Nonlinear modelling and optimal control via Takagi-Sugeno fuzzy techniques: A quadrotor stabilization

[1] L. A. Zadeh, “Fuzzy Sets Information and control vol. 8, 1965.10.1016/S0019-9958(65)90241-XSearch in Google Scholar

[2] I. Abdelmalek, “Non Linear Systems Control: LMI Fuzzy Approach”, PHD thesis, University of Hadj Lakhdar de Batna, Algerie, 2009.Search in Google Scholar

[3] L. X. Wang, and J. M. Mendel, “Fuzzy basis functions, universal approximators and orthogonal least-squares learning”, IEEE Transactions on Neural Networks, vol. 3, no. 5, 807-814, 1992.10.1109/72.159070Search in Google Scholar

[4] T. Takagi, and M. Sugeno, “Fuzzy identication of systems and its applications tomodelling and control, IEEE Transactions on Systems Man and Cybernetics”,SMC-15 (1), 1985.10.1109/TSMC.1985.6313399Search in Google Scholar

[5] K. Tanaka, and M. Sugeno, “Stability analysis and design of fuzzy control systems”, Fuzzy Sets and Systems, vol. 45 no. 2, 135-156, 1992.10.1016/0165-0114(92)90113-ISearch in Google Scholar

[6] H. O. Wang, K. Tanaka, E. Griffin, “Parallel Distributed Compensation of Non-linear Systems by Takagi-Sugeno Fuzzy Model”, Proceedings of 1995 IEEE International Conference on Fuzzy Systems, vol.2, pp.531-538, 1995.Search in Google Scholar

[7] K. Tanaka, and H. O.Wang, “Fuzzy Control Systems Design and Analysis, A Linear Matrix Inequality Approach”, John Wiley and Sons, 2001.Search in Google Scholar

[8] S. K. Hong, and Y. Nam, “Stable fuzzy control system design with pole-placement constraint:An LMI approach”, Computer in Industry, vol 51, pp. 1-11, 2003.10.1016/S0166-3615(03)00057-5Search in Google Scholar

[9] M. Pokorny, and T. Docekal “Dynamic Tuning of the Optimal Fuzzy LQR Controller”, Proc, IEEE SMC 2019, Bari, Italy, 2019.10.1109/SMC.2019.8914651Search in Google Scholar

[10] K. Tanaka, T. Ikeda, and H. O. Wang, “Robust stabilization of a class of uncertain non-linear systems via fuzzy control:Quadratic stability, H control theory, and linear matrix inequalities”, IEEE Transactions on Fuzzy Systems, vol. 4 no. 1, pp. 1-13, 1996.10.1109/91.481840Search in Google Scholar

[11] R. G. Venkata, K. Padmanabhan, and S. Ananthi, “Optimal Control with Fuzzy State Space Modelling Using Riccati Equation”, Int. Journal of Information and Electronic Engineering, vol. 2, no. 5, pp. 800-805, 2012.Search in Google Scholar

[12] F. Khaber, K. Zehar, and A. Hamzaoui, “State Feedback Controller Design via Takagi-Sugeno Fuzzy Model:LMI Approach”, Int. Journal of Mechanical and Mechatronics Engineering, vol. 2, no. 6, pp. 836-841, 2008.Search in Google Scholar

[13] A. Khajeh, and R. Ghazi, “GA-Based Optimal LQR Controller to Improve LVRT Capability of DFIG Wind Turbines”, Iranian Journal of Electrical and Electronic Engineering, vol. 9, no. 3, pp, 167-176, 2013.10.1155/2013/157431Search in Google Scholar

[14] J. G. G. Grijalva, “Control of a quadrotor using TS fuzzy techniques”, PHD thesis, vol. University of Santa Catarina, no. Florianopolis, Equador, 2017.Search in Google Scholar

[15] P. Gahinet et al, “The LMI Control Toolbox, Proc, of 33rd IEEE Conference on Decision and Control”, Lake Buena Vista, USA, vol. 3, pp. 2038-2041, 1994.Search in Google Scholar

[16] J. Löfberg, “YALMIP: A toolbox for modelling and optimization in MATLAB”, IEEE International Conference on Robotics and Automation, New Orleans, USA, pp. 284-289, 2004.Search in Google Scholar

[17] SEDUMI“Semidefinite programming solver”, available com/SQLP/SeDuMi, 2019.Search in Google Scholar