Autonomous Flight Control for Multi-Rotor UAVs Flying at Low Altitude

Unmanned aerial vehicles (UAVs) at low altitude flight may significantly degrade their performance and the safety under wind disturbances and incorrect operations. This paper presents a robust control strategy for UAVs to achieve good performance of low altitude flight and disturbance rejection. First, a novel second-order hexacopter dynamics is established and the position tracking is translated to the altitude and the rotational angle tracking problem. An integrated control scheme is created to deal with the challenges faced by hexacopter at low altitude flight, in which the influence of near-ground threshold distance and the desired roll, pitch, and yaw are analyzed. Moreover, an improved flying altitude planner and an attitude planner for low altitude conditions are designed respectively to avoid the overturning risk due to the big reaction torque and external disturbances. Second, a sliding-mode-based altitude tracking controller and an attitude tracking controller are designed to reduce the tracking errors and improve the robustness of the system. Finally, the proposed control scheme is tested on simulation and experiment platforms of multi-rotor UAV to show the feasibility and accurate trajectory tracking at low altitude flight

System identification and nonlinear model predictive control with collision avoidance applied in Hexacopters UAVs

Accurate trajectory tracking is a critical property of unmanned aerial vehicles (UAVs) due to system nonlinearities, under-actuated properties and constraints. Specifically, the use of unmanned rotorcrafts with accuracy trajectory tracking controllers in dynamic environments has the potential to improve the fields of environment monitoring, safety, search and rescue, border surveillance, geology and mining, agriculture industry, and traffic control. Monitoring operations in dynamic environments produce significant complications with respect to accuracy and obstacles in the surrounding environment and, in many cases, it is difficult to perform even with state-of-the-art controllers. This work presents a nonlinear model predictive control (NMPC) with collision avoidance for hexacopters’ trajectory tracking in dynamic environments, as well as shows a comparative study between the accuracies of the Euler–Lagrange formulation and the dynamic mode decomposition (DMD) models in order to find the precise representation of the system dynamics. The proposed controller includes limits on the maneuverability velocities, system dynamics, obstacles and the tracking error in the optimization control problem (OCP). In order to show the good performance of this control proposal, computational simulations and real experiments were carried out using a six rotary-wind unmanned aerial vehicle (hexacopter—DJI MATRICE 600). The experimental results prove the good performance of the predictive scheme and its ability to regenerate the optimal control policy. Simulation results expand the proposed controller in simulating highly dynamic environments that showing the scalability of the controller

A benchmark for orientation control of a multirotor in a three degrees-of-freedom rotation structure

