The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Neural Network-Based Event-Triggered State Feedback Control of Nonlinear Continuous-Time Systems
Abstract
This paper presents a novel approximation-based event-triggered control of multi-input multi-output uncertain nonlinear continuous-time systems in affine form. The controller is approximated using a linearly parameterized neural network (NN) in the context of event-based sampling. After revisiting the NN approximation property in the context of event-based sampling, an event-triggered condition is proposed using the Lyapunov technique to reduce the network resource utilization and to generate the required number of events for the NN approximation. In addition, a novel weight update law for aperiodic tuning of the NN weights at triggered instants is proposed to relax the knowledge of complete system dynamics and to reduce the computation when compared with the traditional NN-based control. Nonetheless, a nonzero positive lower bound for the inter-event times is guaranteed to avoid the accumulation of events or Zeno behavior. For analyzing the stability, the event-triggered system is modeled as a nonlinear impulsive dynamical system and the Lyapunov technique is used to show local ultimate boundedness of all signals. Furthermore, in order to overcome the unnecessary triggered events when the system states are inside the ultimate bound, a dead-zone operator is used to reset the event-trigger errors to zero. Finally, the analytical design is substantiated with numerical results.
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“Several event-based controllers have been proposed in the literature, most of which are state-feedback controllers [35-39].”
From Data-driven control of hydraulic servo actuator: An event-triggered adaptive dynamic programming approach (2023) · cites this paper as [39]
Citations
Cites
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
Event-Triggered Real-Time Scheduling of Stabilizing Control Tasks
Paulo Tabuada, B. (2007) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2007.904277 · Cited by 4,756
Neural Network Control Of Robot Manipulators And Non-Linear Systems
Lewis, F. L., Jagannathan, S., & Yesildirak, A. (2020) · CRC Press eBooks · doi:10.1201/9781003062714 · Cited by 1,872
Self-citationGoebel, R., Sanfelice, R. G., & Teel, A. R. (2009) · IEEE Control Systems · doi:10.1109/mcs.2008.931718 · Cited by 1,656
Event-Triggering in Distributed Networked Control Systems
Wang, X., & Lemmon, M. D. (2010) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2010.2057951 · Cited by 1,171
A state-feedback approach to event-based control
Lunze, J., & Lehmann, D. (2009) · Automatica · doi:10.1016/j.automatica.2009.10.035 · Cited by 1,024
Ioannou, P., & Fi̇dan, B. (2006) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9780898718652 · Cited by 977
Donkers, M. C. F., & Heemels, W. P. M. H. (2011) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2011.2174696 · Cited by 872
To Sample or not to Sample: Self-Triggered Control for Nonlinear Systems
Anta, A., & Paulo Tabuada, B. (2010) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2010.2042980 · Cited by 871
Comparison of periodic and event based sampling for first-order stochastic systems
Åström, K. J., & Bernhardsson, B. (1999) · IFAC Proceedings Volumes · doi:10.1016/s1474-6670(17)56852-4 · Cited by 826
Research works printed but not linked: 2
- wang, 2010, ${\mathcal{ L}}_{1}$ adaptive control of event-triggered networked systems, Proc Amer Control Conf, 2458
- khalil, 2002, Nonlinear Systems
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Zhang, X., Han, Q., & Zhang, B. (2016) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2016.2607150 · Cited by 780
Adaptive Critic Nonlinear Robust Control: A Survey
Wang, D., He, H., & Liu, D. (2017) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2712188 · Cited by 384
Model-Based Adaptive Event-Triggered Control of Strict-Feedback Nonlinear Systems
Li, Y., & Yang, G. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2650238 · Cited by 274
An Event-Triggered ADP Control Approach for Continuous-Time System With Unknown Internal States
Zhong, X., & He, H. (2016) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2016.2523878 · Cited by 263
Lakshmanan, S., Mani, P., Rakkiyappan, R., & Joo, Y. H. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2877410 · Cited by 234
Metadata
- Authors
- Sahoo, A., Xu, H., & Jagannathan, S.
- Affiliations
- Missouri University of Science and Technology (US)
- Texas A&M University – Corpus Christi (US)
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2016-03
- Volume
- 27
- Issue
- 3
- Pages
- 497-509
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W1978921618
- Cited by
- 292
- References
- 25
- Citations by year
- 2026: 7 · 2025: 27 · 2024: 15 · 2023: 34 · 2022: 29 · 2021: 41 · 2020: 33 · 2019: 30 · 2018: 28 · 2017: 27 · 2016: 19
- FWCI
- 23.14
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Neural Networks and Learning Systems (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Intelligent Systems Center, Missouri University of Science and Technology, Rolla, MO, USA
- National Science Foundation (ECCS 1406533)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Stability and Control of Uncertain Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Adaptive Control of Nonlinear Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Neural Networks Stability and Synchronization
- Keywords
- event-triggered control, neural network approximation, continuous-time nonlinear systems, MIMO, affine systems, state feedback control, Zeno behavior avoidance, inter-event time lower bound
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