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Type: Journal article
Title: Neural-network-based adaptive backstepping control with application to spacecraft attitude regulation
Author: Cao, X.
Shi, P.
Li, Z.
Liu, M.
Citation: IEEE Transactions on Neural Networks and Learning Systems, 2018; 29(9):4303-4313
Publisher: IEEE
Issue Date: 2018
ISSN: 2162-237X
Statement of
Xibin Cao, Peng Shi, Zhuoshi Li, and Ming Liu
Abstract: This paper investigates the neural-network-based adaptive control problem for a class of continuous-time nonlinear systems with actuator faults and external disturbances. The model uncertainties in the system are not required to satisfy the norm-bounded assumption, and the exact information for components faults and external disturbance is totally unknown, which represents more general cases in practical systems. An indirect adaptive backstepping control strategy is proposed to cope with the stabilization problem, where the unknown nonlinearity is approximated by the adaptive neural-network scheme, and the loss of effectiveness of actuators faults and the norm bounds of exogenous disturbances are estimated via designed online adaptive updating laws. The developed adaptive backstepping control law can ensure the asymptotic stability of the fault closed-loop system despite of unknown nonlinear function, actuator faults, and disturbances. Finally, an application example based on spacecraft attitude regulation is provided to demonstrate the effectiveness and the potential of the developed new neural adaptive control approach.
Keywords: Actuator degradation; adaptive control; backstepping control; neural network; spacecraft attitude regulation
Description: Date of publication November 1, 2017
Rights: © 2017 IEEE
DOI: 10.1109/TNNLS.2017.2756993
Grant ID: 91438202
Appears in Collections:Aurora harvest 3
Electrical and Electronic Engineering publications

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