Machine Learning in Event-Triggered Control: Recent Advances and Open Issues

Dirk Pesch; Kritchai Witheephanich; Leila Sedghi; Md. Noor-A-Rahim; Zohaib Ijaz

REVIEW 4 major objections 6 minor 135 references

Machine Learning in Event-Triggered Control: Recent Advances and Open Issues

T0 review · 4 major / 6 minor · reviewed 2026-08-27 · deepseek-v4-flash

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Pith's one-line read This survey claims machine-learning-based event-triggered control splits cleanly into three purposes — learning the system model, solving the control optimization, or doing both — and that all surveyed work ignores real network faults.

desk verdict A useful first-pass map of ML-based event-triggered control, but the taxonomy and open-issues claims need cleanup before the survey can be trusted. read the letter →

arxiv 2009.12783 v2 pith:QJE4VQVH submitted 2020-09-27 eess.SY cs.LGcs.SY

Leila Sedghi , Zohaib Ijaz , Md. Noor-A-Rahim , Kritchai Witheephanich , Dirk Pesch This is my paper · ORCID

classification eess.SYcs.LGcs.SY

keywords machinelearningevent-triggeredcontrolnetworkedsystemsreinforcementdeepneuralnetworksstatisticalsurvey

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that the scattered literature combining machine learning with event-triggered control can be organized by what the learning is for: learning the plant's dynamics, solving the control optimization problem, or doing both simultaneously. It reviews 51 works across statistical learning, neural networks, reinforcement learning, and deep reinforcement learning, and further classifies each by whether the learned policy shapes control, communication, or both. The payoff of this map is a concrete open-problems list: the paper contends that no reviewed work incorporates network-induced imperfections such as packet loss, delay, or quantization, and that learning the network itself is a largely untouched question. A reader should care because a reliable taxonomy makes gaps visible and lets new research target them directly.

What carries the argument

The central instrument is a three-way classification scheme, instantiated in three tables that assign each of 51 references to a purpose of machine learning: model-dynamics learning, optimization of control and communication, or joint learning and optimization. The scheme's load-bearing distinctions are the learning technique (statistical learning, neural networks, reinforcement learning including deep RL) and the policy type (control, communication, or both). For the joint-learning category the paper names the actor-critic-identifier architecture — three neural networks in which an identifier learns uncertain dynamics, a critic approximates the value function, and an actor produces the control — as the standard mechanism carrying those works.

What would settle it

Read each of the 51 cited papers' problem statements and check whether packet loss, communication delay, or quantization appears in the learning or triggering formulation; if even one surveyed paper treats any of these network-induced imperfections, the Section VI claim that none do is false. A second check: attempt to place a published ML-ETC method that learns the plant model and solves an optimization problem in inseparable order into exactly one of the three categories.

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Extended reading notes

Core claim

The paper's central claim is that ML-based ETC methods fall into three categories depending on the purpose of the machine learning: model-dynamics learning, optimization of control and communication, and joint learning and optimization. The paper reviews 51 references, sorts them into three tables, and labels each approach by learning technique (statistical learning, neural networks, reinforcement learning, deep reinforcement learning) and by whether the learned policy affects control, communication, or both. On the open-issues side, the paper asserts that none of the reviewed works incorporate network-induced imperfections into their learning algorithms and that learning the network model jointly with the system model remains mostly unaddressed. The contribution is therefore a map of a young field together with a concrete agenda for making ML-based ETC work over real wireless channels.

Load-bearing premise

The taxonomy's value depends on the selected 51 references being representative and correctly categorized, yet the survey gives no search or inclusion protocol and one table still contains an unfinished placeholder row, so a miscategorized or omitted work could distort the classification and the open-issues list.

Editorial extensions

If this is right

Any new ML-ETC contribution can be positioned by asking what the learning is for; the survey claims the three categories exhaust the space, making misalignment visible.
Because the survey claims no reviewed paper treats network-induced imperfections, the next generation of ML-ETC designs must build delay, packet loss, and quantization into the learning and triggering formulation, along with new data sets.
If the taxonomy holds, transferring ML-ETC to a new application reduces to picking a category: model learning where models are poor, optimization where models are good but the control problem is hard, joint learning where both are hard.
The paper argues that self-triggered control, which predicts the next communication instant instead of continuously monitoring, is a more resource-efficient partner for ML than reactive ETC.
For scalability and privacy in large-scale deployments, the survey points to federated learning and edge/cloud offloading as necessary research directions.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

One could test the taxonomy by coding the same 51 papers a second time from their abstracts alone; the categories likely blur because any optimization method that relies on data is implicitly learning something about the plant.
A concrete testable prediction of the open-issues claim: scanning the 51 cited papers for 'packet loss', 'delay', and 'quantization' in their problem formulations should return zero hits; if any hit appears, the claim needs qualification.
The paper's suggestion that triggering should be based on quantized state values implies existing design templates, where thresholds compare ideal state norms, may need reworking before ML-ETC deploys on real digital links.
The paper implicitly licenses treating the wireless channel as part of the system to be learned rather than as a perfect pipe, merging its model-learning and network-learning open issues into one research program.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Colleague,

Here is my read of arXiv:2009.12783, the survey on machine learning in event-triggered control.

It is a usable orientation map, not a deep contribution. The genuinely new part is the three-way taxonomy: ML for learning model dynamics, ML for solving control/communication optimization problems, and ML for joint learning and optimization. That is a reasonable frame for roughly 51 papers, and the paper organizes them into tables by learning approach, control structure, communication policy, and experimental validation. The open-issues list, covering packet loss, delay, quantization, mobility, scalability, cloud/edge offloading, and security, is sensible and will help people locate gaps. A newcomer would get real value from this as a starting point.

What the taxonomy does not do is cut cleanly. The stress-test note is right. [23] is both a GP dynamics learner and a self-triggered controller that computes optimal control and communication policies; [12] explicitly does joint learning of control and communication. Both sit under "model dynamics learning" in Table 1, with no stated primary-purpose rule. The paper's Section VI.A claim that none of the reviewed works consider network-induced imperfections is contradicted by its own Section IV.A treatment of [44], which addresses denial-of-service attacks. The placeholder row "citelu2022event" in Table 2 is small but concrete evidence that the tables were not finalized. None of these are fatal to the survey's purpose, but they mean the central map cannot be used as-is; a reader would need to check each assignment against the original papers.

I also agree with the reader's weaker concerns. There is no search protocol or inclusion criteria, so completeness is unverifiable, and the prose occasionally overreaches, for example calling computational load "the only disadvantage" of combining ML and ETC. Several sentences in the body are incomplete, which suggests the manuscript was not carefully copyedited.

On balance, this deserves peer review, not rejection. The topic is timely, the taxonomy is defensible as a first pass, and the open-issues discussion is genuinely thought-provoking. But the authors need to fix the internal contradictions, either restate the taxonomy as a primary-intent heuristic or make the classes disjoint by definition, reclassify [23] and [12], and remove placeholder entries from the tables. If that is done, the survey would become a solid resource. I would not cite it in its current form, but I would keep it on the reading list and revisit after revision.

Referee Report

4 major / 6 minor

Summary. This manuscript surveys the use of machine learning in event-triggered control (ETC). It proposes a three-way classification of the literature by the purpose of machine learning: learning model dynamics, solving an optimal control or communication problem, and jointly learning dynamics and optimizing control. The paper reviews roughly fifty works in three corresponding sections, summarizes them in three tables, and concludes with a discussion of open issues including communication errors, quantization, mobility, scalability, cloud/edge computing, joint learning of system and network models, energy efficiency, self-triggered control, and security. The paper contains no new mathematical derivations; its contribution is the taxonomy, the summary tables, and the research agenda.

