Spacecraft formation orbit fault-tolerant control method based on learning neural network sliding mode

Abstract

The invention discloses a spacecraft formation orbit fault-tolerant control method based on a learning neural network sliding mode, and the method comprises the following steps: 1, building a nonlinear relative dynamic model of a spacecraft formation system in consideration of space perturbation and thruster faults; 2, designing a learning neural network sliding mode observer by combining a P-type iterative learning algorithm, a radial basis function neural network model and a sliding mode algorithm, and performing robust reconstruction on a thruster fault; and 3, in combination with the learning neural network model and a sliding mode control algorithm, designing a learning neural network sliding mode fault-tolerant controller, and realizing high-precision maintenance control on nominal configuration when the spacecraft formation system is subjected to space perturbation and thruster faults.

Description

technical field [0001] The invention belongs to the field of spacecraft fault diagnosis and fault-tolerant control, mainly relates to a spacecraft formation orbit fault-tolerant control method based on a learning neural network sliding mode, and can be applied to the fault reconstruction and active fault-tolerant control of the spacecraft formation system. Background technique [0002] As spacecraft play an increasingly important role in communication, remote sensing and other fields, it has become an important direction for the development of space technology to form a formation of multiple spacecraft instead of a single comprehensive spacecraft to complete complex space missions. The spacecraft formation realizes the functional reorganization of multiple sub-spacecraft through strategies, and realizes the functions that are difficult to achieve by a single spacecraft. For example, the spacecraft formation system breaks through the limitation of the structure and size of tra...

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Problems solved by technology

[0004] Although the above-mentioned published patents researched the fault-tolerant control method of spacecraft formation, they did not consider the fault diagnosis and reconstruction of spacecraft formation, and belonged to the category of passive fault-tolerant control.

In addition, through the investigation of existing technologies, no theoretical research on learning neural network sliding mode and applications in spacecraft formation fault diagnosis and fault-tolerant control have been found.

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