H infinity control design for Eight-Rotor MAV attitude system based on identification by interval type II fuzzy neural network

  • CHEN, Xiangjian ;
  • SHU, Kun ;
  • LI, Di
  • Received : 2014.09.09
  • Accepted : 2016.06.10
  • Published : 2016.06.30


In order to overcome the influence of system stability and accuracy caused by uncertainty, estimation errors and external disturbances in Eight-Rotor MAV, L2 gain control method was proposed based on interval type II fuzzy neural network identification here. In this control strategy, interval type II fuzzy neural network is used to estimate the uncertainty and non-linearity factor of the dynamic system, the adaptive variable structure controller is applied to compensate the estimation errors of interval type II fuzzy neural network, and at last, L2 gain control method is employed to suppress the effect produced by external disturbance on system, which is expected to possess robustness for the uncertainty and non-linearity. Finally, the validity of the L2 gain control method based on interval type II fuzzy neural network identifier applied to the Eight-Rotor MAV attitude system has been verified by three prototy experiments.


interval type II fuzzy neural network;Eight-Rotor MAV;variable structure control;L2 gain control


  1. Pounds, P., Mahony, R., Hynes, P. and Roberts, J. M., "Design of a Four-rotor Aerial Robot", Proceedings of Australian Conference on Robotics and Automation, 2002, pp. 145-150.
  2. McKerrow, P., "Modelling the Draganflyer Four-rotor Helicopter", Proceedings of the IEEE International Conference on Robotics and Automation, 2004, pp. 3596-3601.
  3. Bouabdallah, S., Noth, A. and Siegwart, R., "PID vs. LQ Control Techniques Applied to an Indoor Micro Quadrotor", IEEE International Conference on Intelligent Robots and Systems, Vol. 3, 2004, pp. 2451-2456.
  4. Tayebi, A. and McGilvray, S., "Attitude Stabilization of a VIOL Quadrotor Aircraft", IEEE Transaction on Control Systems Technology, Vol. 14, No. 3, 2006, pp. 562-571.
  5. Erginer, B. and Altug, E., "Modeling and PD Control of a Quadrotor VTOL Vehicle", Proc. of the IEEE Intelligent Vehicles Symposium, 2007, pp. 894-899.
  6. Chen, X., Li, D., Bai, Y. and Xu, Z., "Modeling and Neuro-fuzzy Adaptive Attitude Control for Eight-rotor MAV", International Journal of Control, Automation and Systems, Vol. 9, No. 6, 2011, pp. 1154-1163.
  7. Chen, X. J. and Li, D., "Modeling and Designing Intelligent Adaptive Sliding Mode Controller for an Eight-Rotor MAV", International Journal of Aeronautical and Space Sciences, Vol. 14, No. 2, 2013, pp. 172-182.
  8. Melin, P. and Castillo, O., "A New Method for Adaptive Model-based Control of Non-linear Dynamic Plants Using a Neuro-fuzzy-fractal Approach", Soft Computing Journal, Vol. 5, No. 2, 2001, pp. 171-177.
  9. Lou, X., Sun, Z. and Sun, F., "A New Approach to Fuzzy Modeling and Control for Nonlinear Dynamic Systems: Neuro-Fuzzy Dynamic Characteristic Modeling and Adaptive Control Mechanism", International Journal of Control, Automation, and Systems, Vol. 7, No. 1, 2009, pp. 123-132.
  10. Melin, P. and Castillo, O., "Intelligent Control of Complex Electrochemical Systems with a Neuro-fuzzy-genetic Approach", IEEE Transactions on Industrial Electronics, Vol. 48, No. 5, 2001, pp. 951-955.
  11. Er, M. J., Low, C. B., Nah, K. H., Lim, M. H. and Ng, S. Y., "Real-time Implementation of a Dynamic Fuzzy Neural Networks Controller for a SCARA", Microprocessors and Microsystems, Vol. 26, No. 9, 2002, pp. 449-461.
  12. Lee, C. H., Lin, Y. C. and Lai, W. Y., "Systems Identification Using Type-II Fuzzy Neural Network (type-II FNN) Systems", In Proc. IEEE Int. Symp. Comput. Intell. Robot. Antom., Vol. 3, 2003, pp. 1264-1269.
  13. Wang, C. H., Cheng, C. S. and Lee, T. T., "Dynamic Optimal Training for Interval Type-II Fuzzy Neural Network (T2FNN)", IEEE Trans. Syst., Man, Cybern. B, Vol. 34, No. 3, 2004, pp. 1462-1477.
  14. Tsung-Chih Lin. Based on interval type-II fuzzyneural network direct adaptive sliding mode control for SISO nonlinear systems. Communications in Nonlinear Science and Numerical Simulation, 2010,15:4084-4099
  15. Faa-Jeng Lin, Po-IIuan Chou. Adaptive control of two-axis motion control system using interval type-II fuzzy neural network. IEEE Transactions on Industrial Electronics. 2009; 56: 178-193.
  16. Lin, F. J., Chou, P. H., Shieh, P. H. and Chen, S. Y., "Robust Control of an LUSM-based x-y-tha Motion Control Stage Using an Adaptive Interval Type-II Fuzzy Neural Network", IEEE Transactions on Fuzzy Systems, Vol. 17, No. 1, 2009, pp. 24-38.

Cited by

  1. Self-Tuning Proportional Double Derivative-Like Neural Network Controller for a Quadrotor pp.2093-2480, 2018,


Supported by : National Natural Science Found of China