Fast Anomaly Detection and Joint State Estimation for Perturbed Nonlinear Systems With Prolonged Output Anomalies

Guangdeng Chen; Xiao-Jie Peng; Yan Lei; Chao Huang; Hongyi Li

doi:10.1109/JAS.2025.125900

Volume 13 Issue 6

Jun. 2026

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2026 > 13(6): 1301-1313

G. Chen, X.-J. Peng, Y. Lei, C. Huang, and H. Li, “Fast anomaly detection and joint state estimation for perturbed nonlinear systems with prolonged output anomalies,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 6, pp. 1301–1313, Jun. 2026. doi: 10.1109/JAS.2025.125900

Citation:

G. Chen, X.-J. Peng, Y. Lei, C. Huang, and H. Li, “Fast anomaly detection and joint state estimation for perturbed nonlinear systems with prolonged output anomalies,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 6, pp. 1301–1313, Jun. 2026. doi: 10.1109/JAS.2025.125900

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Fast Anomaly Detection and Joint State Estimation for Perturbed Nonlinear Systems With Prolonged Output Anomalies

doi: 10.1109/JAS.2025.125900

Funds: This work was supported in part by the National Natural Science Foundation of China (62403396, U25A20474, 62303189, 62433018) and the China Postdoctoral Science Foundation (2024M762667, 2025T180463)

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Author Bio:
Guangdeng Chen received the M.S. and Ph.D. degrees in control science and engineering from Guangdong University of Technology in 2021 and 2023, respectively. He is currently a Postdoctoral Researcher with the College of Electronic and Information Engineering, Southwest University. His research interests include nonlinear systems, cyber-physical systems, and attack detection

Xiao-Jie Peng received the B.S. degree in automation from Hunan University of Science and Technology in 2017, and the Ph.D. degree in control science and engineering from China University of Geosciences in 2024. She is currently a Research Assistant with the College of Electronic and Information Engineering, Southwest University. Her current research interests include time-delay systems and multi-agent systems

Yan Lei received the B.S. degree in automatic control from Hohai University in 2016 and the Ph.D. degree in control theory and control engineering with the Huazhong University of Science and Technology in 2021. He is currently an Associate Professor with the College of Electronic and Information Engineering, Southwest University. His research interests include hybrid systems, singularly perturbed systems, multi-agent systems, and event-triggered control

Chao Huang (Senior Member, IEEE) is a Senior Lecturer at the School of Electrical and Mechanical Engineering, Adelaide University, Adelaide, Australia. Her research interests include human-machine collaboration, fault-tolerant control, mobile robots (EV, UAV), and path planning and control. Dr. Huang serves as an Associate Editor for IEEE Transactions on Transportation Electrification, IEEE Transactions on Consumer Electronics, and Engineering Applications of Artificial Intelligence

Hongyi Li (Senior Member, IEEE) received the Ph.D. degree in intelligent control from the University of Portsmouth, Portsmouth, U.K., in 2012. He was a Research Associate with the Department of Mechanical Engineering, University of Hong Kong, Hong Kong, China, and Hong Kong Polytechnic University, Hong Kong, China. He was a Visiting Principal Fellow with the Faculty of Engineering and Information Sciences, University of Wollongong, Wollongong, Australia. He is currently a Professor with the Southwest University. His research interests include intelligent control, cooperative control and their applications. Prof. Li was the recipient of the 2016 and 2019 Andrew P. Sage Best Transactions Paper Awards from IEEE System, Man, Cybernetics Society, Best Paper Award in Theory from ICCSS 2017, and Zadeh Best Student Paper from IEEE ICCSS 2019. He currently serves or has served on the Editorial Board of several international journals, including IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Systems, Man and Cybernetics: Systems, IEEE Transactions on Automation Science and Engineering, IEEE Transactions on Cognitive and Developmental Systems, SCIENCE CHINA Information Sciences, IEEE/CAA Journal of Automatica Sinica, Journal of Systems Science and Complexity, and Asian Journal of Control. He was also the Guest Editor of IEEE Transactions on Cybernetics and IET Control Theory and Applications
Corresponding author: Yan Lei, e-mail: yanleis@swu.edu.cn
Received Date: 2025-04-11
Revised Date: 2025-07-17
Accepted Date: 2025-09-03

Abstract

Abstract

State estimation under anomalies such as disturbances and faults remains a fundamental challenge in nonlinear systems, with its difficulty further exacerbated by potential network attacks. This study investigates fast anomaly detection and state estimation for perturbed nonlinear systems where actual outputs may be anomalous over a prolonged period. First, a fixed-time observer is constructed. By leveraging integral-type composite Lyapunov functions and homogeneity theory, the error bounds are proven under varying scenarios involving model disturbances, measurement noise, and nonlinearity. Based on these bounds, a fast anomaly detection mechanism is designed. Next, a cascade predictor is developed based on the fixed-time observer, which uses historical outputs from a previous time window to predict the current system state. Simultaneously, an algorithm is proposed to determine the reference historical output based on anomaly detection results, improving long-term prediction accuracy and mitigating the impact of anomaly detection delays. Finally, the secure state estimation is derived by fusing states from the fixed-time observer and the cascade predictor, depending on the anomaly detection results. The effectiveness of the proposed method is demonstrated through simulations on autonomous vehicles.
- Anomaly detection,
- cascade predictor,
- fixed-time observer,
- output anomalies,
- secure state estimation

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