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Research Papers

Adaptive Self-Learning Fuzzy Autopilot Design for Uncertain Bank-to-Turn Missiles

[+] Author and Article Information
Hai-Jun Rong

State Key Laboratory for Strength and Vibration
of Mechanical Structures,
School of Aerospace,
Xi'an Jiaotong University,
Xi'an, Shaanxi 710049, China
e-mail: hjrong@mail.xjtu.edu.cn

Zhao-Xu Yang

State Key Laboratory for Strength and Vibration
of Mechanical Structures,
School of Aerospace,
Xi'an Jiaotong University,
Xi'an, Shaanxi 710049, China
e-mail: zhxyang@stu.xjtu.edu.cn

Pak Kin Wong

Department of Electromechanical Engineering,
University of Macau,
Macau, China
e-mail: fstpkw@umac.mo

Chi Man Vong

Department of Computer and Information
Science,
University of Macau,
Macau, China
e-mail: cmvong@umac.mo

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received September 2, 2015; final manuscript received October 21, 2016; published online February 6, 2017. Assoc. Editor: Manish Kumar.

J. Dyn. Sys., Meas., Control 139(4), 041002 (Feb 06, 2017) (12 pages) Paper No: DS-15-1419; doi: 10.1115/1.4035091 History: Received September 02, 2015; Revised October 21, 2016

This paper proposes an adaptive self-learning fuzzy autopilot design for uncertain bank-to-turn (BTT) missiles due to external disturbances and system errors from the variations of the aerodynamic coefficients and control surface loss. The self-learning fuzzy systems called extended sequential adaptive fuzzy inference systems (ESAFISs) are utilized to compensate for these uncertainties in an adaptive backstepping architecture. ESAFIS is a real‐time self-learning fuzzy system with simultaneous structure identification and parameter learning. The fuzzy rules of the ESAFIS can be added or deleted based on the input data. Based on the Lyapunov stability theory, adaptation laws are derived to update the consequent parameters of fuzzy rules, which guarantees both tracking performance and stability. The robust control terms with the adaptive bound-estimation schemes are also designed to compensate for modeling errors of the ESAFISs by augmenting the self-learning fuzzy autopilot control laws. The proposed autopilot is validated under the control surface loss, aerodynamic parameter perturbations, and external disturbances. Simulation study is also compared with a conventional backstepping autopilot and a neural autopilot in terms of the tracking ability. The results illustrate that the designed fuzzy autopilot can obtain better steady‐state and transient performance with the dynamically self-learning ability.