[EN] A fully equipped quadrotor is attached to a structure that allows free rotation without translation. Additionally, a set of MATLAB-Simulink® tools execute the flight controller programming and manage the real-time transmission of commands and flight states for the remote pilot. For this test bench a simulator is offered. It faithfully reproduces the behaviour of the real system in order to propose a benchmark on Control Engineering. This aims to control the quadrotor orientation described using the Euler angles. Thus the three control actions that attack the propulsion system must be generated taking into account the rotation speeds and angles that are estimated by the navigation system and the angle set points. During the performance tests, a modifiable supply voltage replaces the battery charge level and a control action emulates the height control, resulting in dierent operating points of the system as in a real flight. The simulator allows free setup of closed and open loop experiments for model identification tasks or analysing the control performance for dierent inputs and operating points. The final objective is to incorporate a control law that improves the behaviour given as a reference for a certain experiment. After a simulation, an evaluation function quantifies the dierences in tracking error and control action between the current control and the reference control for each degree of freedom. The main challenge is a narrow control bandwidth to govern a complex three-variable system.[ES] Un cuatrirrotor con todo el equipamiento de vuelo se encuentra fijado a una estructura que permite la rotación en el espacio sin desplazamiento. Además, un conjunto de herramientas software desarrolladas con MATLAB-Simulink® ejecutan la programación de su controladora y gestionan la transmisión en tiempo real de consignas y estados del vuelo pilotado remotamente. Para este banco de pruebas se ofrece un simulador que reproduce fielmente el comportamiento del sistema real con el fin de plantear un benchmark de Ingeniería de Control. El problema propuesto es controlar la orientación del mutirrotor definida por los ángulos de Euler. Para ello, deben generarse las tres acciones de control que atacan al sistema de propulsión, considerando las velocidades y ángulos que estima el sistema de navegacion y las consignas angulares. Para lograr un mayor realismo, en las pruebas de comportamiento se pueden modificar la tensión de alimentación, que simula el nivel de carga de la batería, y una acción de control que emula el control de la altura, lo que da lugar a diferentes puntos de operación. El simulador permite configurar experimentos en lazo abierto o cerrado, para tareas de identificación o para analizar el comportamiento de los controladores en diferentes puntos de operación y ante diferentes entradas. El objetivo final es incorporar una ley de control que mejore el comportamiento dado como referencia para cierto experimento. Tras una simulación, una función de evaluación cuantifica las diferencias en el error de seguimiento y en la acción de control entre el control actual y el de referencia para cada grado de libertad. El principal desafío es optimizar el reducido ancho de banda disponible para controlar un sistema dinámico complejo.Los autores agradecen la ayuda prestada por el Gobierno de La Rioja a través del proyecto de I+D ADER 2017-I-IDD00035, y por la Universidad de La Rioja a través de la Ayuda para la realización de Proyectos de Innovación Docente 2020 PID Nº 36 y la Ayuda a Grupos de Investigación REGI2020 /23.Rico-Azagra, J.; Gil-Martínez, M.; Rico, R.; Nájera, S.; Elvira, C. (2021). Benchmark de control de la orientación de un multirrotor en una estructura de rotación con tres grados de libertad. Revista Iberoamericana de Automática e Informática industrial. 18(3):265-276. https://doi.org/10.4995/riai.2021.14356OJS265276183Bejarano, G., Alfaya, J., Rodriguez, D., Ortega, M., Morilla, F., 2019. Control de un sistema de refrigeración. Visitado 27.03.2021. URL: http://www.dia.uned.es/∼fmorilla/CIC2019/Bigazzi, L., Gherardini, S., Innocenti, G., Basso, M., 2021. Development of non expensive technologies for precise maneuvering of completely autonomous unmanned aerial vehicles. Sensors (Switzerland) 21 (2), 1-24. https://doi.org/10.3390/s21020391Blasco, X., García-Nieto, S., Reynoso-Meza, G., 2012. Control autónomo del seguimiento de trayectorias de un vehículo cuatrirrotor. Simulación y evaluación de propuestas. Revista Iberoamericana de Automática e Informática Industrial 9 (2), 194-199. https://doi.org/10.1016/j.riai.2012.01.001Bo, G., Xin, L., Hui, Z., Ling, W., 2016. Quadrotor helicopter attitude control using cascade PID. In: Proceedings of the 28th Chinese Control and Decision Conference, CCDC 2016. pp. 5158-5163. https://doi.org/10.1109/CCDC.2016.7531919Chen, Y., Zhang, G., Zhuang, Y., Hu, H., 2019. Autonomous flight control for multi-rotor UAVs flying at low altitude. IEEE Access 7, 42614-42625. https://doi.org/10.1109/ACCESS.2019.2908205Ebeid, E., Skriver, M., Terkildsen, K. H., Jensen, K., Schultz, U. P., 2018. A survey of open-source UAV flight controllers and flight simulators. Microprocessors and Microsystems 61, 11-20. https://doi.org/10.1016/j.micpro.2018.05.002García-Sanz, M., Elso, J., 2007. Resultados del benchmark de diseño de controladores para el cabeceo de un helicóptero. Revista Iberoamericana de Automática e Informática Industrial 4 (4), 117-120. https://doi.org/10.1016/S1697-7912(07)70251-0Gil-Martínez, M., Rico-Azagra, J., 2015. Multi-rotor robust trajectory tracking. In: 2015 23rd Mediterranean Conference on