Fault Estimation and Fault-Tolerant Control of Multiple Faults and Uncertain Disturbances Based on Generalized Sliding Mode Method

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Junjie Zhang, Fangfang Zhang, Jie Li, Yuanhong Liu, Lei Kou, Michaël Antonie Van Wyk
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引用次数: 0

Abstract

Measuring element is always accompanied by uncertainty disturbances and multiple faults during the long time operation of industrial system in complex environment such as blade and pitch system of floating wind turbines. Timely detection of the fault and fault-tolerant control (FTC) perform a significant part in ensuring the stable operation of the system and saving maintenance fees. Sliding mode control is extensively applied to FTC because of its good robustness. Therefore, a sliding mode controller is constructed to guarantee the stability of the industrial plant which suffers multiple faults and uncertain disturbances. At the same time, most existing literature does not take into account several faults and uncertain disturbances. Firstly, employing generalized sliding mode method, we devise a sliding mode observer for evaluating state vector, actuator fault and sensor fault of the system. Secondly, according to the state estimation, we construct a sliding mode controller and prove its validity by Lyapunov's theorem. Our controller achieves satisfactory performance, and it is easier to be implemented in practical engineering than other controllers. Finally, we establish a SIMULINK model of blade and pitch system and make simulation experiments. Simulation outcomes validate the availability and practicability of our controller, which also provides a general scheme for fault estimation and FTC of other industrial plants.

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来源期刊
International Journal of Robust and Nonlinear Control
International Journal of Robust and Nonlinear Control 工程技术-工程:电子与电气
CiteScore
6.70
自引率
20.50%
发文量
505
审稿时长
2.7 months
期刊介绍: Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.
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