Published at : 06 Oct 2021
Volume : IJtech Vol 12, No 4 (2021)
DOI : https://doi.org/10.14716/ijtech.v12i4.3928
|M Ary Heryanto||Department of Electrical Engineering, Faculty of Engineering, Universitas Dian Nuswantoro, Semarang 50131, Indonesia|
|Benyamin Kusumoputro||Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia|
this study, a neural network–based direct inverse control (DIC) approach was
developed and simulated to control various maneuvers of an unmanned aerial
vehicle (UAV) quadcopter. The aim was to propose an inner loop control
algorithm for UAV quadcopter maneuvers using a neural network–based DIC system.
The appropriate connection weights of neurons in the controller were determined
through a backpropagation learning algorithm using real quadcopter maneuver
flight data. The neural network–based DIC was trained and then tested using a
trajectory dataset different from the training dataset. The experimental
results showed that the neural network–based DIC could follow the maneuvers of
the testing trajectory dataset with excellent performance, as indicated by an
overall mean squared error (MSE) of 1.461 and attitude MSEs of 3.104 for roll,
0.889 for pitch, 1.834 for yaw and 0.018 for altitude. These results indicate
that the proposed artificial neural network–based DIC can be used to control
the attitude and altitude of the quadcopter during maneuvers.
Backpropagation; Direct inverse control; Maneuver; Neural Network; Quadcopter
Unmanned aerial vehicle (UAV) research has grown rapidly in recent years in both the civilian and military sectors (Duan et al., 2010; Gandhi and Ghosal, 2018; Krishnan et al., 2018; Nenni et al., 2020). As multirotor drones, UAV quadcopters have several advantages, including the ability to perform vertical takeoff and landing, high maneuverability, and simple mechanical structures. Quadcopters are under-actuated, with strong coupling and highly nonlinear systems (Gheorghita et al., 2015; Wang et al., 2016); therefore, controlling them is a considerable challenge.
UAV quadcopters are increasingly attracting the attention of researchers, and developing an autonomous quadcopter control method is of central importance. Numerous studies have been conducted to address the issue of quadcopter control. Proportional integral derivative (PID), LQR, backstepping, and sliding mode control are the most widely used methods (Argentim et al., 2013; Tripathi et al., 2015; Wang et al., 2016; Najm and Ibraheem, 2019; Nguyen et al., 2019).
neural network (NN)-based control systems have been developed with decent
et al., 2009; Xianglei et al., 2011;
Suprapto et al., 2017; Muliadi &
Kusumoputro, 2018; Yuning et al., 2019; Mahadika et al., 2020).
Like other aerial vehicles, quadcopters have abilities of six degrees of freedom—pitch, roll, yaw, and x, y, z—but only four basic movements are generated directly—namely, roll, pitch, yaw, and thrust (Wang et al., 2016). Roll and pitch are related to changes in the quadcopter’s position on the x and y axes, and yaw is related to rotational motion on the z axis. Roll, pitch, and yaw produce attitude movement, while thrust is related to changes in altitude movement. Since the quadcopter movement depends entirely on these four basic motions, it is extremely important to maintain stable attitude (pitch, roll, and yaw) and altitude control conditions, especially during maneuvers.
Our research is based on a cross configuration of a quadcopter constructed in the Computational Intelligence and Intelligent Systems Laboratory, Universitas Indonesia. We previously also developed an NN-DIC scheme as a control system and optimized it to easily control the hovering state of the quadcopter (Heryanto et al., 2015). Our results showed that the optimized NN-DIC system improved the controller’s performance, as indicated by a faster settling time compared with that of a non-optimized NN-DIC method. We also confirmed in subsequent experiments (Heryanto et al., 2017) that the NN-DIC system can maintain the quadcopter’s attitude and altitude in a simple UAV simulation flight with reasonable errors. Based on these results, in this study, we aimed to investigate the response of attitude and altitude control using our developed NN-DIC method on a maneuvering trajectory flight of a quadcopter.
The rest of this paper is organized as follows. Section 2 explains the structure and dynamics of the quadcopter. Section 3 describes the NN-DIC strategy and the data acquisition method. Section 4 presents the simulation results of the experiment. Section 5 concludes the paper.
purpose of this study was to continue our research on exploring the
capabilities of NN-DIC applied to a quadcopter under maneuver conditions. The
NN-DIC system succeeded in maintaining the quadcopter’s trajectory. The
experimental results also showed that the quadcopter could follow the
trajectory altitude with a very small MSE (0.018), as well as some inaccuracy
in attitude. These results indicate that the NN-DIC system can control a
quadcopter under maneuver conditions, such as clockwise/counterclockwise
up-and-down helix movements. A full quadcopter control system is being
developed and will be presented in the near future.
authors would like to express their gratitude to the Ministry of Research,
Technology, and Higher Education of Indonesia for supporting this work through
a Universitas Indonesia Scholarship Research Grant number 1251.38/E4.4/2012. We
also thank Universitas Dian Nuswantoro.
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