Hostname: page-component-77f85d65b8-g98kq Total loading time: 0 Render date: 2026-04-21T16:59:05.030Z Has data issue: false hasContentIssue false
  • English
  • Français

Attitude modelling and real-time robust control of a 3-DoF quadcopter UAV test bench

Published online by Cambridge University Press:  28 February 2024

S.M. Ahmad Open the ORCID record for S.M. Ahmad [Opens in a new window]  and
S. Fareed Open the ORCID record for S. Fareed [Opens in a new window]
S.M. Ahmad*
Affiliation:
Control & Instrumentation Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, Kingdom of Saudi Arabia Interdisciplinary Research Center for Intelligent Manufacturing and Robotics, King Fahd University of Petroleum and Minerals, Dhahran, Kingdom of Saudi Arabia
S. Fareed
Affiliation:
Drone Robotics Pvt Ltd, The Catalyst GIK Incubator, Topi, Pakistan
*
Corresponding author: S.M. Ahmad; Email: sarvat.ahmad@kfupm.edu.sa
Get access Rights & Permissions [Opens in a new window]

Abstract

In this work, a three degrees-of-freedom (3-DoF) static quadcopter unmanned aerial vehicle (UAV) test-rig of a pendulum-type configuration is custom-designed, developed, instrumented, and interfaced with a PC. The rig serves as a test bed to develop high-fidelity mathematical models as well as to investigate autopilot designs and real-time closed-loop controllers’ performances. The Simulink Desktop Real-Time software is employed for the quadcopter’s attitude signals acquisition and real-time implementation of closed-loop controllers on a target microcontroller hardware. The mathematical models for pitch, roll, and yaw axes are derived via the first principle and validated with the experimental linear system identification (SI) techniques. Subsequently, employing the multi-parameter root contour technique, the classical proportional integral derivative (PID) controllers are designed and implemented in real-time on the quadcopter UAV test rig. This served as a benchmark controller for comparing it with an integral-based linear quadratic regulator (LQR) controller. Further, to improve the transient response of the LQR controller, a novel robust integral-based LQR controller with a feedforward term (LQR-FF) is implemented, which shows much superior performance than the benchmark and basic LQR controller. This work thus will act as a precursor for a more complex 3-DoF autopilot design of an untethered quadcopter.

Keywords

quadcopter UAVmathematical modellingrobust real-time controlsystem identificationLQR-FF

