A Tutorial and Review on Flight Control Co-Simulation Using Matlab/Simulink and Flight Simulators

Abstract

Flight testing in a realistic three-dimensional virtual environment is increasingly being considered a safe and cost-effective way of evaluating aircraft models and their control systems. The paper starts by reviewing and comparing the most popular personal computer-based flight simulators that have been successfully interfaced to date with the MathWorks software. This co-simulation approach allows combining the strengths of Matlab toolboxes for functions including navigation, control, and sensor modeling with the advanced simulation and scene rendering capabilities of dedicated flight simulation software. This approach can then be used to validate aircraft models, control algorithms, handle flight characteristics, or perform model identification from flight data. There is, however, a lack of sufficiently detailed step-by-step flight co-simulation tutorials, and there have also been few attempts to evaluate more than one flight co-simulation approach at a time. We, therefore, demonstrate our own step-by-step co-simulation implementations using Simulink with three different flight simulators: Xplane, FlightGear, and Alphalink’s virtual flight test environment (VFTE). All three co-simulations employ a real-time user datagram protocol (UDP) for data communication, and each approach has advantages depending on the aircraft type. In the case of a Cessna-172 general aviation aircraft, a Simulink co-simulation with Xplane demonstrates successful virtual flight tests with accurate simultaneous tracking of altitude and speed reference changes while maintaining roll stability under arbitrary wind conditions that present challenges in the single propeller Cessna. For a medium endurance Rascal-110 unmanned aerial vehicle (UAV), Simulink is interfaced with FlightGear and with QGroundControl using the MAVlink protocol, which allows to accurately follow the lateral UAV path on a map, and this setup is used to evaluate the validity of Matlab-based six degrees of freedom UAV models. For a smaller ZOHD Nano Talon miniature aerial vehicle (MAV), Simulink is interfaced with the VFTE, which was specifically designed for this MAV, and with QGroundControl for the testing of advanced H-infinity observer-based autopilots using a software-in-the-loop (SIL) simulation to achieve robust low altitude flight under windy conditions. This is then finally extended to hardware-in-the-loop (HIL) implementation on the Nano Talon MAV using a controller area network (CAN) databus and a Pixhawk-4 mini autopilot with simulated sensor models.

Advancing UAV Communications: A Comprehensive Survey of Cutting-Edge Machine Learning Techniques

“…Incorporating these insights, MATLAB and Python, alongside Octave, Scilab, Julia, R, and the ROS framework, enrich the UAV simulation and analysis landscape. MATLAB's toolbox and Simulink platform allow for detailed modeling and simulation of UAV dynamics [78], while Python, with libraries like DroneKit, facilitates automation for simulation control and data processing. These tools extend beyond basic flight simulations to complex, mission-specific scenario test-ing, highlighting their indispensable roles in UAV technology development.…”

Comparison of Flight Parameters in SIL Simulation Using Commercial Autopilots and X-Plane Simulator for Multi-Rotor Models

“…This method allows developers to monitor and analyse system responses and quickly implement improvements. The existing literature has researched the integration of commercial autopilots with SIL simulations, such as the work presented in [11][12][13][14]. This approach was utilised by [15] to design a novel control structure that permits the explicit creation of tracking techniques for a system with an unchangeable autopilot in the loop.…”