基于人工神经网络的状态和故障估计

2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA) Pub Date : 2020-12-20 DOI:10.1109/STA50679.2020.9329322

Dhouha Miri, Atef Khedher, K. BenOthman

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引用次数: 1

摘要

本文研究了用Takagi Sugeno方法建模的非线性系统的状态和故障估计。采用未知输入的人工神经网络来估计影响系统的状态和故障。首先，考虑了状态估计问题。第二步，将该方法推广到执行器故障估计中。通过实例验证了该方法的有效性。

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State and faults estimation via Artificial Neural Networks

This paper deals with the state and fault estimation for non linear systems modeled using the Takagi Sugeno approach. An artificial neural network with unknown inputs is used in the objective of estimate state and faults affecting the system. Firstly, the problem of state estimation is considered. In second step, the proposed approach is extended to the actuator fault estimations. The proposed method is applied to an academic example to show its efficiency.

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来源期刊

2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)

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