Huang Qiang, Gao Zhi-Wei, Liu Yuanhong
Research Centre for Digitalization and Intelligent Diagnosis to New Energies, College of Electrical and Information Engineering, Northeast Petroleum University, Daqing 163318, China.
Sensors (Basel). 2024 May 19;24(10):3224. doi: 10.3390/s24103224.
Sensors are a key component in industrial automation systems. A fault or malfunction in sensors may degrade control system performance. An engineering system model is usually disturbed by input uncertainties, which brings a challenge for monitoring, diagnosis, and control. In this study, a novel estimation technique, called adaptive unknown-input observer, is proposed to simultaneously reconstruct sensor faults as well as system states. Specifically, the unknown input observer is used to decouple partial disturbances, the un-decoupled disturbances are attenuated by the optimization using linear matrix inequalities, and the adaptive technique is explored to track sensor faults. As a result, a robust reconstruction of the sensor fault as well as system states is then achieved. Furthermore, the proposed robustly adaptive fault reconstruction technique is extended to Lipschitz nonlinear systems subjected to sensor faults and unknown input uncertainties. Finally, the effectiveness of the algorithms is demonstrated using an aircraft system model and robotic arm and comparison studies.
传感器是工业自动化系统中的关键组件。传感器的故障或失灵可能会降低控制系统的性能。工程系统模型通常会受到输入不确定性的干扰，这给监测、诊断和控制带来了挑战。在本研究中，提出了一种名为自适应未知输入观测器的新型估计技术，用于同时重构传感器故障和系统状态。具体而言，未知输入观测器用于解耦部分干扰，通过使用线性矩阵不等式的优化来衰减未解耦的干扰，并探索自适应技术来跟踪传感器故障。结果，实现了对传感器故障和系统状态的鲁棒重构。此外，所提出的鲁棒自适应故障重构技术被扩展到受传感器故障和未知输入不确定性影响的Lipschitz非线性系统。最后，使用飞机系统模型和机器人手臂进行了算法有效性的演示和比较研究。