Significance. If the taxonomy and coverage were reliable, the paper would be a useful entry point to a growing area and a plausible source of research directions. Its strengths are the breadth of the reference list, the organization of works by learning technique and control architecture, and the concrete open-issues discussion with pointers to potential remedies. The paper does not claim or provide machine-checked proofs, reproducible code, or new derivations, so the verification burden lies entirely on the accuracy and consistency of the survey claims. The stress-test concern about circularity does not land: as a survey, no fitting or derivation is performed, and applying a self-defined taxonomy to the literature is inherent to the genre. The taxonomy and the negative coverage claims in Section VI are, however, the load-bearing parts of the paper, and they contain inconsistencies that need repair.

major comments (4)

[Section I, Section III.C, Table 1] The proposed three-way taxonomy is not a clean partition as applied. Section III.C describes [23] as a 'joint learning algorithm' that learns Gaussian-process dynamics and computes optimal control and communication policies, and [12] as deep RL that 'simultaneously learn[s] control and communication behaviour'; both are listed in Table 1 under 'ML for dynamic model learning' rather than under the joint or optimization categories. The paper gives no primary-purpose rule that decides which category applies when a single work learns dynamics and optimizes control and communication. Since the classification is the paper's central contribution, the definitions need to be sharpened and the affected rows re-assigned.
[Table 2] Table 2 contains the row 'citelu2022event', a placeholder citation with no matching entry in the reference list. A final manuscript cannot present an unresolved citation in a summary table; at minimum, the actual reference must be inserted or the row removed. This placeholder is also direct evidence that the table construction was not finalized, which weakens confidence in the coverage claims made in Section I and Section VI.
[Section VI.A and Section IV.A] Section VI.A states that 'the existing works on ML-based event-driven control in our comprehensive review in sections III, IV, and V have not considered the impact of network-induced imperfections in their learning algorithms.' This is contradicted by Section IV.A, which reviews [44] as considering denial-of-service attacks in an event-triggered iterative single-critic learning framework for autonomous driving. If denial-of-service attacks are not counted as network-induced imperfections, that distinction should be stated explicitly; otherwise the claim should be weakened to 'most reviewed works' or the contradiction resolved.
[Section I and Section VI] The survey's sampling method is not described. The only statement of coverage is 'By analyzing ML-based ETC methods presented in [11], [12], [14]–[61]' in Section I, with no search databases, query terms, inclusion criteria, or search dates. Because the open-issue claims in Section VI are negative claims about the entire reviewed field, a reproducible selection protocol is needed to make them load-bearing. Without such a protocol, the claim that the survey is 'comprehensive' cannot be verified.

minor comments (6)

[Section II.A] The phrase 'in an architectures' is a typo and should read 'in an architecture'.
[Section III.B] The sentence beginning 'In [19], [24] an adaptive ETC problem is studied ... in [22]' is grammatically garbled; the electromagnetic suspension result should be attributed to [22] and the sentence rewritten.
[Section IV.A] In the sentence 'While [35], [36] present zero-sum games, [37], [38] applies an event-triggered IRL algorithm...', the verb should agree with the plural subject; the sentence should also clarify which of [37] and [38] is being discussed.
[Tables 1-3] The tables use check and square symbols in the columns for communication policy, control policy, multi-agent, and experimental validation, but no legend defines these symbols; a legend or spelled-out labels would make the tables interpretable.
[Section II.B.3] The claim that 'RL is not preferable for solving simple problems or for solving problems that need a lot of data' is vague; either state the criterion or remove the unsupported generalization.
[Section VI.B] The statement that 'All the ML-based ETC methods reviewed in this survey consider perfect quantization' is a universal negative claim; it should be verified row-by-row against the tables and qualified if any reviewed work involves digital communication channels.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the survey's taxonomy and open-issues discussion are self-contained; the single self-citation is background and not load-bearing.

full rationale

This is a review article, not a derivation. The paper's central claims are (i) a three-way classification of ML-based event-triggered control literature by the purpose of machine learning use and (ii) a list of open issues. These claims are supported by the authors' reading of references [11]-[61], summarized in Tables 1-3; no equation is derived, no parameter is fitted, and no uniqueness theorem is imported. The taxonomy is stipulated in Section I and then applied to the reviewed papers, which is the normal structure of a survey. The only publication by the present authors in the reference list is [62] (Ijaz et al.), cited in Section II.A for the definition and proactive nature of self-triggered control; that citation is background and not load-bearing for the classification or open-issues claims. The unfinished placeholder row 'citelu2022event' in Table 2 and the apparent tension between Section VI's assertion that no reviewed work considers network-induced imperfections and the review of [44] (denial-of-service attacks) are accuracy and completeness concerns, not circularity: neither claim reduces to its own input by construction. No self-definitional, fitted-input-called-prediction, uniqueness-imported-from-authors, or ansatz-smuggled-via-citation pattern is present.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper has no mathematical derivation or fitted parameters; its central claims are a taxonomy and a completeness judgment. The assumptions are about the representative set of references, the non-overlap of the categories, and the accuracy of the tables.

assumptions (3)

domain assumption The surveyed set of references [11]-[61] is representative and complete enough to support the taxonomy.
Section I claims a gap and builds the classification on [11]-[61], but no systematic search protocol or inclusion criteria are provided, so completeness is not demonstrated.
domain assumption ML-ETC methods can be partitioned into three non-overlapping purposes: model dynamics learning, optimization, and joint learning and optimization.
Sections III-V are structured around these three groups, but some cited works, such as [23], appear to combine model learning with optimization, so the exclusivity of the categories is fragile.
domain assumption The table entries accurately represent the cited papers.
The tables classify each reference by communication policy, control policy, and architecture, but the unresolved placeholder row 'citelu2022event' in Table 2 shows that table construction was not fully finished.

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Cite this review

Pith. "Pith review of Machine Learning in Event-Triggered Control: Recent Advances and Open Issues." pith.science (2026). https://pith.science/paper/QJE4VQVH

@misc{pith2026200912783,
  author       = {Pith},
  title        = {Pith review of: Machine Learning in Event-Triggered Control: Recent Advances and Open Issues},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QJE4VQVH}},
  note         = {Machine review of arXiv:2009.12783}
}

read the original abstract

Networked control systems have gained considerable attention over the last decade as a result of the trend towards decentralised control applications and the emergence of cyber-physical system applications. However, real-world wireless networked control systems suffer from limited communication bandwidths, reliability issues, and a lack of awareness of network dynamics due to the complex nature of wireless networks. Combining machine learning and event-triggered control has the potential to alleviate some of these issues. For example, machine learning can be used to overcome the problem of a lack of network models by learning system behavior or adapting to dynamically changing models by continuously learning model dynamics. Event-triggered control can help to conserve communication bandwidth by transmitting control information only when necessary or when resources are available. The purpose of this article is to conduct a review of the literature on the use of machine learning in combination with event-triggered control. Machine learning techniques such as statistical learning, neural networks, and reinforcement learning-based approaches such as deep reinforcement learning are being investigated in combination with event-triggered control. We discuss how these learning algorithms can be used for different applications depending on the purpose of the machine learning use. Following the review and discussion of the literature, we highlight open research questions and challenges associated with machine learning-based event-triggered control and suggest potential solutions.

