Decentralized Event-triggered Control for Interconnected Systems with Unknown Disturbances

Cai, Jianping; Yu, Rui; Wang, Binrui; Mei, Congli; Shen, Liran

doi:10.1016/j.jfranklin.2019.10.033

Cited by 57 publications

(30 citation statements)

References 24 publications

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“…On the other hand, the derived results in this work make it interesting to consider the extension of this framework to closed-loop networked control systems where the objective is to identify the performance of control under communication constraints, see for instance [5], [8], [36]. Due to the nonlinear and uncertain characteristics of most of the practical networked systems, see e.g., [6], [7], [37], the design and performance analysis of the coding-estimation and control systems in such cases will be also under consideration in future. Advancements in these relevant researches will be valuable for various applications of networked systems.…”

Section: Discussionmentioning

confidence: 97%

“…Recent advancements in signal processing and communication technologies have led to significant research attentions on wireless sensor networks (WSNs) and networked control systems (NCSs) [1]- [6]. The problem of state estimation or filtering over digital communication channel is a commonly performed task in networked applications such as radar tracking, industrial monitoring, system control, etc.…”

Section: Introductionmentioning

confidence: 99%

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Real Time Coding and Estimation of Linear Discrete Time System Over Networks

Chen

Zhou

2020

IEEE Access

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This study deals with the real time coding and estimation of linear discrete time scalar system over communication networks. With the mean-squared error (MSE) distortion criterion, the information rate distortion function describing the performance limit of the coding-estimation system is analyzed and discussed. To achieve near instantaneous encoding and decoding, an asymptotic design scheme has been presented as a realization of real time coding-estimation system. The outputs of standard Kalman filter relying on unquantized observations are encoded using the Lloyd-Max quantization rules and transmitted over the noiseless channel. The decoder side runs the corresponding decoding and reconstruction algorithms to produce the optimal real time state estimate of system. To synchronize the encoder and decoder, a doublepredictor regime on the updating rules of the encoder-decoder pair is proposed, and it does not require any feedback information. The rate distortion function of the proposed scheme is derived and when comparing with the information theoretical lower bound, there is only a factor discrepancy related to the quantization rules. The rate distortion performance results of various design schemes are compared and demonstrated with numerical simulations. INDEX TERMS Real time coding and estimation, mean-squared error, information rate distortion function, Lloyd-Max quantization, double-predictor regime.

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Section: Discussionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Real Time Coding and Estimation of Linear Discrete Time System Over Networks

Chen

Zhou

2020

IEEE Access

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“…PLI is a significant source of noise in the frequency range of 50-60 Hz. State Space Recursive Least Square Adaptive Filter (SSRLS), ANF, and Fast Fourier Transform (FFT) Filter-based denoising of power-line interference is performed by [26][27][28], [31] and unknown external disturbances are removed by adaptive control schemes in [32]. Other external white noise can be removed by Hardware Descriptive Language (HDL)-based Finite Impulse Response (FIR) filters and NN-based Denoising Autoencoders (DAE) [22][23][24], [29].…”

Section: B Stage2: Denoisingmentioning

confidence: 99%

Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

et al. 2020

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Electrocardiogram (ECG) gives essential information about different cardiac conditions of the human heart. Its analysis has been the main objective among the research community to detect and prevent life threatening cardiac circumstances. Traditional signal processing methods, machine learning and its subbranches, such as deep learning, are popular techniques for analyzing and classifying the ECG signal and mainly to develop applications for early detection and treatment of cardiac conditions and arrhythmias. A detailed literature survey regarding ECG signal analysis is presented in this paper. We first introduce a stages-based model for ECG signal analysis where a survey of ECG analysis related work is then presented in the form of this stage-based process model. The model describes both traditional time/frequency-domain and advanced machine learning techniques reported in the published literature at every stage of analysis, starting from ECG data acquisition to its classification for both simulations and real-time monitoring systems. We present a comprehensive literature review of real-time ECG signal acquisition, prerecorded clinical ECG data, ECG signal processing and denoising, detection of ECG fiducial points based on feature engineering and ECG signal classification along with comparative discussions among the reviewed studies. This study also presents a detailed literature review of ECG signal analysis and feature engineering for ECG-based body sensor networks in portable and wearable ECG devices for real-time cardiac status monitoring. Additionally, challenges and limitations are discussed and tools for research in this field as well as suggestions for future work are outlined. INDEX TERMS ECG analysis, cardiac arrhythmias, QRS and ST detection, ECG classification, deep learning.

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“…Finite-time stability is not only a need, but also a trend. The investigations about finite-time stability are more interesting but challenging than other types of stability, which inspired many scholars to make great achievements [30]- [44]. For example, the authors in [30] have investigated the global finite-time stabilization for a class of nonlinear systems with parametric uncertainties, and an adaptive finitetime control law is obtained by using the global practical finite-time stability theory, which however did not work for the case that system functions are unknown.…”

Section: Introductionmentioning

confidence: 99%

Observer-Based Adaptive Finite-Time Tracking Control for a Class of Switched Nonlinear Systems With Unmodeled Dynamics

et al. 2020

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This paper investigates the adaptive finite-time tracking control problem for a class of switched nonlinear systems with unmodeled dynamics. In practical applications, switched systems usually possess unfavourable factors, such as unmeasured states and unmodeled dynamics both of which are taken into account in this paper. A dynamic signal defined with a special property is introduced in this paper to improve control performance while garanteeing stability of the controlled system. By designing an observer, a finite-time adaptive output-feedback tracking controller is constructed via the backstepping technique. Then, the finite-time stability problem of the considered systems is studied. It is shown that all the signals in the closed-loop system are semi-globally uniformly finite-time bounded (SGFUB), and the observer errors and tracking errors can be regulated to a small neighborhood of the origin by choosing appropriate parameters. It is noted that, the design process is less complex than some existing results on tackling control problems of nonlinear systems with unmodeled dynamics. In the example, the simulation result testifies the effectiveness of the proposed method.

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Decentralized Event-triggered Control for Interconnected Systems with Unknown Disturbances

Cited by 57 publications

References 24 publications

Real Time Coding and Estimation of Linear Discrete Time System Over Networks

Real Time Coding and Estimation of Linear Discrete Time System Over Networks

Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

Observer-Based Adaptive Finite-Time Tracking Control for a Class of Switched Nonlinear Systems With Unmodeled Dynamics

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