A Back-Stepping Controller Scheme for Altitude Subsystem of Hypersonic Missile with ANFIS Algorithm
محورهای موضوعی : Journal of Computer & RoboticsDavood Allahverdy 1 , Ahmad Fakharian 2
1 - Science and Research Branch, Islamic Azad University, Tehran, Iran
2 - Faculty of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
کلید واژه: Adaptive Neuro-Fuzzy Inference System (ANFIS), Hypersonic Missile, Back-Stepping, Maneuvering Target,
چکیده مقاله :
In this paper, we propose a back-stepping controller scheme for the altitude subsystem of hypersonic missile of which model is nonlinear, non-minimum phase, uncertain, and highly coupled. In the scheme, the guidance law is selected as a desired flight path angle that derived from the sliding mode control method. The back-stepping technique is designed and analyzed for the altitude dynamics of hypersonic missiles for maneuvering targets. Additionally, the algorithm of adaptive neuro-fuzzy inference system (ANFIS) is used for estimating the uncertainty of model parameters and Lyapunove theorem is used to examine the stability of closed-loop systems. The simulation indicates that the proposed scheme has shown effectiveness of the control strategy, high accuracy, stability of states, and low-amplitude control inputs in the presence of uncertainties with external disturbance.
1] Kokotovic, P. V., "The Joy of Feedback Nonlinear and Adaptive: 1991 Bode Prize Lecture", IEEE Control Systems, vol. 12, no. 3, pp. 7-17 (1992).
[2] Chen, M.; Ge, S. S.; How, B. V. E., "Robust Adaptive Neural Network Control for a Class of Uncertain MIMO Nonlinear Systems with Input Nonlinearities", IEEE Transactions on Neural Networks, vol. 21, no. 5, pp. 796-812 (2010).
[3] Chen, L. J.; Narendra, K. S., "Nonlinear Adaptive Control Using Neural Networks and Multiple Models", Automatica, vol. 37, no. 8, pp. 1245-1255 (2001).
[4] Allahverdy, D.; Fakharian, A.; Menhaj, M. B., "Back‑Stepping Integral Sliding Mode Control with Iterative Learning Control Algorithm for Quadrotor UAVs", Journal of Electrical Engineering & Technology, vol. 14, no. 6, pp. 2539-2547 (2019).
[5] Fu, W.; Yan, B.; Chang, X.; Yan, J., "Guidance Law and Neural Control for Hypersonic Missile to Track Target", Hindawi Publishing Corporation, vol. 2016, ID 6219609, 10 pages (2016).
[6] Xu, B.; Huang, X.; Wang, D.; Sun, F., "Dynamic surface control of constrained hypersonic flight models with parameter estimation and actuator compensation", Asian Journal of Control, vol. 16, no. 1, pp. 162-174 (2014).
[7] Liu, X.; Huang, W.; Du, L., "An Integrated Guidance and Control Approach in Three Dimensional Space for Hypersonic Missile Constrained by Impact Angles", Beijing Aerospace Automatic Control Institute, China (2016).
[8] Jang, J. S. R., "ANFIS: adaptive network-based fuzzy inference systems", IEEE Trans. Syst., Man Cybern., vol. 23, pp. 665-685 (1993).
[9] Azimi, V.; Nekoui, M. A.; Fakharian, A., "Robust Multi-Objective H2/H∞ Tracking Control Based on the Takagi–Sugeno Fuzzy Model for a Class of Nonlinear Uncertain Drive Systems", Journal of System and Control Engineering, vol. 226, no. 8, pp. 1107-1118 (2012).
[10] Azimi, V.; Menhaj, M. B., "Tool Position Tracking Control of a Nonlinear Uncertain Flexible Robot Manipulator by Using Robust H2/H∞ Controller via T–S Fuzzy Model", Indian Academy of Sciences, vol. 40, no. 2, pp. 307-333 (2015).
[11] Wu, L.; Su, X.; Shi, P.; Qiu, J., "Model approximation for discrete-time state-delay systems in the TS fuzzy framework", IEEE Transactions on Fuzzy Systems, vol. 19, no. 2, pp. 366-378 (2011).
[12] Xu, B.; Shi, Z.; Yang, C., "Composite Fuzzy Control of a Class of Uncertain Nonlinear Systems with Disturbance Observer", Nonlinear Dynamics, vol. 80, no. 12, pp. 341-351 (2015).
[13] Xu, B.; Shi, Z.; Yang, C.; Sun, F., "Composite Neural Dynamic Surface Control of a Class of Uncertain Nonlinear Systems in Strict-Feedback Form", IEEE Transactions on Cybernetics, vol. 44, no. 12, pp. 2626-2634 (2014).
[14] Wu, Z. G.; Shi, P.; Su, H.; Chu, J., "Local Synchronization of Chaotic Neural Networks with Sampled-Data and Saturating Actuators", IEEE Transactions on Cybernetics, vol. 44, no. 12, pp. 2635-2645 (2014).
[15] Xu, B.; Zhang, Q.; Pan, Y., "Neural Network Based Dynamic Surface Control of Hypersonic Flight Dynamics using Small-Gain Theorem", Neuro computing, vol. 173, part 3, pp. 690-699 (2016).
[16] Chen, M.; Shi, P.; Lim, C. C., "Adaptive Neural Fault-Tolerant Control of a 3-Dof Model Helicopter System", IEEE Transactions on Systems, Man, and Cybernetics Systems, vol. 46, no. 2, pp. 260-270 (2015).
[17] Azimi, V.; Nekoui, M. A.; Fakharian, A., "Robust multi-objective H_2 / H_∞tracking control based on the Takagi–Sugeno fuzzy model for a class of nonlinear uncertain drive systems", SAGE, vol. 226, no. 8, pp. 1107-1118 (2012).
[18] Zhou, D.; Mu, C.; Xu, W., "Adaptive Sliding-Mode Guidance of a Homing Missile", Journal of Guidance, Control, and Dynamics, vol. 22, no. 4, pp. 589-594 (1999).
[19] Parker, J. T.; Serrani, A.; Yurkovich, S.; Bolender, M. A.; Doman, D.
B., "Control-Oriented Modeling of an Air-Breathing Hypersonic Vehicle", Journal of Guidance, Control, and Dynamics, vol. 30, No. 3, pp. 856-869 (2007).
[20] Syahputra, R.; Soesanti, I.; Ashari, M., "Performance Enhancement of Distribution Network with DG Integration Using Modified PSO Algorithm", Journal of Electrical Systems, vol. 12, no. 1, pp. 1-19 (2016).
[21] Allahverdy, D.; Fakharian, A., "Back-Stepping Controller Design for Altitude subsysytem of Hypersonic Missile with Takagi-Sugeno Fuzzy Estimator", The 9th joint Conference on Artificial Intelligence & Robotics and the 2nd RoboCup Asia-Pacific International Symposium, Kish, Iran, pp. 80-87 (2018).
