4.6 Article

Robust Adaptive Tracking Control for Hypersonic Vehicle Based on Interval Type-2 Fuzzy Logic System and Small-Gain Approach

期刊

IEEE TRANSACTIONS ON CYBERNETICS
卷 51, 期 5, 页码 2504-2517

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2019.2927309

关键词

Adaptation models; Aerodynamics; Adaptive systems; Fuzzy logic; Atmospheric modeling; Control design; Vehicle dynamics; Adaptive control; hypersonic vehicle; interval type-2 fuzzy logic system (IT2-FLS); robust control; small-gain approach

资金

  1. National Natural Science Foundation of China [61421004, 61603383, 61603384]
  2. Beijing Advanced Innovation Center of Intelligent Robots and Systems [2016IRS23]

向作者/读者索取更多资源

This paper presents a novel robust adaptive tracking control method for a hypersonic vehicle based on IT2-FLS and small-gain approach, which can effectively handle complicated uncertainties during the flight stage and maintain the stability of the system.
This paper presents a novel robust adaptive tracking control method for a hypersonic vehicle in a cruise flight stage based on interval type-2 fuzzy-logic system (IT2-FLS) and small-gain approach. After the input-output linearization, the vehicle model can be decomposed into two uncertain subsystems by considering matching disturbances and parametric uncertainties. For each subsystem, an interval type-2 Takagi-Sugeno-Kang fuzzy logic system (IT2-TSK-FLS) is then employed to approximate the unavailable model information. Following the idea of a small-gain approach, a composite feedback form for each subsystem is constructed, based on which the final robust adaptive tracking control law is developed. Rigorous stability analysis shows that all signals in the derived closed-loop system are kept uniformly ultimately bounded (UUB). The main contribution of this paper is that the proposed control law for the hypersonic vehicle is with only two adaptive parameters in total which can greatly alleviate the computation and storage burden in practice; meanwhile its superiority over the conventional minimal-learning-parameter (MLP)-based one is specifically illustrated. Comparative numerical simulations of three cases demonstrate the effectiveness of our proposed control method with respect to complicated uncertainties.

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