HILS of FPV-2600 UAV using MyRIO-1950 as Optimal Flight Control System
Abstract
Hardware in the Loop Simulation (HILS) system was successfully implemented to the embedded MyRIO-1950 on board as the flight control system (FCS) in FPV-2600 UAV modeling in X-Plane flight simulator. The modeling is carried out step by step using the Loop Simulation (SILS) and HILS software. In the SILS step, Labview and X-Plane succeeded in combining data communication via User Datagram Protocol (UDP) and controlling the vehicle to autopilot by waypoints mode. The subsequent development is to move the whole SILS results program into HILS, which involves software and hardware directly by combining the Predictive Control Model (MPC) as a linear simulation control model and PID as classical control, successfully controlling the FPV-2600 in a flight mode simulation in manual, stability and autopilot by waypoints. The simulation is done by doing a flight test manually and stability directly using remote control manually and stability using the remote control to analyze flight performance and vehicle stability. Furthermore, the simulation of autopilot by waypoints by tuning the MPC’s predictive and control horizon is related to the inner loop control on the roll and pitch, and the PID gain tuning is related to the altitude and the waypoints target. In this simulation, MyRIO-1950 as hardware can be used as a real-time simulation control for MPC and PID integrated into HILS, and this will be very useful for initial procedural reference before flying the FPV-2600 in the actual flight test.
Keywords
Full Text:
PDFReferences
Pandey A.K., Chaudhary T., Mishra S., Verma S. Longitudinal Control of Small Unmanned Aerial Vehicle by PID Controller. In: Advances in Intelligent Systems and Computing, 2018, vol 624, p. 923–31. Springer, Singapore, doi.org/10.1007/978-981-10-5903-2_97
Tang W., Wang L., Gu J., Gu Y. Single neural adaptive PID control for small UAV micro-turbojet engine, 2020, Sensors (Switzerland), 20 (2), art. no. 345, doi.org/10.3390/s20020345
Muhammad Fajar, Ony Arifianto. The Design of The Lateral-Directional AutoPilot for the LSU-05 Unmanned Aerial Vehicle. Jurnal Teknologi Dirgantara, 2017, vol. 15(2), p. 93-104.
I. E. Putro, R. A. Duhri. Longitudinal Stability Augmentation Control for Turbojet UAV Based on Linear Quadratic Regulator (LQR) Approach. AIP Conference Proceedings, 2020, 2226(1), doi.org/10.1063/5.0002786
Lee S, Lee J, Lee S, Choi H, Kim Y, Kim S, et al. Sliding Mode Guidance and Control for UAV Carrier Landing. IEEE Trans Aerosp Electron Syst. 2019;55(2):951–66, doi: 10.1109/TAES.2018.2867259.
Jemie Muliadi, Benyamin Kusumoputro. Neural Network Control System of UAV Altitude Dynamics and Its Comparison with the PID Control System. Hindawi Journal of Advanced Transportation, 2018, doi.org/10.1155/2018/3823201
Pengkai Ru, Kamesh Subbarao. Nonlinear Model Predictive Control for Unmanned Aerial Vehicles. MDPI Journal, Aerospace 2017, 4(2), 31; https://doi.org/10.3390/aerospace4020031.
Abubakar Surajo Imam, Robert Bicke. Quadrotor Model Predictive Flight Control System. International Journal of Current Engineering and Technology. 2014; Vol.4, No.1 (2014): 355-365.
Schacht-RodrÃguez R, Ortiz-Torres G, GarcÃa-Beltrán CD, Astorga-Zaragoza CM, Ponsart JC, Pérez-Estrada AJ. Design and development of a UAV Experimental Platform. IEEE Latin America Transactions. 2018; 16(5): 1320–1327, doi: 10.1109/TLA.2018.8408423.
Boyang Li, Weifeng Zhou, Jingxuan Sun, Chih-Yung Wen, Chih-Keng Chen. Development of Model Predictive Controller for a Tail-Sitter VTOL UAV in Hover Flight. Sensors (Basel). 2018; 18(9): 2859, doi: 10.3390/s18092859.
Michailidis MG, Agha M, Rutherford MJ, Valavanis KP. A software in the loop (SIL) kalman and complementary filter implementation on X-Plane for UAVs. In: 2019 International Conference on Unmanned Aircraft Systems, ICUAS 2019. 2019. p. 1069–76, doi: 10.1109/ICUAS.2019.8797942.
Bittar A, Figuereido H V., Guimaraes PA, Mendes AC. Guidance software-in-the-loop simulation using x-plane and simulink for UAVs. In: 2014 International Conference on Unmanned Aircraft Systems, ICUAS 2014 - Conference Proceedings. 2014. p. 993–1002, doi: 10.1109/ICUAS.2014.6842350.
Kaviyarasu A, Saravanakumar A, Loga Venkatesh M. Hardware in loop simulation of a way point navigation using matlab/simulink and x-plane simulator. In: Proceedings of the International Conference on Intelligent Sustainable Systems, ICISS 2019. 2019. p. 332–5, doi: 10.1109/ISS1.2019.8908103.
HY Irwanto. Development of Autonomous Controller System of High-Speed UAV from Simulation to Ready to Fly Condition. Journal of Physics: Conference Series. 2018; 962(1): 012015, doi :10.1088/1742-6596/962/1/012015
HY Irwanto, E. Artono. Correlation of Hardware in the loop Simulation (HILS) and Real Control Vehicle Flight Test for Reducing Flight Failures. Journal of Physics: Conference Series. 2018; 1130(1): 012014, doi :10.1088/1742-6596/1130/1/012014
H. Y. Irwanto, Increase maneuver performance of high-speed UAV, 2017 International Seminar on Sensors, Instrumentation, Measurement and Metrology (ISSIMM), IEEE 2017, pp. 17-20, doi: 10.1109/ISSIMM.2017.8124253.
UAV Autonomous Navigation by Data Fusion and FPGA. Mecánica Computacional. 2019;37(16):609–18.
Zermani S, Dezan C, Euler R. Embedded decision making for UAV missions. In: 2017 6th Mediterranean Conference on Embedded Computing, MECO 2017 - Including ECYPS 2017, Proceedings. 2017, doi: 10.1109/MECO.2017.7977165
Cheng H, Yang Y. Model predictive control and PID for path following of an unmanned quadrotor helicopter. In: Proceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017. 2018. p. 768–73, doi: 10.1109/ICIEA.2017.8282943
KlauÄo M, Kvasnica M. Model predictive control. In: Advances in Industrial Control. 2019. p. 15–34.
DOI: http://dx.doi.org/10.18517/ijaseit.11.5.11919
Refbacks
- There are currently no refbacks.
Published by INSIGHT - Indonesian Society for Knowledge and Human Development