Type-2 fuzzy logic control of a 2-DOF helicopter (TRMS system)

Central European Journal of Engineering - Tập 4 - Trang 303-315 - 2014
Samir Zeghlache1, Kamel Kara2, Djamel Saigaa1,3
1LASS Laboratory, Department of Electronics, Faculty of Technology, University of Msila, Msila, Algeria
2SET laboratory Department of Electronics, Faculty of Engineering Sciences, University of Blida, Msila, Algeria
3LMSE Laboratory, Department of Electrical Engineering, Faculty of Sciences and Technology, University of Biskra, Biskra, Algeria

Tóm tắt

The helicopter dynamic includes nonlinearities, parametric uncertainties and is subject to unknown external disturbances. Such complicated dynamics involve designing sophisticated control algorithms that can deal with these difficulties. In this paper, a type 2 fuzzy logic PID controller is proposed for TRMS (twin rotor mimo system) control problem. Using triangular membership functions and based on a human operator experience, two controllers are designed to control the position of the yaw and the pitch angles of the TRMS. Simulation results are given to illustrate the effectiveness of the proposed control scheme.

Tài liệu tham khảo

Tee K. P., Ge S. S. and Tay F. E. H., Adaptive neural network control for helicopters in vertical flight, IEEE Trans. on Control Systems Technology 16(4), 2008, 753–762 Wen P. and Lu T. W., Decoupling Control of a twin rotor MIMO system using robust deadbeat control technique, IET Control Theory and Applications 2(11), 2008, 999–1007 Juang J. G., Huang M. T., and Liu W. K., PID control using presearched genetic algorithms for a MIMO system, IEEE Trans. on Systems, Man and Cybernetics: Part C 38(5), 2008, 716–727 L’pez-Martinez M., Vivas C., and Ortega M. G., A multivariable nonlinear H ∞ controller for a laboratory helicopter, IEEE Conf. on European Control, 2005, 4065–4070 Rahideh A., Shaheed M. H. and Bajodah A. H., Adaptive Nonlinear Model Inversion Control of a Twin Rotor System Using Artificial Intelligence, IEEE Conf on Control Applications, 2007, 898–903 Juang J. G., Lin R. W., and Liu W. K., Comparison of classical control and intelligent control for a MIMO cystem, Applied Mathematics and Computation 205(2), 2008, 778–791 Utkin V., Variable structure systems with sliding modes, IEEE Trans. On Automatic Control 22(2), 1977, 212–222 Hung J. Y., Gao W. and Hung J. C., Variable structure control: a survey, IEEE Trans on Industrial Electronics 40(1), 1993, 2–22 Zhang H., Shi Y., Mehr A. S., On H ∞ Filtering for Discrete-Time Takagi-Sugeno Fuzzy Systems, IEEE Transactions on Fuzzy Systems 20(2) 2012, 396–401 Zhang H., Shi Y., Liu M., Cal H ∞ Step Tracking Control for Networked Discrete-Time Nonlinear Systems With Integral and Predictive Actions, IEEE Transactions on Industrial Informatics 9(1), 2013, 337–345 Zhang H., Shi Y. and Mu B., Optimal HâĹđ-Based Linear-Quadratic Regulator Tracking Control for Discrete-Time Takagi-Sugeno Fuzzy Systems With Preview Actions, ASME Transactions, Journal of Dynamic Systems, Measurement and Control 135(4), 2013, 044501 Shi Y., Zhang H., Wang J., On Energy-to-peak Filtering for Nonuniformly Sampled Nonlinear Systems: A Markovian Jump System Approach, IEEE Transactions on Fuzzy Systems, DOI:10.1109/TFUZZ.2013.2250291, accepted and in press, 2013. Tao C. W., Taurb J. S. and Chen Y. C., Design of a parallel distributed fuzzy LQR controller for the twin rotor multi-input multi-output system, Fuzzy Sets and Systems 161(1), 2010, 2081–2103 Tao C. W., Taur J. S., Chang Y. H., Chang C. W., A Novel Fuzzy Sliding and Fuzzy Integral Sliding Controller for the Twin Rotor Multi-Input Multi-Output System, IEEE Trans. on Fuzzy Systems 18(4), 2010, 1–12 Rahideh A., Shaheed M. H., Bajodah A. H., Neural network based adaptive nonlinear model inversion control of a twin rotor system in real time, 7th IEEE International Conference on Cybernetic Intelligent Systems, 2008, 1–6 Omar M., Zaidan M. A. and Tokhi M. O., Dynamic modelling and control of a twin-rotor system using adaptive neuro-fuzzy inference system techniques, Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 226(7), July 2012, 787–803 Shaik F. A. and Purwar S., A Nonlinear State Observer Design for 2 — DOF Twin Rotor System Using Neural Networks, IEEE Conf on Advances in Computing, Control, and Telecommunication Technologies, 2009, 15–19 Hagras H. A., A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots, IEEE Trans. Fuzzy Syst 12(4), 2004, 524–539 Feedback Instruments Ltd., Twin Rotor MIMO System Advanced Technique Manual 33-949S, E. Sussex, England, 2002 Mendel J. M., Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions, Prentice-Hall, Upper Saddle River, NJ, first edition, 2001 Mendel J. M., John R. I., and Liu F., Interval type-2 fuzzy logic systems made simple, IEEE Transactions on Fuzzy Systems 14(6), 2006, 808–818 Liang Q. and Mendel J. M., Interval type-2 fuzzy logic systems: theory and design, IEEE Transactions on Fuzzy Systems 8(5), 2000, 535–550 Mendel J. M., Type-2 fuzzy sets: some question and answers, IEEE Connections, Newsletter of the IEEE Neural Networks Society 1, 2003, 10–13