Event-Triggered Control for Networked Switched Systems With Quantization

Zhao, Rui; Zuo, Zhiqiang; Wang, Yijing

doi:10.1109/tsmc.2021.3139386

Cited by 40 publications

(15 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Remark 2: Essential different from the existing ETPs, by resorting to the dynamically quantified parameter µ(t), a novel IETP is scheduled to arrange the transmission of the packets efficiently. As such, more information can be applied based on IETP (9), which renders higher freedom in analysis control performance. Compared with ETPs (8), fewer packets will be released while maintaining the desired control performance.…”

Section: It Yieldsmentioning

confidence: 99%

“…Meanwhile, for analysis clarity, we define the triggering rate TR = triggering times the number of sampled-data . In the proposed IETP (9), the triggering rate is calculated as 24.98%. However, based on the ETP (8), the triggering rate is computed as 47.11%.…”

Section: Illustrative Examplementioning

confidence: 99%

“…These techniques are so-called static quantization strategies. Comparably, the dynamic quantization mechanism has distinguishing flexibility by introducing dynamic parameters [9]. In contrast to the fruitful achievements of static quantization strategies, the dynamic case has not gained enough research interest, which motivates the present study.…”

Section: Introductionmentioning

confidence: 98%

See 2 more Smart Citations

Protocol-Based Control for Semi-Markov Jump Systems With Dynamic Quantization

Cheng

Yan

et al. 2022

IEEE Trans. Circuits Syst. II

View full text Add to dashboard Cite

Section: It Yieldsmentioning

confidence: 99%

Section: Illustrative Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

Protocol-Based Control for Semi-Markov Jump Systems With Dynamic Quantization

Cheng

Yan

et al. 2022

IEEE Trans. Circuits Syst. II

View full text Add to dashboard Cite

“…The work in [14] presented a quantized distributed composite optimization problem over relay-assisted networks solved via a simplified augmented Lagrangian method. In [15], the stabilization problem for switched linear systems with quantization and eventtriggered control is studied. In [16], a distributed optimization problem affected with quantization was solved using the inexact proximal gradient method.…”

Section: Related Workmentioning

confidence: 99%

Communication-efficient ADMM using Quantization-aware Gaussian Process Regression

Tudela¹,

Wei²,

Nghiem³

2022

Preprint

View full text Add to dashboard Cite

<p> In networks consisting of agents communicating with a central coordinator and working together to solve a global optimization problem in a distributed manner, the agents are often required to solve private proximal minimization subproblems. Such a setting often requires a decomposition method to solve the global distributed problem, resulting in extensive communication overhead. In networks where communication is expensive, it is crucial to reduce the communication overhead of the distributed optimization scheme. Gaussian processes (GPs) are effective at learning the agents’ local proximal operators, thereby reducing the communication between the agents and the coordinator. We propose combining this learning method with adaptive uniform quantization for a hybrid approach that can achieve further communication reduction. In our approach, the GP algorithm is modified to account for the introduced quantization noise statistics due to data quantization. We further improve our approach by introducing an orthogonalization process to the quantizer’s input to address the inherent correlation of the input components. We also use dithering to ensure uncorrelation between the quantizer’s introduced noise and its input. We propose multiple measures to quantify the trade-off between the communication cost reduction and the optimization solution’s accuracy/optimality. Under such metrics, our proposed algorithms can achieve significant communication reduction for distributed optimization with acceptable accuracy, even at low quantization resolutions. This result is demonstrated by simulations of a distributed sharing problem with quadratic cost functions for the agents. </p>

show abstract

“…Therefore, a better event‐triggered strategy should be further developed for PMJSs, which is the first motivation of our investigation. In addition, for the general networked control systems, while introducing the event‐triggered mechanism, data quantization is also considered to further save resources, and many accomplishments have been obtained in References 43‐46. However, due to the fact that the positive state of positive systems increases the complexity of the system design, so far, event‐triggered control or estimation for positive systems with quantization has not been studied, which is the second motivation of our investigation.…”

Section: Introductionmentioning

confidence: 99%

Event‐triggered positive L₁ filter design for positive Markovian jump systems with quantization

Zhang

Jing

2022

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

This paper is concerned with the design of positive L 1 filter for positive Markovian jump systems (PMJSs) with adaptive event-triggered mechanism and quantization. Considering the mode jump of the system, a new adaptive event-triggered condition is established, which not only can regulate the communication threshold according to the error of dynamic systems, but also can avoid the asynchronous phenomenon between the system mode and the filter mode. In order to further reduce the burden of communication, a new positive L 1 filter with the quantized measure system output is developed, and the design condition which ensures the filter error system to be stochastically stable with L 1 performance is presented which can be solved in terms of linear programming (LP) technique. It is illustrated that there is no Zeno phenomenon. The proposed results are applied to the power allocation in a mobile telecommunication system, and it is shown that compared with the existing results, the event-triggered filter of this paper has some advantages.

show abstract

Event-Triggered Control for Networked Switched Systems With Quantization

Cited by 40 publications

References 47 publications

Protocol-Based Control for Semi-Markov Jump Systems With Dynamic Quantization

Protocol-Based Control for Semi-Markov Jump Systems With Dynamic Quantization

Communication-efficient ADMM using Quantization-aware Gaussian Process Regression

Event‐triggered positive L₁ filter design for positive Markovian jump systems with quantization

Contact Info

Product

Resources

About

Event-Triggered Control for Networked Switched Systems With Quantization

Cited by 40 publications

References 47 publications

Protocol-Based Control for Semi-Markov Jump Systems With Dynamic Quantization

Protocol-Based Control for Semi-Markov Jump Systems With Dynamic Quantization

Communication-efficient ADMM using Quantization-aware Gaussian Process Regression

Event‐triggered positive L1 filter design for positive Markovian jump systems with quantization

Contact Info

Product

Resources

About

Event‐triggered positive L₁ filter design for positive Markovian jump systems with quantization