Figures

Figures reproduced from arXiv: 2009.12783 by the authors.

**Figure 2.** FIGURE 2: Event triggered learning diagram [15] [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** FIGURE 3: NN-based ETC [17] [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: FIGURE 4: Event-triggered Adaptive Critic Architecture. [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]

**Figure 5.** Figure 5: FIGURE 5: Joint Learning of System and Network Model [PITH_FULL_IMAGE:figures/full_fig_p016_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

135 extracted references · 80 canonical work pages

[23]

Learning self-triggered controllers with Gaussian processes

Kazumune Hashimoto, Yuichi Yoshimura, and Toshimitsu Ushio. Learn- ing self-triggered controllers with gaussian processes. arXiv preprint arXiv:1909.00178, 2019

work page Pith review arXiv 1909
[12]

Deep reinforcement learning for event-triggered control

Dominik Baumann, Jia-Jie Zhu, Georg Martius, and Sebastian Trimpe. Deep reinforcement learning for event-triggered control. In 2018 IEEE Conference on Decision and Control (CDC), pages 943–950. IEEE, 2018

2018
[44]

Adaptive resilient event-triggered control design of autonomous vehicles with an iterative single critic learning framework

Kun Zhang, Rong Su, Huaguang Zhang, and Yunlin Tian. Adaptive resilient event-triggered control design of autonomous vehicles with an iterative single critic learning framework. IEEE transactions on neural networks and learning systems, 32(12):5502–5511, 2021

2021
[11]

Event-triggered learning for resource-efﬁcient networked con- trol

Friedrich Solowjow, Dominik Baumann, Jochen Garcke, and Sebastian Trimpe. Event-triggered learning for resource-efﬁcient networked con- trol. In 2018 Annual American Control Conference (ACC), pages 6506–

2018
[14]

Event-triggered learning

Friedrich Solowjow and Sebastian Trimpe. Event-triggered learning. Automatica, 117:109009, 2020

2020
[61]

Event-triggered distributed h ∞ constrained control of physically interconnected large-scale partially unknown strict-feedback systems

Luy Nguyen Tan. Event-triggered distributed h ∞ constrained control of physically interconnected large-scale partially unknown strict-feedback systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(4):2444–2456, 2019

work page 2019
[1]

C. Sun, G. Cembrano, V . Puig, and J. Meseguer. Cyber-physical systems for real-time management in the urban water cycle. In 2018 Interna- tional Workshop on Cyber-physical Systems for Smart Water Networks (CySWater), pages 5–8, 2018

2018
[2]

Cyber-physical systems research and education in 2030: Scenarios and strategies

Didem Gürdür Broo, Ulf Boman, and Martin Törngren. Cyber-physical systems research and education in 2030: Scenarios and strategies. Journal of Industrial Information Integration, 21:100192, 2021

2021

Show all 135 references

[3]

Iot, iiot, and cyber-physical systems integration

Halim Khujamatov, Ernazar Reypnazarov, Doston Khasanov, and Nur- shod Akhmedov. Iot, iiot, and cyber-physical systems integration. In Emergence of Cyber Physical System and IoT in Smart Automation and Robotics, pages 31–50. Springer, 2021

2021
[4]

Cyber- physical systems architectures for industrial internet of things applica- tions in industry 4.0: A literature review

Diego GS Pivoto, Luiz FF de Almeida, Rodrigo da Rosa Righi, Joel JPC Rodrigues, Alexandre Baratella Lugli, and Antonio M Alberti. Cyber- physical systems architectures for industrial internet of things applica- tions in industry 4.0: A literature review. Journal of manufacturi...

2021
[5]

Networked control system: Overview and research trends

Rachana Ashok Gupta and Mo-Yuen Chow. Networked control system: Overview and research trends. IEEE transactions on industrial electron- ics, 57(7):2527–2535, 2009

2009
[6]

On event-triggered and self-triggered control over sensor/actuator networks

Manuel Mazo and Paulo Tabuada. On event-triggered and self-triggered control over sensor/actuator networks. In 2008 47th IEEE Conference on Decision and Control, pages 435–440. IEEE, 2008

2008
[7]

Distributed control for large-scale systems with adaptive event- triggering

María Guinaldo, José Sánchez, Raquel Dormido, and Sebastián Dormido. Distributed control for large-scale systems with adaptive event- triggering. Journal of the Franklin Institute, 353(3):735–756, 2016

2016
[8]

A simple self-triggered sampler for perturbed nonlinear systems

U Tiberi and Karl Henrik Johansson. A simple self-triggered sampler for perturbed nonlinear systems. Nonlinear Analysis: Hybrid Systems, 10:126–140, 2013

2013
[9]

A survey on recent advances in event- triggered communication and control

Chen Peng and Fuqiang Li. A survey on recent advances in event- triggered communication and control. Information Sciences, 457:113– 125, 2018

2018
[10]

Co-design of distributed model-based control and event-triggering scheme for load frequency regulation in smart grids

Shichao Liu, Wensheng Luo, and Ligang Wu. Co-design of distributed model-based control and event-triggering scheme for load frequency regulation in smart grids. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(9):3311–3319, 2018

2018
[13]

An actor–critic–identiﬁer architecture for adaptive ap- proximate optimal control

S Bhasin, R Kamalapurkar, M Johnson, KG Vamvoudakis, FL Lewis, and WE Dixon. An actor–critic–identiﬁer architecture for adaptive ap- proximate optimal control. In Reinforcement Learning and Approximate Dynamic Programming for Feedback Control, pages 258–280. Wiley, 2012

2012
[15]

Event-triggered pulse control with model learning (if necessary)

Dominik Baumann, Friedrich Solowjow, Karl Henrik Johansson, and Sebastian Trimpe. Event-triggered pulse control with model learning (if necessary). In 2019 American Control Conference (ACC), pages 792–

2019
[16]

Event-triggered model predictive control with a statistical learning

Jaehyun Yoo and Karl H Johansson. Event-triggered model predictive control with a statistical learning. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019

2019
[17]

Neu- ral network-based event-triggered state feedback control of nonlinear continuous-time systems

Avimanyu Sahoo, Hao Xu, and Sarangapani Jagannathan. Neu- ral network-based event-triggered state feedback control of nonlinear continuous-time systems. IEEE Transactions on Neural Networks and Learning Systems, 27(3):497–509, 2015

2015
[18]

Event- triggered learning for linear quadratic control

Henning Schluter, Friedrich Solowjow, and Sebastian Trimpe. Event- triggered learning for linear quadratic control. arXiv preprint arXiv:1910.07732, 2019

1910 arXiv
[19]

Adaptive neural control of pure- feedback nonlinear systems with event-triggered communications

Yuan-Xin Li and Guang-Hong Yang. Adaptive neural control of pure- feedback nonlinear systems with event-triggered communications. IEEE transactions on neural networks and learning systems, 29(12):6242– 6251, 2018

2018
[20]

Event- triggered adaptive nn tracking control with dynamic gain for a class of unknown nonlinear systems

Jing Li, Han Liu, Zhaohui Zhang, Xiaobo Li, and Xiaoli Yang. Event- triggered adaptive nn tracking control with dynamic gain for a class of unknown nonlinear systems. Neurocomputing, 467:292–299, 2022

2022
[21]

Model-based adaptive event-triggered control of nonlinear continuous- time systems

Zhongyu Chen, Ben Niu, Xudong Zhao, Liang Zhang, and Ning Xu. Model-based adaptive event-triggered control of nonlinear continuous- time systems. Applied Mathematics and Computation, 408:126330, 2021

2021
[22]

Neural net- work based adaptive event trigger control for a class of electromagnetic suspension systems

Lei Liu, Xiangsheng Li, Yan-Jun Liu, and Shaocheng Tong. Neural net- work based adaptive event trigger control for a class of electromagnetic suspension systems. Control Engineering Practice, 106:104675, 2021

2021
[24]

Event-triggering based adaptive neural tracking control for a VOLUME 4, 2016 17 Leila S

Chuang Gao, Chunlei Zhang, Xiaoping Liu, Huanqing Wang, and Lidong Wang. Event-triggering based adaptive neural tracking control for a VOLUME 4, 2016 17 Leila S. et al.: Machine Learning in Event-T riggered Control class of pure-feedback systems with ﬁnite-time prescribed perf...

