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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Most event-based controllers proposed in the literature are state-feedback controllers.
“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]
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- 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 · 2015: 1 · 2014: 1
- 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 › Computer Science › Computer Networks and Communications › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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