Helicopter Control Using Fuzzy Logic and Narma-L2 Techniques
Автор: Noor Salam Al-Fallooji, Maysam Abbod
Журнал: International Journal of Intelligent Systems and Applications @ijisa
Статья в выпуске: 5 vol.12, 2020 года.
Бесплатный доступ
Helicopter instability is one of the most limitations that should be addressed in a nonlinear application. Accordingly, researchers are invited to design a robust and reliable controller to obtain a stable system and enhance its overall performance. The present study focuses on the use of the intelligent system in controlling the pitch and yaw angles. This lead to controlling the elevation and the direction of the helicopter. Further to the application of the Linear Quadratic Regulator (LQR) controller, this research implemented the Proportional Integral Derivative (PID), Fuzzy Logic Control (FLC), and Artificial Neural Network (ANN). The results show that FLC achieved a good controllability for both angles, particularly for the pitch angle in comparison to the nonlinear auto regressive moving average (NARMA-L2). Moreover, NARMA-L2 requires further improvement by using, for example, the swarm optimization method to provide better controllability. The PID controller, on the other hand, had a greater capability in controlling the yaw angle in comparison to the other controllers implemented. Accordingly, it is suggested that the integration of PID and FLC may lead to more optimal outcomes.
Fuzzy logic controller (FLC), Nonlinear systems, Helicopters, NARMA-L2
Короткий адрес: https://sciup.org/15017510
IDR: 15017510 | DOI: 10.5815/ijisa.2020.05.01
Список литературы Helicopter Control Using Fuzzy Logic and Narma-L2 Techniques
- M. Hernandez-Gonzalez, A. Y. Alanis, and E. A. Hernandez-Vargas, “Decentralized Discrete-time Neural Control for a Quanser 2-DOF Helicopter,” Appl. Soft Comput., vol. 12, no. 8, pp. 2462–2469, 2012.
- D. Iakovou, “Fuzzy Control for Helicopter Aviation,” Master thesis, University of Twente, 2002.
- G. Barbaroso, L. Ozdamar, and E. Ahmet, “An Interactive Approach for Hierarchical Analysis of Helicopter Logistics in Disaster Relief Operations,” Eur. J. Oper. Res., vol. 140, pp. 118–133, 2002.
- G.-R. Yu and H.-T. Liu, “Sliding Mode Control of a Two-degree-of-freedom Helicopter via Linear Quadratic Regulator,” IEEE Int. Conf. Syst. Man Cybern., vol. 4, 2005.
- “PilotFriend,” 2016. [Online]. Available: http://www.pilotfriend.com/training/flight_training/rotary/helis.htm. [Accessed: 03-Aug-2016].
- B. Smaoui and M. Gabr, “The Box-Jenkins Analysis and Neural Networks: Prediction and Time Series Modelling,” Applied Mathematical and Modelling,” vol. 27, no. 10, pp. 805–815, 2003.
- N. Singh and B. Panjwani, “Comparison of Neural Network and Fuzzy Logic Control for Nonlinear Model of Two Link Rigid Manipulator,” Int. J. Control Autom., vol. 7, no. 4, pp. 417–428, 2014.
- M. A. Mohd Basri, A. R. Husain, and K. A. Danapalasingam, “Intelligent Adaptive Backstepping Control for MIMO Uncertain Non-linear Quadrotor Helicopter Systems,” Trans. Inst. Meas. Control, vol. 37, no. 3, pp. 345–361, 2015.
- F. Chetouane and S. Darenfed, “Neural Network NARMA Control of a Gyroscopic Inverted Pendulum,” Eng. Lett., vol. 16, no. 3, pp. 274–279, 2008.
- A. Bousbaine, A. Bamgbose, G. Poyi, and A. K. Joseph, “Design of Self-tuning PID Controller Parameters Using Fuzzy Logic Controller for Quad-rotor Helicopter,” Int. J. Trend Res. Dev., vol. 3, no. 6, pp. 95–99, 2016.
- R. Islam, K. Abdullah, N. Parveen, and M. Mahmud, “Adaptive Control of Nonlinear System Based on QFT Application to 3-DOF Flight Control System,” Telkomnik, vol. 17, no. 5, pp. 2595–2606, 2019.
- Quanser, “Position Control 2-DOF Helicopter Reference Manual.”
- “Quanser 2-DOF Helicopter Laboratory Manual,” 2011.
- F. A. Salem and A. A. Rashed, “PID Controllers and Algorithms: Selection and Design Techniques Applied in Mechatronics Systems Design - Part II,” vol. 2, no. May, pp. 191–203, 2013.
- F. Dernoncourt, Introduction to Fuzzy Logic Control. Massachusetts Institute of Technology, available at: file:///C:/Users/CCC/Downloads/fuzzy_logic1.pdf MIT, pp. 1–19, 2013.
- A. Kusagur, S. F. Kodad, and B. V. S. Ram, “Modeling, Design and Simulation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Speed Control of Induction Motor,” Int. J. Comput. Appl., vol. 6, no. 12, pp. 29–44, 2010.
- M. T. Hagan, H. B. Demuth, and O. DE Jesus, “An Introduction to the Use of Neural Networks in Control Systems,” Int. J. Robust Nonlinear Control, vol. 12, no. 11, pp. 959–985, 2002.
- C. Jeyachandran and M. Rajaram, “Neural Network Based Predictive, NARMA-L2 and Neuro-Fuzzy Control for a CSTR Process,” J. Eng. Appl. Sci., vol. 5, no. 3, pp. 30–42, 2011.
- D. F. Ahmed and A. H. Khalaf, “Artificial Neural Networks Controller for Crude Oil Distillation Column of Baiji Refinery,” J. Chem. Eng. Process Technol., vol. 07, no. 01, pp. 1–9, 2016.