2016
[25]

Neural-network-based event-triggered adaptive control of nonafﬁne non- linear multiagent systems with dynamic uncertainties

Hongjing Liang, Guangliang Liu, Huaguang Zhang, and Tingwen Huang. Neural-network-based event-triggered adaptive control of nonafﬁne non- linear multiagent systems with dynamic uncertainties. IEEE Transactions on Neural Networks and Learning Systems, 32(5):2239–2250, 2020

2020
[26]

Event-triggered adaptive nn control for mimo switched nonlinear systems with non-isps unmodeled dynamics

Fenglan Wang and Lijun Long. Event-triggered adaptive nn control for mimo switched nonlinear systems with non-isps unmodeled dynamics. Journal of the Franklin Institute, 2022

2022
[27]

Event-triggered ﬁnite-time tracking control of underactuated msvs based on neural net- work disturbance observer

Shulan Yu, Jinshu Lu, Guibing Zhu, and Shujie Yang. Event-triggered ﬁnite-time tracking control of underactuated msvs based on neural net- work disturbance observer. Ocean Engineering, 253:111169, 2022

2022
[28]

Event-triggered reinforce- ment learning-based adaptive tracking control for completely unknown continuous-time nonlinear systems

Xinxin Guo, Weisheng Yan, and Rongxin Cui. Event-triggered reinforce- ment learning-based adaptive tracking control for completely unknown continuous-time nonlinear systems. IEEE Transactions on Cybernetics, 2019

2019
[29]

Distributed deep learning with event- triggered communication

Jemin George and Prudhvi Gurram. Distributed deep learning with event- triggered communication. arXiv preprint arXiv:1909.05020, 2019

1909 arXiv
[30]

Event-triggered H∞ control for continuous-time nonlinear system via concurrent learn- ing

Qichao Zhang, Dongbin Zhao, and Yuanheng Zhu. Event-triggered H∞ control for continuous-time nonlinear system via concurrent learn- ing. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(7):1071–1081, 2016

2016
[31]

Event-triggered integral reinforcement learning for nonlinear continuous-time systems

Qichao Zhang and Dongbin Zhao. Event-triggered integral reinforcement learning for nonlinear continuous-time systems. In 2017 IEEE Sym- posium Series on Computational Intelligence (SSCI), pages 1–6. IEEE, 2017

2017
[32]

Event- driven nonlinear discounted optimal regulation involving a power system application

Ding Wang, Haibo He, Xiangnan Zhong, and Derong Liu. Event- driven nonlinear discounted optimal regulation involving a power system application. IEEE Transactions on Industrial Electronics, 64(10):8177– 8186, 2017

2017
[33]

Event- triggered H∞ tracking control of nonlinear systems via reinforcement learning method

Lili Cui, Wei Qu, Li Wang, Yanhong Luo, and Zhanshan Wang. Event- triggered H∞ tracking control of nonlinear systems via reinforcement learning method. In 2019 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE, 2019

2019
[34]

Event-triggered robust stabilization of nonlinear input-constrained systems using single network adaptive critic designs

Xiong Yang and Haibo He. Event-triggered robust stabilization of nonlinear input-constrained systems using single network adaptive critic designs. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018

2018
[35]

Event-driven H∞-constrained control using adaptive critic learning

Xiong Yang and Haibo He. Event-driven H∞-constrained control using adaptive critic learning. IEEE Transactions on Cybernetics, 2020

2020
[36]

Event-triggered safe control for the zero-sum game of nonlinear safety-critical systems with input saturation

Chunbin Qin, Heyang Zhu, Jinguang Wang, Qiyang Xiao, and Dehua Zhang. Event-triggered safe control for the zero-sum game of nonlinear safety-critical systems with input saturation. IEEE Access, 10:40324– 40337, 2022

2022
[37]

Event-triggered integral reinforcement learning for nonzero-sum games with asymmetric input saturation

Shan Xue, Biao Luo, Derong Liu, and Ying Gao. Event-triggered integral reinforcement learning for nonzero-sum games with asymmetric input saturation. Neural Networks, 2022

2022
[38]

Integral reinforcement learning-based online adaptive event-triggered control for non-zero-sum games of partially unknown nonlinear systems

Hanguang Su, Huaguang Zhang, Shaoxin Sun, and Yuliang Cai. Integral reinforcement learning-based online adaptive event-triggered control for non-zero-sum games of partially unknown nonlinear systems. Neuro- computing, 377:243–255, 2020

2020
[39]

Improving the critic learning for event-based nonlinear H∞ control design

Ding Wang, Haibo He, and Derong Liu. Improving the critic learning for event-based nonlinear H∞ control design. IEEE transactions on cybernetics, 47(10):3417–3428, 2017

2017
[40]

Goal representation adaptive critic design for discrete-time uncertain systems subjected to input constraints: The event-triggered case

Shangwei Zhao, Jingcheng Wang, Hongyuan Wang, and Haotian Xu. Goal representation adaptive critic design for discrete-time uncertain systems subjected to input constraints: The event-triggered case. Neu- rocomputing, 2022

2022
[41]

Learning and guaranteed cost control with event-based adaptive critic implementation

Ding Wang and Derong Liu. Learning and guaranteed cost control with event-based adaptive critic implementation. IEEE transactions on neural networks and learning systems, 29(12):6004–6014, 2018

2018
[42]

Adaptive critic learning and experience replay for decentralized event-triggered control of nonlinear interconnected systems

Xiong Yang and Haibo He. Adaptive critic learning and experience replay for decentralized event-triggered control of nonlinear interconnected systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019

2019
[43]

Adaptive critic optimization to decentralized event-triggered control of continuous-time nonlinear interconnected systems

Yu Huo, Ding Wang, and Junfei Qiao. Adaptive critic optimization to decentralized event-triggered control of continuous-time nonlinear interconnected systems. Optimal Control Applications and Methods, 43(1):198–212, 2022

2022
[45]

Event- trigger-based robust control for nonlinear constrained-input systems us- ing reinforcement learning method

Dongsheng Yang, Ting Li, Huaguang Zhang, and Xiangpeng Xie. Event- trigger-based robust control for nonlinear constrained-input systems us- ing reinforcement learning method. Neurocomputing, 340:158–170, 2019

2019
[46]

Model-free event- triggered control algorithm for continuous-time linear systems with opti- mal performance

Kyriakos G Vamvoudakis and Henrique Ferraz. Model-free event- triggered control algorithm for continuous-time linear systems with opti- mal performance. Automatica, 87:412–420, 2018

2018
[47]

