update_gps(self):
self.measured_gps = self.gps.sensor_value(
[self.euler_state, self.wind, self.va])
self.kalman_loc = self.loc_filter.update(
np.array([
self.sensor_values[7], self.sensor_values[4],
self.sensor_values[5], self.kalman_attitude[0],
self.kalman_attitude[1]
]),
np.array([
self.measured_gps[0], self.measured_gps[1],
self.measured_gps[4], self.measured_gps[3], [1], [1]
]))
# print(self.measured_gps)
def get_flight_state(self):
"""
:return: the actual flight state
[va_mag, altitude, course_angle]
of the uav.
"""
flight_state = [
self.flight_state.item(0), -self.state.item(2),
self.flight_state.item(4)
]
return flight_state
def Output(self):
"""
:return: The states that define the location and alltitude of the UAV in a list
"""
x = self.state.item(0)
y = self.state.item(1)
z = self.state.item(2)
roll = self.eulerAttitude.item(0)
pitch = self.eulerAttitude.item(1)
yaw = self.eulerAttitude.item(2)
return [x, y, z, roll, pitch, yaw]
def States(self):
"""
:return: All the current states of the UAV in a list
"""
return self.state.T.tolist()[0]
def EulerStates(self):
"""
:return: list of the current state of the UAV in euler states
"""
set1 = self.state[0:6]
# set2 = Quaternion2Euler(self.state[6:10])
set2 = self.eulerAttitude
set3 = self.state[10:13]
ret = np.vstack((set1, set2, set3)).T.tolist()[0]
# print(ret)
return ret
def euler_states_array(self):
"""
Calculate the euler states for the UAV and return them in a numpy matrix
:return: matrix of the current state of the UAV in euler states
"""
set1 = self.state[0:6]
# set2 = Quaternion2Euler(self.state[6:10])
set2 = self.eulerAttitude
set3 = self.state[10:13]
ret = np.vstack((set1, set2, set3))
return ret