Control and Automation, MED 2015 - Conference Proceedings. pp. 865-870. https://doi.org/10.1109/MED.2015.7158854González-Vargas, A., Serna-Ramírez, J., Fory-Aguirre, C., Ojeda-Misses, A., Cardona-Ordoñez, J., Tombé-Andrade, J., Soria-López, A., 2019. A low-cost, free-software platform with hard real-time performance for control engineering education. Computer Applications in Engineering Education 27 (2), 406-418. https://doi.org/10.1002/cae.22084Hancer, M., Bitirgen, R., Bayezit, I., 2018. Designing 3-DOF hardware-inthe-loop test platform controlling multirotor vehicles. IFAC-PapersOnLine 51 (4), 119-124. https://doi.org/10.1016/j.ifacol.2018.06.058Kangunde, V., Jamisola, R.S., J., Theophilus, E., 2021. A review on drones controlled in real-time. International Journal of Dynamics and Control.https://doi.org/10.1007/s40435-020-00737-5Khan, S., Jaffery, M. H., Hanif, A., Asif, M. R., 2017. Teaching tool for a control systems laboratory using a quadrotor as a plant in MATLAB. IEEE Transactions on Education 60 (4), 249-256. https://doi.org/10.1109/TE.2017.2653762Lim, H., Park, J., Lee, D., Kim, H., 2012. Build your own quadrotor: Opensource projects on unmanned aerial vehicles. IEEE Robotics and Automation Magazine 19 (3), 33-45. https://doi.org/10.1109/MRA.2012.2205629Lotufo, M., Colangelo, L., Perez-Montenegro, C., Canuto, E., Novara, C., 2019. UAV quadrotor attitude control: An ADRC-EMC combined approach. Control Engineering Practice 84, 13-22. https://doi.org/10.1016/j.conengprac.2018.11.002Madridano, A., Campos, S., Al-Kaff, A., García, F., Martín, D., Escalera, A., 2020. Vehículo aéreo no tripulado para vigilancia y monitorización de incendios. Revista Iberoamericana de Automática e Informática industrial 17 (3), 254-263. https://doi.org/10.4995/riai.2020.11806Mercader, P., Cánovas, C. D., Baños, A., 2019. Control PID multivariable de una caldera de vapor. Revista Iberoamericana de Automática e Informática Industrial 16 (1), 15-25. https://doi.org/10.4995/riai.2018.9034Morilla, F., Rodríguez, C., 2017. Control de una caldera de vapor. Visitado 27.03.2021. URL: http://www.dia.uned.es/∼fmorilla/CIC2017/Nájera, S., Rico-Azagra, J., Elvira, C., Gil-Martínez, M., 2019. Plataforma giroscópica realizada mediante impresión 3D para el control de actitud y orientación de UAVs multi-rotor. In: Actas de las XL Jornadas de Automática, Comité Español de Automática de la IFAC. pp. 317-323. https://doi.org/10.17979/spudc.9788497497169.317Nascimento, T. P., Saska, M., 2019. Position and attitude control of multi-rotor aerial vehicles: A survey. Annual Reviews in Control 48, 129-146. https://doi.org/10.1016/j.arcontrol.2019.08.004Rico, R., Maisterra, P., Gil-Martínez, M., Rico-Azagra, J., S., N., 2015. Identificación experimental de los parámetros de un cuatrirrotor. In: Actas de las XXXVI Jornadas de Automática, Comité Español de Automática de la 'IFAC. pp. 973-982.Rico-Azagra, J., Gil-Martínez, M., Rico, R., Maisterra, P., 2016a. Plataforma didáctica de bajo coste para el control de actitud y orientación de UAVs multi-rotor. In: Actas de las XXXVII Jornadas de Automática, Comité Español de Automática de la IFAC. pp. 989-997.Rico-Azagra, J., Gil-Martínez, M., Rico-Azagra, R., Maisterra, P., 2016b. Low-cost attitude estimation for a ground vehicle. Advances in Intelligent Systems and Computing 417, 121-132. https://doi.org/10.1007/978-3-319-27146-0Rico-Azagra, J., Rico, R., Maisterra, P., Gil-Martínez, M., 2015. Comparación de algoritmos de estimación de actitud. In: Actas de las XXXVI Jornadas de Automática, Comité Español de Automática de la IFAC. pp. 911-920.Romero, J. A., Sanchis, R., 2011. Benchmark para la evaluación de algoritmos de auto-ajuste de controladores PID. Revista Iberoamericana de Automática e Informática Industrial 8 (1), 112-117. https://doi.org/10.4995/RIAI.2011.01.13Rubí, B., Perez, R., Morcego, B., 2020. A survey of path following control strategies for UAVs focused on quadrotors. Journal of Intelligent and Robotic Systems: Theory and Applications 98 (2), 241-265. https://doi.org/10.1007/s10846-019-01085-zShakhatreh, H., Sawalmeh, A. H., Al-Fuqaha, A., Dou, Z., Almaita, E., Khalil, I., Othman, N. S., Khreishah, A., Guizani, M., 2019. Unmanned aerial vehicles (UAVs): A survey on civil applications and key research challenges. IEEE Access 7, 48572-48634. https://doi.org/10.1109/ACCESS.2019.2909530Shraim, H., Awada, A., Youness, R., 2018. A survey on quadrotors: Configurations, modeling and identification, control, collision avoidance, fault diagnosis and tolerant control. IEEE Aerospace and Electronic Systems Magazine 33 (7), 14-33. https://doi.org/10.1109/MAES.2018.160246Shuster, M. D., 1993. Survey of attitude representations. Journal of the Astronautical Sciences 41 (4), 439-517.Sanchez-Fontes, E., Vilchis, J. A., Vilchis-González, A., Saldivar, B., Jacinto- 'Villegas, J., Martínez-Mendez, R., 2020. Nuevo vehículo aéreo autónomo estable por construcción: configuración y modelo dinámico. Revista Ibero-americana de Automática e Informática industrial 17 (3), 264-275. https://doi.org/10.4995/riai.2020.11603SolidWorks, 2018. Versión 2018. Dassault Systèmes S.A., Vélizy-Villacoublay, Francia.Wang, P., Man, Z., Cao, Z., Zheng, J., Zhao, Y., 2016. Dynamics modelling and linear control of quadcopter. In: International Conference on Advanced Mechatronic Systems, ICAMechS. Vol. 0. pp. 498-503. https://doi.org/10.1109/ICAMechS.2016.7813499Zhang, X., Li, X., Wang, K., Lu, Y., 2014. A survey of modelling and identification of quadrotor robot. Abstract and Applied Analysis 2014, Article ID 320526, 16 pages. https://doi.org/10.1155/2014/32052