Design an Aircraft Autopilot in Python using FlightGear

Maintain level flight by automatic wing leveling with a PID controller in a Python FlightGear setup, processing roll angle phi and adjusting ailerons through a feedback loop.

An inertial measurement unit combines accelerometer and gyroscope to measure linear acceleration on x, y, z and angular velocity for roll, pitch, yaw, using right-hand rule to define positive directions.

Explore how a PID controller uses roll angle phi and target roll angle of zero to compute roll error e(t) with proportional, integral, and derivative terms and update the aileron.

Learn how Python code implements a pid-based wing leveling autopilot: convert roll from radians to degrees, compute zero-setpoint roll error, and output bounded aileron commands at 100 Hz.

Tune pid controller coefficients (kp, ki, kd) by gradually increasing kp to reduce steady-state error without oscillations. Adjust ki to eliminate residual error, and fine-tune kd to dampen oscillations.

Explain how the Dutch roll emerges from yaw and roll coupling, amplified by adverse yaw during aileron inputs that create sideslips and alternating nose directions.

Learn how coordinated turns use rudder with ailerons to counter adverse yaw and prevent dutch roll, aligning the nose with the turn while rolling the aircraft.

Coordinate ailerons and rudder to achieve the yaw rate target from roll angle, using gravity times tan(roll angle) (ignoring airspeed) and a proportional controller with yaw-rate error scaled by Kp.

Use python code to level the aircraft and perform coordinated turns with flight gear data, converting yaw and roll from radians to degrees and commanding ailerons and rudder with kp.

Set a target altitude of 5000 ft and use a proportional controller to adjust throttle, holding altitude by restricting throttle output between zero and one.

Demonstrates altitude hold with a proportional controller in python using FlightGear, converting altitude from meters to feet and maintaining a 5000 ft set point with throttle adjustments.

Explore how waypoint following enables automated navigation, guiding an aircraft through a list of GPS coordinates by tracking position, computing distance and heading, and adjusting roll toward the next waypoint.

Explore how latitude and longitude locate points, by World Geodetic System since 1984, with latitude north positive and south negative, and longitude east positive and west negative from Greenwich.

Calculate the aircraft to waypoint distance with the haversine formula, using delta l, delta g, and Earth radius to compute d in meters.

Implement a Python function that computes haversine distance between two coordinates by converting latitudes and longitudes from degrees to radians and returning earth radius in meters times angular distance.

Compute the initial bearing from current location to the waypoint using arctan(y/x), with y and x defined by lat/long differences, then convert to a 0-360 final bearing.

Implement a bearing function in Python that accepts current and waypoint coordinates in degrees, converts them to radians, computes the initial bearing, and normalizes it to 0–360 degrees.

utilize a pid controller to align the aircraft heading with the waypoint bearing, computing heading error as bearing minus heading, wrap to ±180 degrees, and limit aileron input to ±1.

Define waypoints in a Python list with latitude and longitude, export as XML, and display them on the FlightGear map as navigational aids.

Define a list of waypoints and switch to the next target when the aircraft comes within 800m of the current waypoint, until the last waypoint is reached.

Design and implement automated waypoint navigation in Python with FlightGear, using FDM callbacks, yaw-based heading, a PID-controlled coordinated turn, and waypoint distance and bearing outputs.

This lecture demonstrates a python autopilot for automated waypoint navigation in FlightGear, outputting distance, heading error, yaw rate, and rudder commands while executing smooth coordinated turns through four waypoints.

Estimate roll and pitch from IMU data using sensor fusion, then level the aircraft with a PID controller for roll and set pitch to a set point with the elevator.

Understand how the IMU aligns with the aircraft body frame in FlightGear, and how gyroscope and accelerometer data yield roll, pitch, yaw rates (phi, theta, psi) and linear accelerations.

Learn to estimate aircraft roll and pitch from accelerometer data by computing arctan(y/z) for roll and arctan(-x/√(y^2+z^2)) for pitch, with gravity-based references and degree conversion for autopilot inputs.

Estimate roll and pitch from gyroscopic data by integrating rate with delta t (0.033 s at 30 Hz) to degrees for the Python FlightGear autopilot.

Combine gyroscope and accelerometer data with a complementary filter to estimate roll and pitch for an aircraft autopilot, balancing short-term accuracy and long-term drift using alpha weight, implemented in Python.

Estimate yaw with the gyroscope by integrating the yaw rate over delta t to obtain the yaw angle for the coordinated turn logic, using prior yaw; accelerometer cannot estimate yaw.

Integrate gyroscope and accelerometer data to estimate roll, pitch, and yaw using a complementary filter in a Python function, outputting roll/pitch in degrees and yaw in radians.

Apply pid controllers for roll and pitch to implement autopilot. Use a zero roll setpoint with roll error to adjust the aileron, and a proportional pitch controller for the elevator.

Use a Python autopilot that fuses FlightGear gyroscope and accelerometer data with a complementary filter to estimate roll, pitch, and yaw, driving the aircraft's leveling and coordinated turn.

Learn automated waypoint navigation using sensor fusion and pid control, where a complementary filter estimates roll and pitch and a Kalman filter fuses imu and gps for position.

Define north east down coordinate system as the standard navigation frame, contrast NAD with earth centered GPS, and explain x north, y east, and z down toward the earth center.

Explore the body frame system fixed to the aircraft, with x forward, y right, z downward, and relate roll, pitch, and yaw to onboard sensors relative to northeast down frame.

Convert accelerometer measurements from the body frame to the NAD frame using yaw, pitch, and roll rotations; apply the overall rotation matrix to obtain north, east, and down accelerations.

Learn how the Kalman filter fuses noisy sensor measurements to estimate an aircraft's state in real time. Explore its recursive prediction and correction steps using a physics-based model.

Fuse IMU and GPS with a Kalman filter; apply a complementary filter for roll/pitch, yaw from gyroscope; perform prediction and correction to estimate lat, lon, v_n, v_e, yaw.

Apply the Kalman filter prediction step to an aircraft, updating velocity and position from accelerations north and east via the state transition matrix, with covariance predicted by process noise.

Explain the Kalman filter update: compute the innovation matrix from predicted latitude, longitude, and jaw against GPS and gyroscope measurements; compute Kalman gain and update the state and covariance.

Implement a Kalman filter in Python by coding the prediction step with matrix products and the update step with the Kalman gain and innovation, updating the state and covariance.

Construct a rotation matrix from roll, pitch, and yaw to convert body-frame accelerations into the north, east, and downward components via the transform to NAD frame in python.

Deploy a kalman-filter based autopilot in python using FlightGear data to perform automated waypoint navigation with sensor fusion, updating latitude, longitude, and yaw while steering via coordinated turns.

Run python code in FlightGear to automate waypoint navigation using sensor fusion, track distance and bearing to each target, and adjust heading, yaw rate, and rudder for coordinated turns.

Apply the Kalman filter to fuse high-frequency IMU data with low-rate GPS readings, predicting aircraft state from accelerometer and gyroscope measurements and correcting it with GPS updates for reliable navigation.

Demonstrate how a Python Kalman filter enables autopilot sensor fusion by combining 30 Hz IMU data and 1 Hz GPS to navigate FlightGear waypoints with smooth heading corrections.