Intelligent Observer-Based Feedback Linearization for Autonomous Quadrotor Control

  • Authors

    • Izzuddin M. Lazim
    • Abdul Rashid Husain
    • Nurul Adilla Mohd Subha
    • Mohd Ariffanan Mohd Basri
  • Disturbance Observer, Feedback Linearization, K-means clustering, Neural Network, Quadcopter.
  • The presence of disturbances can cause instability to the quadrotor flight and can be dangerous especially when operating near obstacles or other aerial vehicles. In this paper, a hybrid controller called state feedback with intelligent disturbance observer-based control (SF-iDOBC) is developed for trajectory tracking of quadrotor in the presence of time-varying disturbances, e.g. wind. This is achieved by integrating artificial intelligence (AI) technique with disturbance observer-based feedback linearization to achieve a better disturbance rejection capability. Here, the observer estimates the disturbances acting on the quadrotor, while AI technique using the radial basis function neural network (RBFNN) compensates the disturbance estimation error. To improve the error compensation of RBFNN, the k-means clustering method is used to find the optimal centers of the Gaussian activation function. In addition, the weights of the RBFNN are tuned online using the derived adaptation law based on the Lyapunov method, which eliminates the offline training. In the simulation experiment conducted, a total of four input nodes and five hidden neurons are used to compensate for the error. The results obtained demonstrate the effectiveness and merits of the theoretical development.


  • References

    1. [1] I.M. Lazim, A.R. Husain, N.A.M. Subha, Z. Mohamed, M.A. Mohd Basri, Optimal Formation Control of Multiple Quadrotors Based on Particle Swarm Optimization, in: Asian Simul. Conf., Springer, Singapore, 2017: pp. 121–135.

      [2] A.L. Salih, M. Moghavvemi, H.A.F. Mohamed, K.S. Gaeid, Modelling and PID controller design for a quadrotor unmanned air vehicle, in: 2010 IEEE Int. Conf. Autom. Qual. Test. Robot., 2010: pp. 1–5.

      [3] T. Sangyam, P. Laohapiengsak, W. Chongcharoen, I. Nilkhamhang, Path tracking of UAV using self-tuning PID controller based on fuzzy logic, in: Proc. SICE Annu. Conf. 2010, 2010: pp. 1265–1269.

      [4] A. Dharmawan, T.K. Priyambodo, others, Model of Linear Quadratic Regulator (LQR) Control Method in Hovering State of Quadrotor, J. Telecommun. Electron. Comput. Eng., 9 (2017) 135–143.

      [5] M.A. Henson, D.E. Seborg, Feedback Linearizing Control, in: Nonlinear Process Control, Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1997: pp. 149–232.

      [6] A. Mahmood, Y. Kim, Leader-following formation control of quadcopters with heading synchronization, Aerosp. Sci. Technol., 47 (2015) 68–74.

      [7] V. Mistler, A. Benallegue, N.K. M’Sirdi, Exact linearization and noninteracting control of a 4 rotors helicopter via dynamic feedback, in: Proc. 10th IEEE Int. Work. Robot Hum. Interact. Commun., 2001: pp. 586–593.

      [8] F. Sabatino, Quadrotor control : modeling, nonlinear control design, and simulation, KTH Royal Institute of Technology, 2015.

      [9] A. Mahmood, Y. Kim, Decentralized formation control of quadcopters using feedback linearization, in: 2015 6th Int. Conf. Autom. Robot. Appl., 2015: pp. 537–541.

      [10] I.H. Choi, H.C. Bang, Quadrotor-tracking controller design using adaptive dynamic feedback-linearization method, Proc. Inst. Mech. Eng. Part G J. Aerosp. Eng., 228 (2014) 2329–2342.

      [11] W. Zhao, T.H. Go, Quadcopter formation flight control combining MPC and robust feedback linearization, J. Franklin Inst., 351 (2014) 1335–1355.

      [12] L.B. Freidovich, H.K. Khalil, Performance Recovery of Feedback-Linearization-Based Designs, IEEE Trans. Automat. Contr., 53 (2008) 2324–2334.

      [13] A. Benallegue, A. Mokhtari, L. Fridman, Feedback linearization and high order sliding mode observer for a quadrotor UAV, in: Int. Work. Var. Struct. Syst. 2006. VSS’06., 2006: pp. 365–372.

      [14] A. Mokhtari, N.K. M’Sirdi, K. Meghriche, A. Belaidi, Feedback linearization and linear observer for a quadrotor unmanned aerial vehicle, Adv. Robot., 20 (2006) 71–91.

      [15] A. Aboudonia, A. El-Badawy, R. Rashad, Disturbance observer-based feedback linearization control of an unmanned quadrotor helicopter, Proc. Inst. Mech. Eng. Part I J. Syst. Control Eng., 230 (2016) 877–891.

      [16] J.S. Bay, Fundamentals of Linear State Space Systems, WCB/McGraw-Hill, 1999.

      [17] M. Sharma, A.J. Calise, Neural-Network Augmentation of Existing Linear Controllers, J. Guid. Control. Dyn., 28 (2005) 12–19.

      [18] S.S.A. Ali, M. Moinuddin, K. Raza, S.H. Adil, An Adaptive Learning Rate for RBFNN Using Time-Domain Feedback Analysis, Sci. World J., 2014 (2014).

      [19] H. Sun, L. Hou, Y. Li, Disturbance observer based dynamic surface tracking control for a class of uncertain nonlinear systems with mismatched disturbances, in: 2016 12th World Congr. Intell. Control Autom., 2016: pp. 605–610.

      [20] M.M. Arefi, M.R. Jahed-Motlagh, H.R. Karimi, Adaptive Neural Stabilizing Controller for a Class of Mismatched Uncertain Nonlinear Systems by State and Output Feedback, IEEE Trans. Cybern., 45 (2015) 1587–1596.

      [21] M.A.M. Basri, K.A. Danapalasingam, A.R. Husain, Intelligent adaptive backstepping control for MIMO uncertain non-linear quadrotor helicopter systems, Trans. Inst. Meas. Control, 37 (2015) 345–361.

      [22] M.A.B.M. Basri, Intelligent Backstepping Control of Quadrotor Unmanned Aerial Vehicle, Universiti Teknologi Malaysia, 2015.

      [23] T. Chen, H. Chen, Approximation capability to functions of several variables, nonlinear functionals, and operators by radial basis function neural networks, IEEE Trans. Neural Networks, 6 (1995) 904–910.

      [24] L. Behera, I. Kar, Intelligent Systems and Control: Principles and Applications, Oxford University Press, New Delhi, India, 2010.

      [25] J. Liu, Radial Basis Function (RBF) neural network control for mechanical systems: design, analysis and Matlab simulation, Springer Science & Business Media, 2013.

      [26] J. MacQueen, others, Some methods for classification and analysis of multivariate observations, in: Proc. Fifth Berkeley Symp. Math. Stat. Probab., 1967: pp. 281–297.

      [27] S. Waslander, C. Wang, Wind Disturbance Estimation and Rejection for Quadrotor Position Control, in: AIAA Infotech@aerosp. Conf., Seattle, WA, 2009.

  • Downloads

  • How to Cite

    M. Lazim, I., Rashid Husain, A., Adilla Mohd Subha, N., & Ariffanan Mohd Basri, M. (2018). Intelligent Observer-Based Feedback Linearization for Autonomous Quadrotor Control. International Journal of Engineering & Technology, 7(4.35), 904-911.