Information

Type
Research Article
Information
The Aeronautical Journal , Volume 128 , Issue 1326 , August 2024 , pp. 1767 - 1788
Check for updates
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of Royal Aeronautical Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Silvagni, M., Tonoli, A., Zenerino, E. and Chiaberge, M. Multipurpose UAV for search and rescue operations in mountain avalanche events, Geometics Nat. Hazards Risk, 2017, 8, (17).Google Scholar
Rizwan, R., Naeem Shehzad, M. and Naeem Awais, M. Quadcopter-based rapid response first-aid unit with live video monitoring, Drones, 2019.CrossRefGoogle Scholar
Navia, J., Mondragon, I., Patino, D. and Colorado, J. Multispectral mapping in agriculture: terrain mosaic using an autonomous quadcopter UAV, The 6th International Conference on Unmanned Aircraft Systems (ICUAS), Arlington, VA USA, 2016.CrossRefGoogle Scholar
Rahman, M.F.F., Fan, S., Zhang, Y. and Chen, L. A comparative study on application of unmanned aerial vehicle systems in agriculture, Agriculture (Switzerland), 2021, 11, (1), pp 1–26.Google Scholar
Xiang, H., Han, Y., Pan, N., Zhang, M. and Wang, Z. Study on multi-UAV cooperative Path planning for complex patrol tasks in large cities, Drones, 2023.CrossRefGoogle Scholar
Chang, J., Cieslak, J., Dávila, J., Zhou, J., Zolghadri, A. and Guo, Z. A two-step approach for an enhanced quadrotor attitude estimation via IMU data, IEEE Trans. Control Syst. Technol., 2018, 26, (3), pp 1140–1148.CrossRefGoogle Scholar
Bouabdallah, S., Murrieri, P. and Siegwart, R. Design and control of an indoor micro quadrotor, Proceedings of the 2004 IEEE, International Conference on Robotics and Automation, New Orleans, LA, USA, 2004.CrossRefGoogle Scholar
Mardan, M., Esfandiari, M. and Sepheri, N. Attitude and position controller design and implementation for a quadrotor, Int. J. Adv. Rob., 2017, pp 1–11.Google Scholar
Vong, C.H., Ryan, K. and Chung, H. Trajectory tracking control of quadcopters under tunnel effects, Mechatronics, 2021, 78.CrossRefGoogle Scholar
Quan, Q. Introduction to Multicopter Design and Control, Springer, 2017, Singapore.Google Scholar
Patel, R., Deb, D., Modi, H. and Shah, S. Adaptive backstepping control scheme with integral action for Quanser 2-dof helicopter, International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017.CrossRefGoogle Scholar
Rajappa, S., Chriette, A., Chandra, R. and Khalil, W. Modelling and dynamic identification of 3 DOF Quanser Helicopter, 16th International Conference on Advanced Robotics (ICAR), 2013.CrossRefGoogle Scholar
Dhaybi, M. and Daher, N. Real-time estimation of the inertia tensor elements of a quadcopter hover platform, Proceedings of the 2019 IEEE/ASME, International Conference on Advanced Intelligent Mechatronics, Hong Kong, China, July 8-12, 2019.Google Scholar
Daponte, P. DronesBench: an innovative bench to test drones, IEEE Instrum. Meas. Mag., 2017.CrossRefGoogle Scholar
Beharie, M.M., Pedro, J.O. and Dala, L. Design of a quadrotor flight test stand for system identification, Aeronaut. J., 2015, 119, (1214).CrossRefGoogle Scholar
Powers, C., Mellinger, D. and Kumar, V. Quadrotor kinematics and dynamics, in Handbook of Unmanned Aerial Vehicles, Springer-Verlag, 2015, New York, NY, USA, pp 307–328.CrossRefGoogle Scholar
Resendiz, V.M.A. and Araiza, E.R. System identification of a quadrotor in X configuration from experimental data, Res. Comput. Sci., 2016, 118.Google Scholar
Gremillion, G. and Humber, J.S. System identification of a quadrotor micro air vehicle, AIAA Atmospheric Flight Mechanics Conference, Toronto, Ontario, Canada, 2010.CrossRefGoogle Scholar
Sarioglu, A. and Kural, A. Modeling and ARX identification of a quadrotor Mini-UAV, 2015 9th International Conference on Electrical and Electronics Engineering (ELECO), Bursa, Turkey, IEEE, 2015, pp 1196–1200.CrossRefGoogle Scholar
Brzozowski, B., Rochala, Z., Wojtowicz, K. and Wieczorek, P., Measurement data fusion with cascaded Kalman and complementary filter in the flight parameter indicator for hang-glider and paraglider, Measurement, 2018, 123, pp 94–103.CrossRefGoogle Scholar
Fields, T.D., Ellisy, L.M. and Kingz, G.W. PID quadrotor 6-DOF HIL simulation and verification using a 6-axis load cell, AIAA Atmospheric Flight Mechanics Conference, 5-9 January, Kissimmee, Florida, USA, 2015.CrossRefGoogle Scholar
Xuan-Mung, N. and Hong, S.-K. Improved altitude control algorithm for quadcopter unmanned aerial vehicles, Appl. Sci., 2019.CrossRefGoogle Scholar
Yuksel, B., Secchi, C., Bulthoff, H.H. and Franchi, A. Aerial physical interaction via IDA-PBC, Int. J. Rob. Res., 2019, 38, pp 403–421.CrossRefGoogle Scholar
Estrada, F.R.L., Ponsart, J.C., Theilliol, D. and Zhang, Y. LPV model-based tracking control and robust sensor fault diagnosis for a quadrotor UAV, J. Intell. Rob. Syst., 2016, 84, pp 163–177.CrossRefGoogle Scholar
Liu, H., Shi, P. and Chadli, M. Finite-time stability and stabilization for a class of nonlinear systems with time-varying delay, Int. J. Syst. Sci., 2016, 47, pp 1433–1444.CrossRefGoogle Scholar
Ahmad, S.M., Chipperfield, A.J. and Tokhi, M.O. Dynamic modelling and linear quadratic Gaussian control of a twin-rotor multi-input multi-output system, Proc. Inst. Mech. Eng. Part I J. Syst. Control Eng., 2003, 217, (3), pp 203–227.Google Scholar
Ljung, L. System Identification: Theory for the User, Prentice Hall, 1999.Google Scholar
Kuo, B.C. and Golnaraghi, F. Automatic Control Systems, 8th ed, 2002.Google Scholar
Ogata, K. Modern Control Engineering, 5th ed, Pearson, 2011.Google Scholar
Franklin, G., Powell, J. and Emami-Naeini, A. Feedback Control of Dynamic Systems, 7th ed, Pearson, 2015, UK.Google Scholar
Ahmad, S.M. Flight dynamics, parametric modelling and real-time control of a 1-DOF Tailplane, Math. Comput. Model Dyn. Syst. 2013, 19, 220–237.CrossRefGoogle Scholar
Khan, Y.M., Ahmad, S.M., Ali, M. and Khan, M. Flight dynamics and parametric modelling of a 2-DOF lab aircraft, Proc. Inst. Mech. Eng. Part G J. Aerosp. Eng., 2019, 233, pp 2923–2931.CrossRefGoogle Scholar
Özaslan, T., Loianno, G., Keller, J., Taylor, C.J., Kumar, V., Wozencraft, J.M. and Hood, T. Autonomous navigation and mapping for inspection of penstocks and tunnels with MAVs, IEEE Robot. Autom. Lett., 2017, 2, pp 1740–1747.CrossRefGoogle Scholar
Bavle, H., Sanchez-Lopez, J.L., de la Puente, P., Rodriguez-Ramos, A., Sampedro, C. and Campoy, P. Fast and robust flight altitude estimation of multirotor UAVs in dynamic unstructured environments using 3D point cloud sensors, Aerospace, 2018, 5, p 94.CrossRefGoogle Scholar
Cook, M.V. Flight Dynamics Principles, Butterworth Heinemann, 2004, Oxford, UK.Google Scholar