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
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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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. 4

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

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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. 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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