End-to-End Deep Fault-Tolerant Control
Baimukashev D. Rakhim B. Rubagotti M. Varol H.A.
1 August 2022Institute of Electrical and Electronics Engineers Inc.
IEEE/ASME Transactions on Mechatronics
2022#27Issue 42224 - 2234 pp.
Ideally, accurate sensor measurements are needed to achieve a good performance in the closed-loop control of mechatronic systems. As a consequence, sensor faults will prevent the system from working correctly, unless a fault-tolerant control (FTC) architecture is adopted. As model-based FTC algorithms for nonlinear systems are often challenging to design, this article focuses on a new method for FTC in the presence of sensor faults, based on deep learning. The considered approach replaces the phases of fault detection and isolation and controller design with a single recurrent neural network, which has the value of past sensor measurements in a given time window as input and the current values of the control variables as output. This end-to-end deep FTC method is applied to a mechatronic system composed of a spherical inverted pendulum, whose configuration is changed via reaction wheels, in turn actuated by electric motors. The simulation and experimental results show that the proposed method can handle abrupt faults occurring in link position/velocity sensors.
Deep learning , fault detection and isolation (FDI) , fault-tolerant control (FTC) , mechatronic systems , recurrent neural networks (RNNs)
Text of the article Перейти на текст статьи
Nazarbayev University, Institute of Smart Systems and Artificial Intelligence, Nur-Sultan, 010000, Kazakhstan
Nazarbayev University, Department of Robotics and Mechatronics, Nur-Sultan, 010000, Kazakhstan
Nazarbayev University
Nazarbayev University
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026