Open Access Open Access  Restricted Access Subscription or Fee Access

Robust Adaptive Fuzzy Backstepping Control for 2-DOF Laboratory Helicopter System with Improved Tracking Performance

(*) Corresponding author

Authors' affiliations



In this paper, a Robust Adaptive Fuzzy Backstepping Control (RAFBC) is applied to electromechanical system which called Twin Rotor Multi Input Multi Output System (TRMS) in order to follow the desired trajectory. This strategy yields robustness to various kinds of uncertainties and guaranteed stability of the closed-loop control system. The adaptive laws have been used in order to ameliorate the robustness against uncertainties, wind effects and external disturbances. The stability of system in the closed-loop has been demonstrated using Lyapunov method. In the control design, type 2 fuzzy logic systems are used to approximate the unknown functions. Hybrid adaptive robust tracking control schemes that are based upon a combination of bounds of type 2 fuzzy approximation parameters and the backstepping design are developed such that all the states and signals are bounded and the proposed approach alleviate the online computation burden and improves the robustness to dynamic uncertainties and external disturbances. In addition, the coupling effects between the horizontal and vertical subsystems of TRMS are considered as uncertainties. Thus, precise trajectory tracking is maintained under various operational conditions with the presence of various types of uncertainties. Unlike other controllers, the proposed control algorithm can estimate model uncertainties online and improve the robustness of the system. Experimental tests were carried out and the results demonstrate that the proposed algorithm performs well in tracking and under model uncertainties.
Copyright © 2023 Praise Worthy Prize - All rights reserved.


TRMS; Robust Control; Adaptive Control; Fuzzy; Backstepping; Uncertainties; Wind Effects

Full Text:



L. M. Belmonte, R. Morales, A. Fernández-Caballero and J. A. Somolinos, "A Tandem Active Disturbance Rejection Control for a Laboratory Helicopter with Variable-Speed Rotors," in IEEE Transactions on Industrial Electronics, vol. 63, no. 10, pp. 6395-6406, Oct. 2016.

Rahideh A, Shaheed MH. "Mathematical dynamic modelling of a twin-rotor multiple input-multiple output system". Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering. 2007;221(1):89-101.

Ying Xin, Zhi-Chang Qin, Jian-Qiao Sun, "Input-output tracking control of a 2-DOF laboratory helicopter with improved algebraic differential estimation", Mechanical Systems and Signal Processing, Volume 116, 2019, Pages 843-857.

Ignatyev, V., Kovalev, A., Spiridonov, O., Kureychik, V., Soloviev, V., Ignatyeva, A., A Method of Optimizing the Rule Base in the Sugeno Fuzzy Inference System Using Fuzzy Cluster Analysis, (2020) International Review of Electrical Engineering (IREE), 15 (4), pp. 316-327.

J. -G. Juang, M. -T. Huang and W. -K. Liu, "PID Control Using Presearched Genetic Algorithms for a MIMO System," in IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 38, no. 5, pp. 716-727, Sept. 2008.

A. Haruna, Z. Mohamed, M.Ö. Efe, M.A.M. Basri, "Improved integral backstepping control of variable speed motion systems with application to a laboratory helicopter", ISA Transactions, Volume 97, 2020, Pages 1-13.

Mohammed Zinelaabidine Ghellab, Samir Zeghlache, Ali Djerioui, Loutfi Benyettou, "Experimental validation of adaptive RBFNN global fast dynamic terminal sliding mode control for twin rotor MIMO system against wind effects", Measurement, Volume 168, 2021, 108472.

C. -W. Tao, J. -S. Taur, Y. -H. Chang and C. -W. Chang, "A Novel Fuzzy-Sliding and Fuzzy-Integral-Sliding Controller for the Twin-Rotor Multi-Input-Multi-Output System," in IEEE Transactions on Fuzzy Systems, vol. 18, no. 5, pp. 893-905, Oct. 2010.

M. Khamar, M. Edrisi, "Designing a backstepping sliding mode controller for an assistant human knee exoskeleton based on nonlinear disturbance observer", Mechatronics, Volume 54, 2018, Pages 121-132.

Abdillah, M., Nugroho, T., Pertiwi, N., Multi-Objective Interval Type 2 Fuzzy Sine Cosine Algorithm for Solving Optimal Power Flow Problem, (2021) International Review of Electrical Engineering (IREE), 16 (2), pp. 118-126.

S. K. Valluru, R. Kumar and R. Kumar, "Design of Precise FLC for Trajectory Tracking and Stabilization of Twin Rotor MIMO System," 2019 IEEE 6th International Conference on Engineering Technologies and Applied Sciences (ICETAS), Kuala Lumpur, Malaysia, 2019, pp. 1-6.

K. Kalyani and S. Kanagalakshmi, "Control of Trms using Adaptive Neuro Fuzzy Inference System (ANFIS)," 2020 International Conference on System, Computation, Automation and Networking (ICSCAN), Pondicherry, India, 2020, pp. 1-5.

Batayneh, W., Aburmaileh, Y., Bataineh, A., Experimental Implementation of Tracking Error Elimination for Omnidirectional Wheelchair Using PD-Fuzzy-P Controller, (2021) International Review of Automatic Control (IREACO), 14 (2), pp. 102-112.

C. Mishra, S. K. Swain, S. Kumar Mishra and S. K. Yadav, "Fractional Order Sliding Mode Controller for the Twin Rotor MIMO System," 2019 International Conference on Intelligent Computing and Control Systems (ICCS), Madurai, India, 2019, pp. 662-667.

G. Rohith, Fractional power rate reaching law for augmented sliding mode performance, Journal of the Franklin Institute, Volume 358, Issue 1, 2021, Pages 856-876.

Rached, B., Elharoussi, M., Abdelmounim, E., New Dynamic Fuzzy High Gain Observer Combined with a Nonlinear Control Approach for Performance Enhancement in WECS Based on DFIG: Design and DSP Implementation, (2021) International Review of Automatic Control (IREACO), 14 (5), pp. 250-261.

S. H. Shah, S. G. Khan, J. Iqbal and M. Alharthi, "Modeling and Robust Control of Twin Rotor MIMO System," 2019 International Conference on Robotics and Automation in Industry (ICRAI), Rawalpindi, Pakistan, 2019, pp. 1-5.

Twin Rotor MIMO System Manual, Feedback Instruments Ltd., UK, 2006.

Nithya, M., Rashmi, M., Gazebo - Simulink Framework for Trajectory Tracking in a Multi-Quadcopter Environment, (2022) International Review of Automatic Control (IREACO), 15 (4), pp. 164-175.

Wan M, Liu Q. Adaptive fuzzy backstepping control for uncertain nonlinear systems with tracking error constraints. Advances in Mechanical Engineering. 2019,11(5).

Belkhiri, D., Alaoui, M., Improved Tracking of Optimal Torque by Artificial Neural Network for Wind Energy Systems, (2021) International Review on Modelling and Simulations (IREMOS), 14 (2), pp. 110-117.

Tawfeic, S., Multiplicative and Additive Error Compensation Techniques for Perfect Reference Tracking, (2023) International Review of Automatic Control (IREACO), 16 (1), pp. 10-16.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2024 Praise Worthy Prize