Adaptive neural networks-based fixed-time fault-tolerant consensus tracking for uncertain multiple Euler–Lagrange systems. (October 2022)
- Record Type:
- Journal Article
- Title:
- Adaptive neural networks-based fixed-time fault-tolerant consensus tracking for uncertain multiple Euler–Lagrange systems. (October 2022)
- Main Title:
- Adaptive neural networks-based fixed-time fault-tolerant consensus tracking for uncertain multiple Euler–Lagrange systems
- Authors:
- Li, He
Liu, Cheng-Lin
Zhang, Ya
Chen, Yang-Yang - Abstract:
- Abstract: This article addresses the fixed-time fault-tolerant consensus tracking (FTCT) problem for uncertain multiple Euler–Lagrange systems (MELS) with the digraph and actuator faults. Firstly, a fixed-time distributed observer (DO) is built to estimate the states of leader. Then, the approximation ability of radical basic function neural networks (RBFNN) is exploited to deal with the system uncertainties. By using backstepping technique, the novel fault-tolerant local control protocol (FTLCP) and updating laws are designed to ensure that error variables converge to the small adjacent area of zero within fixed-time. Eventually, the effectiveness and practicality of the presented method are demonstrated through a typical MELS simulation. Highlights: For multiple Euler–Lagrange systems, practical factors such as asymmetry of directed communication networks, system uncertainties and actuator faults are comprehensively considered under the fixed-time convergence framework, which greatly expand the application scope of this article. A novel distributed observer of multiple Euler–Lagrange systems under the digraph is proposed. Furthermore, the estimation errors can converge to the origin within fixed time, and the estimation of the upper bound of setting time is relatively less conservative. A novel and effective adaptive fault-tolerant local control protocol is proposed to ensure the tracking errors converge into a compact set near zero in fixed-time. The constructedAbstract: This article addresses the fixed-time fault-tolerant consensus tracking (FTCT) problem for uncertain multiple Euler–Lagrange systems (MELS) with the digraph and actuator faults. Firstly, a fixed-time distributed observer (DO) is built to estimate the states of leader. Then, the approximation ability of radical basic function neural networks (RBFNN) is exploited to deal with the system uncertainties. By using backstepping technique, the novel fault-tolerant local control protocol (FTLCP) and updating laws are designed to ensure that error variables converge to the small adjacent area of zero within fixed-time. Eventually, the effectiveness and practicality of the presented method are demonstrated through a typical MELS simulation. Highlights: For multiple Euler–Lagrange systems, practical factors such as asymmetry of directed communication networks, system uncertainties and actuator faults are comprehensively considered under the fixed-time convergence framework, which greatly expand the application scope of this article. A novel distributed observer of multiple Euler–Lagrange systems under the digraph is proposed. Furthermore, the estimation errors can converge to the origin within fixed time, and the estimation of the upper bound of setting time is relatively less conservative. A novel and effective adaptive fault-tolerant local control protocol is proposed to ensure the tracking errors converge into a compact set near zero in fixed-time. The constructed distributed observer based local tracking control structure significantly reduces the difficulty of control protocol design and stability analysis. … (more)
- Is Part Of:
- ISA transactions. Volume 129(2022)Part A
- Journal:
- ISA transactions
- Issue:
- Volume 129(2022)Part A
- Issue Display:
- Volume 129, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 129
- Issue:
- 2022
- Issue Sort Value:
- 2022-0129-2022-0000
- Page Start:
- 102
- Page End:
- 113
- Publication Date:
- 2022-10
- Subjects:
- Multiple Euler–Lagrange systems -- Distributed observer -- Fixed-time consensus tracking -- Radical basic function neural networks -- Adaptive control
Engineering instruments -- Periodicals
Engineering instruments
Periodicals
Electronic journals
629.805 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00190578 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.isatra.2021.12.023 ↗
- Languages:
- English
- ISSNs:
- 0019-0578
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4582.700000
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