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Sensor Fault-Tolerant Control Design for Magnetic Brake System

机译：磁制动系统传感器容错控制设计

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摘要

The purpose of the paper is to develop an efficient approach to fault-tolerant control for nonlinear systems of magnetic brakes. The challenging problems of accurate modeling, reliable fault detection and a control design able to compensate for potential sensor faults are addressed. The main idea here is to make use of the repetitive character of the control task and apply iterative learning control based on the observational data to accurately tune the system models for different states of the system. The proposed control scheme uses a learning controller built on a mixture of neural networks that estimate system responses for various operating points; it is then able to adapt to changing working conditions of the device. Then, using the tracking error norm as a sufficient statistic for detection of sensor fault, a simple thresholding technique is provided for verification of the hypothesis on abnormal sensor states. This also makes it possible to start the reconstruction of faulty sensor signals to properly compensate for the control of the system. The paper highlights the components of the complete iterative learning procedure including the system identification, fault detection and fault-tolerant control. Additionally, a series of experiments was conducted for the developed control strategy applied to a magnetic brake system to track the desired reference with the acceptable accuracy level, taking into account various fault scenarios.
机译：本文的目的是为磁性制动器的非线性系统开发有效的容错控制。解决了能够补偿潜在传感器故障的准确建模，可靠故障检测和控制设计的具有挑战性的问题。这里的主要思想是利用控制任务的重复特征，并根据观察数据应用迭代学习控制，以准确地调整系统的不同状态的系统模型。所提出的控制方案使用内置于神经网络混合的学习控制器，这些控制器估计各种操作点的系统响应;然后，它能够适应改变设备的工作条件。然后，使用跟踪误差范围作为检测传感器故障的足够统计量，提供了一种简单的阈值技术，用于验证异常传感器状态的假设。这也使得可以开始重建故障传感器信号以适当地补偿系统的控制。本文突出了完整迭代学习程序的组件，包括系统识别，故障检测和容错控制。另外，对于应用于磁性制动系统的开发控制策略进行了一系列实验，以跟踪所需的参考，以考虑各种故障场景。

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