Adaptive critic designs for event-triggered robust control of nonlinear systems with unknown dynamics

Xiong Yang and Haibo He. Adaptive critic designs for event-triggered robust control of nonlinear systems with unknown dynamics. IEEE transactions on cybernetics, 49(6):2255–2267, 2018

2018
[48]

Event- triggered multigradient recursive reinforcement learning tracking control for multiagent systems

Weiwei Bai, Tieshan Li, Yue Long, and CL Philip Chen. Event- triggered multigradient recursive reinforcement learning tracking control for multiagent systems. IEEE Transactions on Neural Networks and Learning Systems, 2021

2021
[49]

Self-learning optimal regulation for discrete-time nonlinear systems under event-driven formu- lation

Ding Wang, Mingming Ha, and Junfei Qiao. Self-learning optimal regulation for discrete-time nonlinear systems under event-driven formu- lation. IEEE Transactions on Automatic Control, 2019

2019
[50]

Event-triggered constrained con- trol using explainable global dual heuristic programming for nonlinear discrete-time systems

Bo Sun and Erik-Jan van Kampen. Event-triggered constrained con- trol using explainable global dual heuristic programming for nonlinear discrete-time systems. Neurocomputing, 468:452–463, 2022

2022
[51]

Event-triggered near-optimal control for unknown discrete-time nonlinear systems using parallel control

Jingwei Lu, Qinglai Wei, Tianmin Zhou, Ziyang Wang, and Fei-Yue Wang. Event-triggered near-optimal control for unknown discrete-time nonlinear systems using parallel control. IEEE Transactions on Cyber- netics, 2022

2022
[52]

Event-triggered optimal neuro- controller design with reinforcement learning for unknown nonlinear systems

Xiong Yang, Haibo He, and Derong Liu. Event-triggered optimal neuro- controller design with reinforcement learning for unknown nonlinear systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017

2017
[53]

Optimizing data centre energy efﬁciency via event-driven deep reinforcement learning

Yongyi Ran, Xin Zhou, Han Hu, and Yonggang Wen. Optimizing data centre energy efﬁciency via event-driven deep reinforcement learning. IEEE Transactions on Services Computing, 2022

2022
[54]

On mixed data and event driven design for adaptive-critic-based nonlinear H∞ control

Ding Wang, Chaoxu Mu, Derong Liu, and Hongwen Ma. On mixed data and event driven design for adaptive-critic-based nonlinear H∞ control. IEEE transactions on neural networks and learning systems, 29(4):993– 1005, 2017

2017
[55]

Event-triggered control for input constrained non-afﬁne nonlinear systems based on neuro- dynamic programming

Shunchao Zhang, Bo Zhao, and Yongwei Zhang. Event-triggered control for input constrained non-afﬁne nonlinear systems based on neuro- dynamic programming. Neurocomputing, 440:175–184, 2021

2021
[56]

Adaptive-critic design for decentralized event-triggered con- trol of constrained nonlinear interconnected systems within an identiﬁer- critic framework

Xin Huo, Hamid Reza Karimi, Xudong Zhao, Bohui Wang, and Guang- deng Zong. Adaptive-critic design for decentralized event-triggered con- trol of constrained nonlinear interconnected systems within an identiﬁer- critic framework. IEEE Transactions on Cybernetics, 2021

2021
[57]

Single- network adp for solving optimal event-triggered tracking control problem of completely unknown nonlinear systems

Ning Xu, Ben Niu, Huanqing Wang, Xin Huo, and Xudong Zhao. Single- network adp for solving optimal event-triggered tracking control problem of completely unknown nonlinear systems. International Journal of Intelligent Systems, 36(9):4795–4815, 2021

2021
[58]

Data-driven-based event-triggered optimal control of unknown nonlinear systems with input constraints

Shanlin Liu, Ben Niu, Guangdeng Zong, Xudong Zhao, and Ning Xu. Data-driven-based event-triggered optimal control of unknown nonlinear systems with input constraints. Nonlinear Dynamics, pages 1–19, 2022

2022
[59]

Event-triggered adp for tracking control of partially unknown constrained uncertain systems

Shan Xue, Biao Luo, Derong Liu, and Ying Gao. Event-triggered adp for tracking control of partially unknown constrained uncertain systems. IEEE Transactions on Cybernetics, 2021

2021
[60]

Event- triggered control of nonlinear discrete-time system with unknown dy- namics based on hdp (λ)

Ting Li, Dongsheng Yang, Xiangpeng Xie, and Huaguang Zhang. Event- triggered control of nonlinear discrete-time system with unknown dy- namics based on hdp (λ). IEEE Transactions on Cybernetics, 2021

2021
[62]

Self-triggered control plane for cognitive radio networks

Zohaib Ijaz, Muhammad Tahir, and Sahar Arshad. Self-triggered control plane for cognitive radio networks. In 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall), pages 1–5. IEEE, 2018

2018
[63]

Neural adaptive self-triggered control for uncertain nonlinear systems with input hysteresis

Jianhui Wang, Hongkang Zhang, Kemao Ma, Zhi Liu, and CL Philip Chen. Neural adaptive self-triggered control for uncertain nonlinear systems with input hysteresis. IEEE Transactions on Neural Networks and Learning Systems, 2021

2021
[64]

W. P. M. H. Heemels, K. H. Johansson, and P. Tabuada. An introduction to event-triggered and self-triggered control. In 2012 IEEE 51st IEEE Conference on Decision and Control (CDC), pages 3270–3285, 2012

2012
[65]

Event-based dissipative control of interval type-2 fuzzy markov jump systems under sensor saturation and actuator nonlinearity

Guangtao Ran, Chuanjiang Li, Hak-Keung Lam, Dongyu Li, and Chun- song Han. Event-based dissipative control of interval type-2 fuzzy markov jump systems under sensor saturation and actuator nonlinearity. IEEE Transactions on Fuzzy Systems, 2020. 18 VOLUME 4, 2016 Leila S. et al...

2020
[66]

Cooperative output regulation with mixed time-and event-triggered observers

Shimin Wang, Zhan Shu, and Tongwen Chen. Cooperative output regulation with mixed time-and event-triggered observers. arXiv preprint arXiv:2105.02200, 2021

2021 arXiv
[67]

Probabilistic- constrained ﬁltering for a class of nonlinear systems with improved static event-triggered communication

Engang Tian, Zidong Wang, Lei Zou, and Dong Yue. Probabilistic- constrained ﬁltering for a class of nonlinear systems with improved static event-triggered communication. International Journal of Robust and Nonlinear Control, 29(5):1484–1498, 2019

2019
[68]

Dynamic event-triggered distributed coordination control and its applications: A survey of trends and techniques

Xiaohua Ge, Qing-Long Han, Lei Ding, Yu-Long Wang, and Xian-Ming Zhang. Dynamic event-triggered distributed coordination control and its applications: A survey of trends and techniques. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(9):3112–3125, 2020

2020
[69]

Adaptive event-triggered control of a class of nonlinear networked systems

Zhou Gu, Engang Tian, and Jinliang Liu. Adaptive event-triggered control of a class of nonlinear networked systems. Journal of the Franklin Institute, 354(9):3854–3871, 2017

2017
[70]

A hybrid event- triggered approach to consensus of multiagent systems with disturbances

Guanglei Zhao, Changchun Hua, and Xinping Guan. A hybrid event- triggered approach to consensus of multiagent systems with disturbances. IEEE Transactions on Control of Network Systems, 7(3):1259–1271, 2020