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References

Williams, D. E. , and Friedland, B. , 1987, “ Modern Control Theory for Design of Autopilots for Bank-to-Turn Missiles,” J. Guid. Control Dyn., 10(4), pp. 378–386. [CrossRef]
Carter, L. H. , and Shamma, J. S. , 1996, “ Gain-Scheduled Bank-to-Turn Autopilot Design Using Linear Parameter Varying Transformations,” J. Guid. Control Dyn., 19(5), pp. 1056–1063. [CrossRef]
Schumacher, C. , and Khargonekar, P. P. , 1998, “ Stability Analysis of a Missile Control System With a Dynamic Inversion Controller,” J. Guid. Control Dyn., 21(3), pp. 508–515. [CrossRef]
Lee, S.-Y. , Lee, J.-I. , and Ha, I.-J. , 2001, “ Nonlinear Autopilot for High Maneuverability of Bank-to-Turn Missiles,” IEEE Trans. Aerosp. Electron. Syst., 37(4), pp. 1236–1253. [CrossRef]
Rong, H.-J. , and Zhao, G.-S. , 2013, “ Direct Adaptive Neural Control of Nonlinear Systems With Extreme Learning Machine,” Neural Comput. Appl., 22(3), pp. 577–586. [CrossRef]
Xu, B. , Sun, F. , Liu, H. , and Ren, J. , 2012, “ Adaptive Kriging Controller Design for Hypersonic Flight Vehicle Via Back-Stepping,” IET Control Theory Appl., 6(4), pp. 487–497. [CrossRef]
Xu, B. , and Shi, Z. , 2013, “ Universal Kriging Control of Hypersonic Aircraft Model Using Predictor Model Without Back-Stepping,” IET Control Theory Appl., 7(4), pp. 573–583. [CrossRef]
Xu, B. , Yang, C. , and Shi, Z. , 2014, “ Reinforcement Learning Output Feedback NN Control Using Deterministic Learning Technique,” IEEE Trans. Neural Networks Learn. Syst., 25(3), pp. 635–641. [CrossRef]
Suresh, S. , Omkar, S. N. , Mani, V. , and Sundararajan, N. , 2006, “ Direct Adaptive Neural Flight Controller for F-8 Fighter Aircraft,” J. Guid. Control Dyn., 29(2), pp. 454–464. [CrossRef]
Suresh, S. , Omkar, S. N. , Mani, V. , and Sundararajan, N. , 2005, “ Nonlinear Adaptive Neural Controller for Unstable Aircraft,” J. Guid. Control Dyn., 28(6), pp. 1103–1111. [CrossRef]
Rong, H.-J. , Han, S. , and Zhao, G.-S. , 2014, “ Adaptive Fuzzy Control of Wing-Rock Motion,” Appl. Soft Comput., 14(Pt. B), pp. 181–193. [CrossRef]
McDowell, D. M. , Irwin, G. W. , Lightbody, G. , and McConnell, G. , 1997, “ Hybrid Neural Adaptive Control for Bank-to-Turn Missiles,” IEEE Trans. Control Syst. Technol., 5(3), pp. 297–308. [CrossRef]
Fu, L.-C. , Chang, W.-D. , Yang, J.-H. , and Kuo, T.-S. , 1997, “ Adaptive Robust Bank-to-Turn Missile Autopilot Design Using Neural Networks,” J. Guid. Control Dyn., 20(2), pp. 346–354. [CrossRef]
McFarland, M. B. , and Calise, A. J. , 2000, “ Adaptive Nonlinear Control of Agile Antiair Missiles Using Neural Networks,” IEEE Trans. Control Syst. Technol., 8(5), pp. 749–756. [CrossRef]
Geng, Z. J. , and McCullough, C. L. , 1997, “ Missile Control Using Fuzzy Cerebellar Model Arithmetic Computer Neural Networks,” J. Guid. Control Dyn., 20(3), pp. 557–565. [CrossRef]
Lin, C.-K. , 2005, “ Adaptive Critic Autopilot Design of Bank-to-Turn Missiles Using Fuzzy Basis Function Networks,” IEEE Trans. Syst. Man Cybern., Part B, 35(2), pp. 197–207. [CrossRef]
Uang, H.-J. , and Chen, B.-S. , 2002, “ Robust Adaptive Optimal Tracking Design for Uncertain Missile Systems: A Fuzzy Approach,” Fuzzy Sets Syst., 126(1), pp. 63–87. [CrossRef]
Lin, C.-K. , and Wang, S.-D. , 1998, “ A Self-Organizing Fuzzy Control Approach for Bank-to-Turn Missiles,” Fuzzy Sets Syst., 96(3), pp. 281–306. [CrossRef]
Chen, C.-S. , 2011, “ Robust Self-Organizing Neural-Fuzzy Control With Uncertainty Observer for MIMO Nonlinear Systems,” IEEE Trans. Fuzzy Syst., 9(4), pp. 694–706. [CrossRef]
Gao, Y. , and Er, M. J. , 2003, “ Online Adaptive Fuzzy Neural Identification and Control of a Class of MIMO Nonlinear Systems,” IEEE Trans. Fuzzy Syst., 11(4), pp. 462–477. [CrossRef]
Rong, H.-J. , Sundararajan, N. , Huang, G.-B. , and Zhao, G.-S. , 2011, “ Extended Sequential Adaptive Fuzzy Inference System for Classification Problems,” Evol. Syst., 2(2), pp. 71–82. [CrossRef]
Lee, T. , and Kim, Y. , 2001, “ Nonlinear Adaptive Flight Control Using Backstepping and Neural Networks Controller,” J. Guid. Control Dyn., 24(4), pp. 675–682. [CrossRef]
Xu, B. , Sun, F. , Yang, C. , Gao, D. , and Ren, J. , 2011, “ Adaptive Discrete-Time Controller Design With Neural Network for Hypersonic Flight Vehicle Via Back-Stepping,” Int. J. Control, 84(9), pp. 1543–1552. [CrossRef]
Pashilkar, A. A. , Sundararajan, N. , and Saratchandran, P. , 2006, “ Adaptive Back-Stepping Neural Controller for Reconfigurable Flight Control Systems,” IEEE Trans. Control Syst. Technol., 14(3), pp. 553–561. [CrossRef]
Rong, H.-J. , Sundararajan, N. , Huang, G.-B. , and Saratchandran, P. , 2006, “ Sequential Adaptive Fuzzy Inference System (SAFIS) for Nonlinear System Identification and Prediction,” Fuzzy Sets Syst., 157(9), pp. 1260–1275. [CrossRef]
Wang, L.-X. , 1994, Adaptive Fuzzy Systems and Control: Design and Stability Analysis, Prentice-Hall, Upper Saddle River, NJ.
Sundararajan, N. , Saratchandran, P. , and Yan, L. , 2001, Fully Tuned Radial Basis Function Neural Networks for Flight Control, Kluwer Academic Publishers, Boston, MA.

Figures

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Fig. 1

Configuration of missile system

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Fig. 2

Structure of backstepping autopilot

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Fig. 3

Structure of adaptive self-learning fuzzy autopilot

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Fig. 5

Controlled state comparisons with RBF-based neural autopilot: (a) fuzzy rules/neurons, (b) angular rate, and (c) control surface deflection

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Fig. 4

Controlled state comparisons with conventional backstepping autopilot: (a) angle-of-attack, (b) sideslip angle, and (c) roll angle

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Fig. 6

Evolution of fuzzy rules/neurons (a), angular rate (b), and control surface deflection (c)

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Fig. 7

Rule evolution process together with time history of adding and pruning criteria

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