Memory Augmented Neural Network-Based Intelligent Adaptive Fault Tolerant Control for a Class of Launch Vehicles Using Second-Order Disturbance Observer | Zendy

Haipeng Chen | Zendy; Kang Chen | Zendy; Wenxing Fu | Zendy

Open Access

Memory Augmented Neural Network-Based Intelligent Adaptive Fault Tolerant Control for a Class of Launch Vehicles Using Second-Order Disturbance Observer

Author(s) -

Haipeng Chen,

Kang Chen,

Wenxing Fu

Publication year - 2021

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2021/9961278

Subject(s) - control theory (sociology) , actuator , observer (physics) , controller (irrigation) , artificial neural network , bounded function , compensation (psychology) , fault tolerance , engineering , fault (geology) , control engineering , class (philosophy) , computer science , control (management) , artificial intelligence , mathematics , quantum mechanics , psychology , mathematical analysis , physics , seismology , geology , psychoanalysis , agronomy , reliability engineering , biology

This paper focuses on the MANN-based intelligent adaptive fault tolerant control for a class of launch vehicles. Firstly, the attitude dynamic model of the launch vehicles suffering from the actuator faults and disturbances has been formulated. Secondly, the second-order disturbance observer has been designed for the launch vehicle to achieve the exact estimation and compensation of the time-varying disturbances. Meanwhile, the MANN has been introduced as online approximator, suppressing the adverse influence of the unknown nonlinearities. Moreover, several adaptive laws have been proposed to achieve the quick response to the actuator faults and the update of the MANN weights. As a result, the MANN-based intelligent adaptive fault tolerant control structure has been constructed for the launch vehicles. It has been proven that all the signals in the closed-loop system are bounded. Simulation results demonstrate the desired performance and the advantages of the proposed control algorithm.

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