• International Journal of Technology (IJTech)
  • Vol 12, No 4 (2021)

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

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

Title: Attitude and Altitude Control of Quadcopter Maneuvers using Neural Network–Based Direct Inverse Control
M Ary Heryanto, Benyamin Kusumoputro

Corresponding email:


Cite this article as:
Heryanto, M.A., Kusumoputro, B., 2021. Attitude and Altitude Control of Quadcopter Maneuvers using Neural Network–Based Direct Inverse Control. International Journal of Technology. Volume 12(4), pp. 843-853

35
Downloads
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
Email to Corresponding Author

Abstract
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

Introduction

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.

Conclusion

      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.

Acknowledgement

    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.

References

Alkamachi, A., Erçelebi, E., 2017. Modelling and Genetic Algorithm Based-PID Control of H-Shaped Racing Quadcopter. Arabian Journal for Science and Engineering, Volume 42(7), pp. 2777–2786

Anuradha, D.B., Reddy, G.P., Murthy, J.S.N., 2009. Direct Inverse Neural Network Control of a Continuous Stirred Tank Reactor (CSTR). In: Proceedings of the International MultiConference of Engineers and Computer Scientists, Volume 3

Argentim, L.M. Rezende, W.C., Santos, P.E., Aguiar, R.A., 2013. PID, LQR and LQR-PID on a Quadcopter Platform. In: 2013 International Conference on Informatics, Electronics and Vision (ICIEV), pp. 1–6

Bresciani, T., 2008. Modelling, Identification and Control of a Quadrotor Helicopter. Master's Thesis, Graduate Program, Lund University, Sweden

Duan, H., Shao, S., Su, B., Zhang, L., 2010. New Development Thoughts on the Bio-inspired Intelligence Based Control for Unmanned Combat Aerial Vehicle. Science China Technological Sciences, Volume 53(8), pp. 2025–2031

Gandhi, D.A., Ghosal, M., 2018. Novel Low Cost Quadcopter for Surveillance Application. In: 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), pp. 412–414

Gheorghita, D., Vîntu, I., Mirea, L., Br?escu, C., 2015. Quadcopter Control System. In: 2015 19th International Conference on System Theory, Control and Computing, ICSTCC 2015, pp. 421–426

Heryanto, M.A., Suprijono, H., Suprapto, B.Y., Kusumoputro, B., 2017. Attitude and Altitude Control of a Quadcopter using Neural Network Based Direct Inverse Control Scheme. Advanced Science Letters, Volume 23(5), pp. 4060–4064

Heryanto, M.A., Wahab, W., Kusumoputro, B., 2015. Optimization of a Neural Network Based Direct Inverse Control for Controlling a Quadrotor Unmanned Aerial Vehicle. MATEC Web of Conferences, Volume 34, pp. 4–7

Krishnan, R.A., Jisha, V.R., Gokulnath, K., 2018. Path Planning of an Autonomous Quadcopter Based Delivery System. In: 2018 International Conference on Emerging Trends and Innovations in Engineering and Technological Research (ICETIETR), pp. 1–5

Mahadika, P., Subiantoro, A., Kusumoputro, B., 2020. Neural Network Predictive Control Approach Design for Adaptive Cruise Control. International Journal of Technology, Volume 11(7), pp. 1451–1462

Muliadi, J., Kusumoputro, B., 2018. Neural Network Control System of UAV Altitude Dynamics and its Comparison with the PID Control System. Journal of Advanced Transportation, Volume 2018, Article ID 3823201

Najm, A.A., Ibraheem K.I., Ahmad T.A., Amjad J.H., 2020. Genetic Optimization-Based Consensus Control of Multi-Agent 6-DoF UAV System. Sensors, Volume 20(12), pp. 1–31

Najm, A.A., Ibraheem, I.K., 2019. Nonlinear PID Controller Design for a 6-DOF UAV Quadrotor System. Engineering Science and Technology, an International Journal, Volume 22(4), pp. 1087–1097

Nenni, M.E., Di Pasquale, V., Miranda, S., Riemma, S., 2020. Development of a Drone-Supported Emergency Medical Service. International Journal of Technology, Volume 11(4), pp. 656–666

Nguyen, A.T., Xuan-Mung, N., Hong, S.-K., 2019. Quadcopter Adaptive Trajectory Tracking Control: A New Approach via Backstepping Technique. Applied Sciences, Volume 9(18), pp. 2–17

Suprapto, B.Y., Mustaqim, A., Wahab, W., Kusumoputro, B., 2017. Modified Elman Recurrent Neural Network for Attitude and Altitude Control of Heavy-Lift Hexacopter. In: 2017 15th International Conference on Quality in Research (QiR): International Symposium on Electrical and Computer Engineering, pp. 309–314

Tripathi, V.K., Behera, L., Verma, N., 2015. Design of Sliding Mode and Backstepping Controllers for a Quadcopter. In: 2015 39th National Systems Conference, pp. 1–6

Wang, P., Man, Z., Cao, Z., Zheng, J., and Zhao, Y., 2016. Dynamics Modelling and Linear Control of Quadcopter. In: 2016 International Conference on Advanced Mechatronic Systems (ICAMechS), pp. 498–503

Xianglei, D., Shuguang, Z., Lvchang, H., and Rong, H., 2011. The Neural Network Direct Inverse Control of Four-Wheel Steering System. In: 2011 Third International Conference on Measuring Technology and Mechatronics Automation, pp. 865–869

Yuning, J., Rasool, M.A.U., Bo, Q., Farid, G., and Chaudary, S.T., 2019. An Adaptive Neural Network State Estimator for Quadrotor Unmanned Air Vehicle. In: (IJACSA) International Journal of Advanced Computer Science and Applications, Volume 10(2), pp. 316–321