Attitude and Altitude Control of Quadcopter Maneuvers using Neural Network–Based Direct Inverse Control

In 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).

 Moreover, neural network (NN)-based control systems have been developed with decent performance (Anuradha 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.

      The 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.

    The 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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