Cited by 292Stability and Control of Uncertain SystemsAdaptive Control of Nonlinear SystemsNeural Networks Stability and Synchronization

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Neural Network-Based Event-Triggered State Feedback Control of Nonlinear Continuous-Time Systems

Sahoo, Avimanyu, Xu, Hao, Jagannathan, Sarangapani·2016·IEEE Transactions on Neural Networks and Learning Systems

DOI 10.1109/tnnls.2015.2416259

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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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Authors: Sahoo, A., Xu, H., & Jagannathan, S.
Affiliations: Missouri University of Science and Technology (US)
Texas A&M University – Corpus Christi (US)
ORCID: Sahoo, Avimanyu
Xu, Hao
Jagannathan, Sarangapani
Venue: IEEE Transactions on Neural Networks and Learning Systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2016-03
Volume: 27
Issue: 3
Pages: 497-509
ISSN: 2162-237X (ISSN-L)
2162-2388
DOI: 10.1109/tnnls.2015.2416259
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: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (am)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
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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Neural Network-Based Event-Triggered State Feedback Control of Nonlinear Continuous-Time Systems

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