System Modeling and Artificial Neural Network (ANN) Design for Lateral and Longitudinal of F-16
Keywords:Linear Quadratic Gaussian (LQG), F-16, Kalman filter
Today, the classical control methods are still widely used because of their excellent performance in a working environment with conditions of geo-graphical distance. They are suitable for functions of the system: more flexible operating system, easy to perform, less unwanted ricks occur, the efficiency of controlling a system better. Besides the traditional control methods, the author has applied more modern and smarter algorithms such as artificial intelligence to control a system on the ground or a system moving in the air. In this paper, artificial neural network (ANN) is applied for a flight model to demonstrate its effectiveness in all cases. ANN in this article to show off its amazing application for flying devices. This is a useful method because it is highly secure. Simulation is done by Matlab.
Stevens, B. L., Lewis, F. L., & Johnson, E. N. (2015). Aircraft control and simulation: dynamics, control design, and autonomous system. John Wiley & Sons.
Ji Hong Zhu. A survey of Advanced Flight control theory and Application. IMACS Multi conference on Computational Engineering in System Application (CESA). 2006, 1: 655-658.
Z Peng, L Jikai. On new UAV flight control system based on Kalman & PID. IEEE Transaction, International Conference on Harbin. 2011, 2: 819-823.
McRuer, Duane T., Dunstan Graham, and Irving Ashkenas. Aircraft dynamics and automatic control. Vol. 740. Princeton University Press, 2014.
Ohri, J. (2014, December). GA turned LQR and PID controller for aircraft pitch control. In 2014 IEEE 6th India International Conference on Power Electronics (IICPE) (pp. 1-6). IEEE.
Usta, M. A., Akyazi, & Akpinar, A.S. (2011, June). Aircraft roll control system using LQR and fuzzy logic controller. In 2011 International Symposium on Innovations in Intelligent Systems and Applications (pp. 223-227), IEEE.
Hajiyev, C., & Vural, S. Y. (2013). LQR controller with Kalman estimator applied to UAV longitudinal dynamics. Positioning, 4 (1), 36.
MR Rahimi, R Ghasemi, D Sanaei. Designing Discrete Time Optimal Controller for Double inverted pendulum system. International Journal on Numerical and Analytical Methods in Engineering. 2013; 1(1): 3-7.
W Dwiono. Fuzzy PI Controllers Performance on Boost Converter. International Journal of Electrical and Computer Engineering. 2013; 3(2): 215-220.
MRI Sheikh, T Junji. Smoothing Control of Wind Farm Output Fluctuations by Fuzzy Logic Controlled SMES. International Journal of Electrical and Computer Engineering. 2011; 1(2): 119-134.
Z Peng, L Jikai. On new UAV flight control system based on Kalman & PID. IEEE Transaction, International Conference on Harbin. 2011; 2: 819-823.
X Zhou, Z Wang, H Wang. Design of Series Leading Correction PID Controller. IEEE Conference. 2009.
L Yu, D Dipankar, T Gang. Modeling and multivariable adaptive control of aircraft with synthetic jet actuators. IEEE International Conference on Congress on Intelligent Control and Automation. 2008: 2192-2199.
Nambisant PR, Singh SN. Adaptive variable structure control of aircraft with an unknown high-frequency gain matrix. Journal of Guidance, Control, and Dynamics. 2008; 31(1): 194-203.
Young A. Adaptive control design methodology for nonlinear- in-control systems in aircraft applications. Journal of Guidance Control and Dynamics. 2007; 30(6): 1770-1782.
Ji-Hong Zhu. A Survey of Advanced Flight Control Theory and Application. IMACS Multi conference on Computational Engineering in System Application (CESA). 2006; 1: 655-658.
Kazemian HB. Developments of fuzzy PID controllers. Expert Systems. Nov2005; 22(5): 254-264.
H Ogata. Modern Control Engineering. New Jersey: Prentice Hall International Inc. 1997.
CC Lee. Fuzzy Logic in Control System: Fuzzy Logic Controller I. System, Man and Cybernatics, IEEE Transaction.1990; 20: 404-418.
C C Lee. Fuzzy Logic in Control System: Fuzzy Logic Controller II. System, Man and Cybernatics, IEEE Transaction. 1990; 20: 419-435.
EH Mamdani. Application of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Trans, Computer. 1977; C-26(12): 1182-1191.
PJ King, EH Mamdani. The application of fuzzy control systems to industrial processes. Automat. 1977; 13(3): 235-242.
E H Mamdani. Application of fuzzy algorithms for simple dynamic plant. Proc IEE. 1974; 121(12):1585-1588.
Noth, A., Bouabdallah, S., & Siegwart, R. (2006). Dynamic modeling of fixed-wing uavs. Autonomous System Laboratory Report, ETH, Zurich.R.
C. Nelson, 1998, Flight Stability and Automatic Control, McGraw Hill, Second Edition.
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