2020
[71]

Dy- namic event-triggered control and estimation: A survey

Xiaohua Ge, Qing-Long Han, Xian-Ming Zhang, and Derui Ding. Dy- namic event-triggered control and estimation: A survey. International Journal of Automation and Computing, 18(6):857–886, 2021

2021
[72]

How often should one update control and estimation: review of networked triggering techniques

Zhiyong Chen, Qing-Long Han, Yamin Yan, and Zheng-Guang Wu. How often should one update control and estimation: review of networked triggering techniques. Science China Information Sciences, 63(5):1–18, 2020

2020
[73]

Adaptive event-triggered asynchronous con- trol for interval type-2 fuzzy markov jump systems with cyber-attacks

Guangtao Ran, Chuanjiang Li, Sakthivel Rathinasamy, Chunsong Han, Bohui Wang, and Jian Liu. Adaptive event-triggered asynchronous con- trol for interval type-2 fuzzy markov jump systems with cyber-attacks. IEEE Transactions on Control of Network Systems, 2022

2022
[74]

Machine learning, 1997

Thomas M Mitchell et al. Machine learning, 1997

1997
[75]

Dorato, and D

V Koltchinski, Abdallah C.T., M Ariolay, P. Dorato, and D. Panchenko. Statistical learning control of uncertain systems: It is better than it seems. Technical report, University of New Mexico, 2000

2000
[76]

Active vehicle suspension control using road preview model predictive control and radial basis function networks

Myron Papadimitrakis and Alex Alexandridis. Active vehicle suspension control using road preview model predictive control and radial basis function networks. Applied Soft Computing, 120:108646, 2022

2022
[77]

Predicting construction labor productivity using lower upper decomposition radial base function neural network

Sasan Golnaraghi, Osama Moselhi, Sabah Alkass, and Zahra Zan- genehmadar. Predicting construction labor productivity using lower upper decomposition radial base function neural network. Engineering Reports, 2(2):e12107, 2020

2020
[78]

Reinforcement learning in robotic applications: a comprehensive survey

Bharat Singh, Rajesh Kumar, and Vinay Pratap Singh. Reinforcement learning in robotic applications: a comprehensive survey. Artiﬁcial Intelligence Review, pages 1–46, 2021

2021
[79]

Natural actor-critic

Jan Peters, Sethu Vijayakumar, and Stefan Schaal. Natural actor-critic. In European Conference on Machine Learning, pages 280–291. Springer, 2005

2005
[80]

A review on deep learning in machining and tool monitoring: methods, opportunities, and challenges

Vahid Nasir and Farrokh Sassani. A review on deep learning in machining and tool monitoring: methods, opportunities, and challenges. The Inter- national Journal of Advanced Manufacturing Technology, 115(9):2683– 2709, 2021

2021
[81]

Ma- chine learning and deep learning frameworks and libraries for large- scale data mining: a survey

Giang Nguyen, Stefan Dlugolinsky, Martin Bobák, Viet Tran, Alvaro Lopez Garcia, Ignacio Heredia, Peter Malík, and Ladislav Hluch `y. Ma- chine learning and deep learning frameworks and libraries for large- scale data mining: a survey. Artiﬁcial Intelligence Review, 52(1):77–124, 2019

2019
[82]

A state-of-the-art review of deep reinforcement learning techniques for real- time strategy games

Nesma M Ashraf, Reham R Mostafa, Rasha H Sakr, and MZ Rashad. A state-of-the-art review of deep reinforcement learning techniques for real- time strategy games. Applications of Artiﬁcial Intelligence in Business, Education and Healthcare, pages 285–307, 2021

2021
[83]

Review on model predictive control: an engineering perspective

Max Schwenzer, Muzaffer Ay, Thomas Bergs, and Dirk Abel. Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology, 117(5):1327–1349, 2021

2021
[84]

Temporally-extended {\epsilon}-greedy exploration

Will Dabney, Georg Ostrovski, and André Barreto. Temporally-extended {\epsilon}-greedy exploration. arXiv preprint arXiv:2006.01782, 2020

2006 arXiv
[85]

Distributed fault-tolerant containment control protocols for the discrete- time multiagent systems via reinforcement learning method

Tieshan Li, Weiwei Bai, Qi Liu, Yue Long, and CL Philip Chen. Distributed fault-tolerant containment control protocols for the discrete- time multiagent systems via reinforcement learning method. IEEE Transactions on Neural Networks and Learning Systems, 2021

2021
[86]

Learning-based robust tracking control of quadrotor with time-varying and coupling uncertainties

Chaoxu Mu and Yong Zhang. Learning-based robust tracking control of quadrotor with time-varying and coupling uncertainties. IEEE transac- tions on neural networks and learning systems, 31(1):259–273, 2019

2019
[87]

A novel z-function-based completely model-free reinforcement learning method to ﬁnite-horizon zero-sum game of nonlinear system

Zhe Chen, Wenqian Xue, Ning Li, Bosen Lian, and Frank L Lewis. A novel z-function-based completely model-free reinforcement learning method to ﬁnite-horizon zero-sum game of nonlinear system. Nonlinear Dynamics, pages 1–20, 2022

2022
[88]

Event- based integral reinforcement learning algorithm for non-zero-sum games of partially unknown nonlinear systems

Hanguang Su, Huaguang Zhang, Yanhong Luo, and Qiuye Sun. Event- based integral reinforcement learning algorithm for non-zero-sum games of partially unknown nonlinear systems. In 2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS), pages 287–292. IEEE, 2021

2021
[89]

Optimized backstepping tracking control using reinforcement learning for a class of stochastic nonlinear strict-feedback systems

Guoxing Wen, Liguang Xu, and Bin Li. Optimized backstepping tracking control using reinforcement learning for a class of stochastic nonlinear strict-feedback systems. IEEE Transactions on Neural Networks and Learning Systems, 2021

2021
[90]

Observer- based event-triggered control for zero-sum games of input constrained multi-player nonlinear systems

Shunchao Zhang, Bo Zhao, Derong Liu, and Yongwei Zhang. Observer- based event-triggered control for zero-sum games of input constrained multi-player nonlinear systems. Neural Networks, 144:101–112, 2021

2021
[91]

Integral concurrent learning: Adaptive control with parameter convergence using ﬁnite excitation

Anup Parikh, Rushikesh Kamalapurkar, and Warren E Dixon. Integral concurrent learning: Adaptive control with parameter convergence using ﬁnite excitation. International Journal of Adaptive Control and Signal Processing, 33(12):1775–1787, 2019

2019
[92]

Online concurrent reinforcement learning algorithm to solve two-player zero-sum games for partially unknown nonlinear continuous-time systems

Sholeh Yasini, Ali Karimpour, Mohammad-Bagher Naghibi Sistani, and Hamidreza Modares. Online concurrent reinforcement learning algorithm to solve two-player zero-sum games for partially unknown nonlinear continuous-time systems. International Journal of Adaptive Control and Si...

