Carlos Tong, Samuel Huaman, Jose Oliden, Guillermo Kemper

Proceedings of the 26th World Multi-Conference on Systemics, Cybernetics and Informatics: WMSCI 2022, Vol. I, pp. 97-102 (2022); https://doi.org/10.54808/WMSCI2022.01.97

Carlos Tong
Escuela de Ingeniería Mecatrónica, Universidad Nacional de Ingeniería, Lima, Peru

Samuel Huaman
Instituto Nacional de Investigación y Capacitación de Telecomunicaciones, Universidad Nacional de Ingeniería, Lima, Peru

Jose Oliden
Escuela de Ingeniería Mecatrónica, Universidad Nacional de Ingeniería, Lima, Peru

Guillermo Kemper
Instituto Nacional de Investigación y Capacitación de Telecomunicaciones, Universidad Nacional de Ingeniería, Lima, Peru

Cite this paper as:
Tong, C., Huaman, S., Oliden, J., Kemper, G. (2022). Conditioning a Control Model Based on Backstepping Algorithm for a Fixed Wing UAV Including Flight Perturbances for Crops Monitoring. In N. Callaos, N. Lace, B. Sánchez, M. Savoie (Eds.), Proceedings of the 26th World Multi-Conference on Systemics, Cybernetics and Informatics: WMSCI 2022, Vol. I, pp. 97-102. International Institute of Informatics and Cybernetics. https://doi.org/10.54808/WMSCI2022.01.97

This work proposes an adapted control model, based on a Backstepping algorithm, for position and attitude control of a fixed wing UAV, simulating its flight including perturbances for crops monitoring. Current works have focused on modelling the perturbances and how those approximately affect to controllers for this type of aircraft. The purpose of this work is to model and include perturbances that simulate an environment as closest to reality as possible and evaluate the performance of the controller in order to check if it is feasible to implement it on a real UAV. This paper will present the original model and contrast it with the modified models including the perturbances. A Gaussian model has been introduced to simulate the positioning sensors noise (from GPS and accelerometer), a Dryden Wind Model has been used to simulate the wind turbulence and a PWM model with low pass filters to simulate actuators noise. The results include tests that evaluate system performance taking into account multiple flight scenarios including takeoff, gliding and landing, with the previously mentioned perturbance models resulting in quantified errors. The position error values for takeoff, landing and gliding are between 4-5 m.