In the present work we propose a novel MATLAB/Simulink based modeling and simulation environment for the design and rapid prototyping of state-of-the- art aircraft control systems. The 6 degrees of freedom nonlinear aircraft dynamics can be simulated with a preferred degree of accuracy, meaning that aerodynamic coefficients, stability derivatives [1], actuators and sensor dynamics, disturbances in measurements, wind gusts, control limits and other characteristics can be en- abled separately and with different models by simply changing MATLAB func- tions. The International Standard Atmosphere model [2] is implemented. The whole system is organized so that it is highly usable and configurable, in that any detail of the simulation can be easily changed. The possibility to visualize the simulation in a detailed 3D environment is obtained thanks to an interface with the open-source flight simulator Flight- Gear [3], [4], [5], and both the toolbox and the flight simulator support multiple platforms (MacOS, Linux, Windows). Also, different aircraft models can be eas- ily loaded from various sources like XML files, DATCOM files and even custom formats. We implemented a manual input interface that allows the user to use an external gamepad to control the aircraft on-line during the simulation, in or- der to validate the effectiveness of the implemented control laws from a pilot perspective. On the control side, various classical and modern control techniques [6], [7], [8] are already implemented for the control of longitudinal and lateral dynamics. With respect to existing simulation environments, here multiple controllers can run simultaneously so that a detailed behavior can be reproduced. For linear controllers, that are designed based on decoupled dynamics in specific trim con- ditions, control laws have been implemented and tested in order to provide a simulation far from the operation point where the controllers have been de- signed, and performance assessment can be conducted in order to analyze pos- sible interactions due the the full nonlinear dynamics of the model. Among the implemented control laws, there are pole-placement techniques, LQR/LQG tech- niques [9], LPV and Lyapunov based techniques. An optimization based planning framework is included in order to obtain feed-forward control laws that might consider states and actuators limits and can be used jointly with feedback con- trollers to track a desired path. In such planning framework, advanced features are included like variable mass due to fuel consumption and via-point tracking in the 6D environment. Tests have been conducted with a Boeing 747 aircraft model, see Figure 1. The resulting system illustrated in Figure 2 is distributed as a MATLAB Toolbox and is appealing for aerospace designers and didactic purposes. With respect to similar commercial platforms, see [10], [11], [12], this toolbox is dis- tributed as open-source software, hence users can modify any detail and con- tribute with aircraft models, physical functions, and advanced control laws. Key words: Aircraft control design, Aircraft dynamics simulation, Linear and nonlinear control
References 1. Robert K Heffley and Wayne F Jewell. Aircraft handling qualities data. 1972.
Mustafa Cavcar. The international standard atmosphere (isa). Anadolu University, Turkey, 30, 2000.
Daniel Ondriˇs and Rudolf Andoga. Aircraft modeling using matlab/flight gear interface. Acta Avionica, 15(27), 2013.
Istas F Nusyirwan. Engineering flight simulator using matlab, python and flight-gear. In Simtect. Melbourne Australia, 2011.
Mohammed Moness, Ahmed M Mostafa, Mohammed A Abdel-Fadeel, Ahmed I Aly, and A Al-Shamandy. Automatic control education using flightgear and matlab based virtual lab. In 8th International Conference on Electrical Engineering, pages 1157–1160, 2012.
Michael V Cook. Flight dynamics principles: a linear systems approach to aircraft stability and control. Butterworth-Heinemann, 2012.
Brian L Stevens, Frank L Lewis, and Eric N Johnson. Aircraft Control and Simula- tion: Dynamics, Controls Design, and Autonomous Systems. John Wiley & Sons, 2015.
Ashish Tewari. Advanced control of aircraft, spacecraft and rockets, volume 37. John Wiley & Sons, 2011.
Labane Chrif and Zemalache Meguenni Kadda. Aircraft control system using lqg and lqr controller with optimal estimation-kalman filter design. Procedia Engineer- ing, 80:245–257, 2014.
Adriano Bittar, Helosman V Figuereido, Poliana Avelar Guimaraes, and Alessan- dro Correa Mendes. Guidance software-in-the-loop simulation using x-plane and simulink for uavs. In Unmanned Aircraft Systems (ICUAS), 2014 International Conference on, pages 993–1002. IEEE, 2014.
YI Jenie and T Indriyanto. X-plane-simulink simulation of a pitch-holding auto- matic control system for boeing 747. In Indonesian-Taiwan Workshop, Bandung, Indonesia, 2006.
Aircraft control toolbox. http://www.psatellite.com/act/index.php. Princeton Satellite Systems.