2015
[93]

Online solution of nonlinear two-player zero-sum games using synchronous policy iteration

Kyriakos G Vamvoudakis and Frank L Lewis. Online solution of nonlinear two-player zero-sum games using synchronous policy iteration. International Journal of Robust and Nonlinear Control, 22(13):1460– 1483, 2012

2012
[94]

Online integral reinforcement learning control for an uncertain highly ﬂexible aircraft using state and output feedback

Chi Peng and Jianjun Ma. Online integral reinforcement learning control for an uncertain highly ﬂexible aircraft using state and output feedback. Aerospace Science and Technology, 109:106442, 2021

2021
[95]

Finite-horizon discounted optimal control: stability and performance

Mathieu Granzotto, Romain Postoyan, Lucian Bu¸ soniu, Dragan Neši ´c, and Jamal Daafouz. Finite-horizon discounted optimal control: stability and performance. IEEE Transactions on Automatic Control, 66(2):550– 565, 2020

2020
[96]

H∞ optimal control and related minimax design problems: a dynamic game approach

Tamer Ba¸ sar and Pierre Bernhard. H∞ optimal control and related minimax design problems: a dynamic game approach. Springer Science & Business Media, 2008

2008
[97]

Finite-time convergence adaptive neural network control for nonlinear servo systems

Jing Na, Shubo Wang, Yan-Jun Liu, Yingbo Huang, and Xuemei Ren. Finite-time convergence adaptive neural network control for nonlinear servo systems. IEEE Transactions on Cybernetics, 50(6):2568–2579, 2019

2019
[98]

Deepee: Joint optimization of job scheduling and cooling control for data center en- ergy efﬁciency using deep reinforcement learning

Yongyi Ran, Han Hu, Xin Zhou, and Yonggang Wen. Deepee: Joint optimization of job scheduling and cooling control for data center en- ergy efﬁciency using deep reinforcement learning. In 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), pages 645...

2019
[99]

Event-based optimization within the lagrangian relaxation framework for energy savings in hvac systems

Biao Sun, Peter B Luh, Qing-Shan Jia, and Bing Yan. Event-based optimization within the lagrangian relaxation framework for energy savings in hvac systems. IEEE Transactions on Automation Science and Engineering, 12(4):1396–1406, 2015

2015
[100]

Wireless Communications: Principles and Practice

Theodore Rappaport. Wireless Communications: Principles and Practice. Prentice Hall PTR, USA, 2nd edition, 2001

2001
[101]

Deeprs: Deep-learning based network-adaptive fec for real-time video communi- cations

Sheng Cheng, Han Hu, Xinggong Zhang, and Zongming Guo. Deeprs: Deep-learning based network-adaptive fec for real-time video communi- cations. arXiv preprint arXiv:2001.07852, 2020

2001 arXiv
[102]

Event-based control with commu- nication delays and packet losses

Daniel Lehmann and Jan Lunze. Event-based control with commu- nication delays and packet losses. International Journal of Control, 85(5):563–577, 2012

2012
[103]

Robust H∞ output feedback control of networked control systems with discrete distributed delays subject to packet dropout and quantization

Binqiang Xue, Haisheng Yu, and Mengling Wang. Robust H∞ output feedback control of networked control systems with discrete distributed delays subject to packet dropout and quantization. IEEE Access, 7:30313–30320, 2019

2019
[104]

Robust mixed H2/H∞ control for an uncertain wireless sensor network systems with time delay and packet loss

Yuanbo Shi, Jianhui Wang, Xiaoke Fang, Yueyang Huang, and Shusheng Gu. Robust mixed H2/H∞ control for an uncertain wireless sensor network systems with time delay and packet loss. International Journal of Control, Automation and Systems, pages 1–13, 2020

2020
[105]

Delay-aware multi-layer multi-rate model predictive control for vehicle platooning under message-rate congestion control

Amr Ibrahim, Dip Goswami, Hong Li, and Twan Basten. Delay-aware multi-layer multi-rate model predictive control for vehicle platooning under message-rate congestion control. IEEE Access, 2022

2022
[106]

Wireless network design for control systems: A survey

Pangun Park, Sinem Coleri Ergen, Carlo Fischione, Chenyang Lu, and Karl Henrik Johansson. Wireless network design for control systems: A survey. IEEE Communications Surveys & Tutorials, 20(2):978–1013, 2017. VOLUME 4, 2016 19 Leila S. et al.: Machine Learning in Event-T rigger...

2017
[107]

Understanding the modeling of computer network delays using neural networks

Albert Mestres, Eduard Alarcón, Yusheng Ji, and Albert Cabellos- Aparicio. Understanding the modeling of computer network delays using neural networks. In Proceedings of the 2018 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, pages 46–52, 2018

2018
[108]

Networked operation of a uav using gaussian process- based delay compensation and model predictive control

Dohyun Jang, Jaehyun Yoo, Clark Youngdong Son, H Jin Kim, and Karl H Johansson. Networked operation of a uav using gaussian process- based delay compensation and model predictive control. In 2019 Inter- national Conference on Robotics and Automation (ICRA), pages 9216–

2019
[109]

Learning communication delay patterns for remotely controlled uav networks

Jaehyun Yoo and Karl H Johansson. Learning communication delay patterns for remotely controlled uav networks. IFAC-PapersOnLine, 50(1):13216–13221, 2017

2017
[110]

Network-based h∞ ﬁltering for descriptor markovian jump systems with a novel neural network event-triggered scheme

Yuzhong Wang, Tie Zhang, Si Chen, and Junchao Ren. Network-based h∞ ﬁltering for descriptor markovian jump systems with a novel neural network event-triggered scheme. Neural Processing Letters, pages 1–19, 2021

2021
[111]

Fuzzy quantized sampled-data control for extended dissipative analysis of t–s fuzzy system and its application to wpgss

Xiao Cai, Jun Wang, Shouming Zhong, Kaibo Shi, and Yiqian Tang. Fuzzy quantized sampled-data control for extended dissipative analysis of t–s fuzzy system and its application to wpgss. Journal of the Franklin Institute, 358(2):1350–1375, 2021

2021
[112]

Dis- sipative analysis for high speed train systems via looped-functional and relaxed condition methods

Xiao Cai, Kaibo Shi, Shouming Zhong, Jun Wang, and Yiqian Tang. Dis- sipative analysis for high speed train systems via looped-functional and relaxed condition methods. Applied Mathematical Modelling, 96:570– 583, 2021

2021
[113]

Memory-event-triggered h∞ output control of neural networks with mixed delays

Shen Yan, Zhou Gu, and Sing Kiong Nguang. Memory-event-triggered h∞ output control of neural networks with mixed delays. IEEE Transac- tions on Neural Networks and Learning Systems, 2021

2021
[114]

Model-based event-triggered control for systems with quantization and time-varying network delays

Eloy Garcia and Panos J Antsaklis. Model-based event-triggered control for systems with quantization and time-varying network delays. IEEE Transactions on Automatic Control, 58(2):422–434, 2012

2012
[115]

Quantization level based event-triggered control with measurement uncertainties

Tianwei Zhou, Guanghui Yue, and Ben Niu. Quantization level based event-triggered control with measurement uncertainties. Information Sciences, 588:442–456, 2022

2022
[116]

Dynamic output feedback control for networked systems subject to communication delays, packet dropouts, and quantization

Elahe Mastani and Mehdi Rahmani. Dynamic output feedback control for networked systems subject to communication delays, packet dropouts, and quantization. Journal of the Franklin Institute, 358(8):4303–4325, 2021

2021
[117]

Tracking trip changes with event triggering

Panayiotis Kolios, Georgios Ellinas, and Christos Panayiotou. Tracking trip changes with event triggering. In 2015 IEEE 18th International Conference on Intelligent Transportation Systems, pages 529–534. IEEE, 2015

2015
[118]

Learning and management for internet of things: Accounting for adaptivity and scalability

Tianyi Chen, Sergio Barbarossa, Xin Wang, Georgios B Giannakis, and Zhi-Li Zhang. Learning and management for internet of things: Accounting for adaptivity and scalability. Proceedings of the IEEE, 107(4):778–796, 2019

2019
[119]

Recent scalability improvements for semideﬁnite programming with applications in machine learning, control, and robotics

Anirudha Majumdar, Georgina Hall, and Amir Ali Ahmadi. Recent scalability improvements for semideﬁnite programming with applications in machine learning, control, and robotics. Annual Review of Control, Robotics, and Autonomous Systems, 3:331–360, 2020

2020
[120]

Fully distributed event-triggered protocols for linear multiagent networks

Bin Cheng and Zhongkui Li. Fully distributed event-triggered protocols for linear multiagent networks. IEEE Transactions on Automatic Control, 64(4):1655–1662, 2018

2018
[121]

Consensus of multi- agent systems via fully distributed event-triggered control

Xianwei Li, Yang Tang, and Hamid Reza Karimi. Consensus of multi- agent systems via fully distributed event-triggered control. Automatica, 116:108898, 2020

2020
[122]

W. Shi, J. Cao, Q. Zhang, Y . Li, and L. Xu. Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5):637–646, 2016

2016
[123]

Y . Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief. A survey on mobile edge computing: The communication perspective. IEEE Communications Surveys Tutorials, 19(4):2322–2358, 2017

2017
[124]

Deep reinforcement learning for scheduling in large-scale networked control systems

Adrian Redder, Arunselvan Ramaswamy, and Daniel E Quevedo. Deep reinforcement learning for scheduling in large-scale networked control systems. IFAC-PapersOnLine, 52(20):333–338, 2019

2019
[125]

Distributed control of large-scale networked control systems with communication constraints and topology switching

Dan Zhang, Sing Kiong Nguang, and Li Yu. Distributed control of large-scale networked control systems with communication constraints and topology switching. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(7):1746–1757, 2017

2017
[126]

A simple self-triggered sampler for nonlinear systems

Ubaldo Tiberi and Karl Henrik Johansson. A simple self-triggered sampler for nonlinear systems. IFAC Proceedings V olumes, 45(9):76– 81, 2012

2012
[127]

Shallow versus deep neural networks in gear fault diagnosis

Giansalvo Cirrincione, Rahul Ranjeev Kumar, Ali Mohammadi, Shahin Hedayati Kia, Pietro Barbiero, and Jacopo Ferretti. Shallow versus deep neural networks in gear fault diagnosis. IEEE Transactions on Energy Conversion, 35(3):1338–1347, 2020

2020
[128]

Bearing fault event-triggered diagnosis using a variational mode decomposition-based machine learning approach

Houssem Habbouche, Yassine Amirat, Tarak Benkedjouh, and Mohamed Benbouzid. Bearing fault event-triggered diagnosis using a variational mode decomposition-based machine learning approach. IEEE Transac- tions on Energy Conversion, 2021

2021
[129]

Composite neural learning fault-tolerant control for underactuated vehicles with event-triggered input

Guoqing Zhang, Shengjia Chu, Xu Jin, and Weidong Zhang. Composite neural learning fault-tolerant control for underactuated vehicles with event-triggered input. IEEE Transactions on Cybernetics, 51(5):2327– 2338, 2020

2020
[130]

Learning observer based and event-triggered control to spacecraft against actuator faults

Chengxi Zhang, Jihe Wang, Dexin Zhang, and Xiaowei Shao. Learning observer based and event-triggered control to spacecraft against actuator faults. Aerospace Science and Technology, 78:522–530, 2018

2018
[131]

J. Park, S. Samarakoon, M. Bennis, and M. Debbah. Wireless network intelligence at the edge. Proceedings of the IEEE, 107(11):2204–2239, 2019

2019
[132]

M. Chen, Z. Yang, W. Saad, C. Yin, H. V . Poor, and S. Cui. A joint learning and communications framework for federated learning over wireless networks. IEEE Transactions on Wireless Communications, to appear, 2020

2020
[133]

Secure consensus of multi-agent systems with redundant signal and communication interference via distributed dynamic event- triggered control

Can Zhao, Xinzhi Liu, Shouming Zhong, Kaibo Shi, Daixi Liao, and Qishui Zhong. Secure consensus of multi-agent systems with redundant signal and communication interference via distributed dynamic event- triggered control. ISA transactions, 112:89–98, 2021. LEILA SEDGHI was bor...

2021
[2012]

Since 2001, he has been a Lecturer with the Department of Electrical Engineering, Srinakhar- inwirot University, Bangkok, where his research focuses on predictive control strategies to system science, control of communication networks, control of energy systems, and uncertain ...

2001
[2015]

He is currently a Research Fellow with the School of Computer Science & IT, University College Cork, Ireland

He was a Postdoctoral Research Fellow with the Centre for Infocomm Technology (INFINI- TUS), Nanyang Technological University (NTU), Singapore. He is currently a Research Fellow with the School of Computer Science & IT, University College Cork, Ireland. His research interests ...

2015

Pith tools

Reviewed August 27, 2026 · model on record in the stance chip above.

[1] [23]

Learning self-triggered controllers with Gaussian processes

Kazumune Hashimoto, Yuichi Yoshimura, and Toshimitsu Ushio. Learn- ing self-triggered controllers with gaussian processes. arXiv preprint arXiv:1909.00178, 2019

work page Pith review arXiv 1909

[2] [12]

Deep reinforcement learning for event-triggered control

Dominik Baumann, Jia-Jie Zhu, Georg Martius, and Sebastian Trimpe. Deep reinforcement learning for event-triggered control. In 2018 IEEE Conference on Decision and Control (CDC), pages 943–950. IEEE, 2018

2018

[3] [44]

Adaptive resilient event-triggered control design of autonomous vehicles with an iterative single critic learning framework

Kun Zhang, Rong Su, Huaguang Zhang, and Yunlin Tian. Adaptive resilient event-triggered control design of autonomous vehicles with an iterative single critic learning framework. IEEE transactions on neural networks and learning systems, 32(12):5502–5511, 2021

2021

[4] [11]

Event-triggered learning for resource-efﬁcient networked con- trol

Friedrich Solowjow, Dominik Baumann, Jochen Garcke, and Sebastian Trimpe. Event-triggered learning for resource-efﬁcient networked con- trol. In 2018 Annual American Control Conference (ACC), pages 6506–

2018

[5] [14]

Event-triggered learning

Friedrich Solowjow and Sebastian Trimpe. Event-triggered learning. Automatica, 117:109009, 2020

2020

[6] [61]

Event-triggered distributed h ∞ constrained control of physically interconnected large-scale partially unknown strict-feedback systems

Luy Nguyen Tan. Event-triggered distributed h ∞ constrained control of physically interconnected large-scale partially unknown strict-feedback systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(4):2444–2456, 2019

work page 2019

[7] [1]

C. Sun, G. Cembrano, V . Puig, and J. Meseguer. Cyber-physical systems for real-time management in the urban water cycle. In 2018 Interna- tional Workshop on Cyber-physical Systems for Smart Water Networks (CySWater), pages 5–8, 2018

2018

[8] [2]

Cyber-physical systems research and education in 2030: Scenarios and strategies

Didem Gürdür Broo, Ulf Boman, and Martin Törngren. Cyber-physical systems research and education in 2030: Scenarios and strategies. Journal of Industrial Information Integration, 21:100192, 2021

2021