Data-Based Predictive Control for Networked Nonlinear Systems With Network-Induced Delay and Packet Dropout

Pang, Zhong-Hua; Liu, Shuai; Zhou, Donghua; Sun, Dehui

doi:10.1109/tie.2015.2497206

Cited by 156 publications

(60 citation statements)

References 39 publications

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“…To build the free-model adaptive control (MFAC) for system (1), two following assumptions are made [13].…”

Section: Problem Descriptionmentioning

confidence: 99%

“…Based on Assumptions 1 and 2, system (1) can be represented in a compacted form dynamic linearization [13] ( 1) ( ) ( )…”

Section: Problem Descriptionmentioning

confidence: 99%

“…Two model-based methods were employed which were the robust variable sampling period controller (RVSPC) [12], and the static state feedback controller (SSFC) [10] combined with the NPD module and a fixed buffer, tagged as SSFC-FB. A model-free method -the MFAC [13] with fixed weight factor  combined with a fixed buffer (named as MFAC-FB) was used to perform the comparison. …”

Section: Case Studymentioning

confidence: 99%

“…Some limitations still exist in the RVSPC as: the state feedback control unit is basically designed for a welldefined networked linear system which is actually not easy to be achieved; for a NCS with large delays and/or high packet dropout ratio, the performance may be degraded when the driving commands and sensing signals are long delayed/dropped. Thanks to the development of model-free control, this advanced technology is successful utilized to construct NCSs with the good adaptability [13]. Nevertheless, these control schemes remain some open problems: the network problems are assumed to be bounded within a predefined maximum round trip delay; the large delay bound also leads to the slow convergence speed; the buffers need to be large enough to deal with the worst network states; the task must be given at the plant side while it is normally at the control side in actual remote applications.…”

Section: Introductionmentioning

confidence: 99%

“…This paper aims to address all the problems raised by [12], [13] by introducing a data-based hybrid driven control (DHDC) approach for a class of imperfect NSCs. The main contribution of this work can be expressed as: 1) Delays and packet dropouts are quickly and accurately detected online by a network problem detector (NPD) using a new concept to classify network problems.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A data-based hybrid driven control for networked-based remote control applications

Dinh

Marco

Greenwood

et al. 2017

2017 IEEE International Conference on Mechatronics (ICM)

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Copies of full items can be used for personal research or study, educational, or not-for profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way.Publisher's statement: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP url' above for details on accessing the published version and note that access may require a subscription. Abstract-This paper develops a data-based hybrid driven control (DHDC) approach for a class of networked nonlinear systems compromising delays, packet dropouts and disturbances. First, the delays and/or packet dropouts are detected and updated online using a network problem detector. Second, a single-variable first-order proportional-integral (PI) -based adaptive grey model is designed to predict in a near future the network problems. Third, a hybrid driven scheme integrated a small adaptive buffer is used to allow the system to operate without any interrupt due to the large delays or packet dropouts. Forth, a prediction-based model-free adaptive controller is developed to compensate for the network problems. Effectiveness of the proposed approach is demonstrated through a case study.

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“…To build the free-model adaptive control (MFAC) for system (1), two following assumptions are made [13].…”

Section: Problem Descriptionmentioning

confidence: 99%

“…Based on Assumptions 1 and 2, system (1) can be represented in a compacted form dynamic linearization [13] ( 1) ( ) ( )…”

Section: Problem Descriptionmentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A data-based hybrid driven control for networked-based remote control applications

Dinh

Marco

Greenwood

et al. 2017

2017 IEEE International Conference on Mechatronics (ICM)

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Observer‐based adaptive event‐triggered neural tracking control for nonlinear cyber‐physical systems with incomplete measurements

Wang

2023

Adaptive Control & Signal

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Summary In this paper, an adaptive event‐triggered neural networks (NNs) tracking control problem is investigated for cyber‐physical Systems (CPSs) with incomplete measurements. The state variables can get unavailable or distorted in incomplete measurements because of data transmission problems, which can degrade the performance of the system. To solve these problems, the radial basis function neural networks (RBF NNs) control is used to approximate the unknown nonlinear function in CPSs, and the Butterworth Low‐pass Filter (LPF) is used to construct the NNs observer, which can estimate the immeasurable states. By using the Lyapunov function, the tracking error of the controller has limited to a small boundary. Based on backstepping control theory and event‐triggered theory, the control signal of the fixed threshold strategy is obtained and two adaptive controllers for CPSs are established, it can ensure that all the closed‐loop signals are uniformly ultimately bounded (UUB) in mean square and avoid the Zeno‐behavior. The simulation results confirm the feasibility and effectiveness of the controller.

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Networked‐based H_∞ control for discrete‐time slow sampling singularly perturbed systems via an improved event‐triggered approach

Cai

2022

Asian Journal of Control

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This paper investigates the H∞ control problem for a class of slow sampling singularly perturbed systems (S3PSs) with an improved event‐triggered method (ETM). Compared with the conventional static ETM, an improved one with time‐varying threshold is exploited to enhance the dynamic performance for the S3PSs with certain communication frequency reduction. By adjusting the threshold with the triggering error, a faster converge speed can be achieved. Sufficient conditions are derived by constructing a singular perturbation parameter (SPP) dependent Lyapunov function, with which both asymptotic stability can be guaranteed for the closed‐loop S3PSs and the ill‐conditioned numerical issues can be avoided. By resorting to the matrix inequality techniques, an ETM‐based controller can be developed within the upper bound of the SPP. Further demonstration for the feasibility of the proposed algorithm is presented by designing an ETM‐based controller for an inverted pendulum system.

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Data-Based Predictive Control for Networked Nonlinear Systems With Network-Induced Delay and Packet Dropout

Cited by 156 publications

References 39 publications

A data-based hybrid driven control for networked-based remote control applications

A data-based hybrid driven control for networked-based remote control applications

Observer‐based adaptive event‐triggered neural tracking control for nonlinear cyber‐physical systems with incomplete measurements

Networked‐based H_∞ control for discrete‐time slow sampling singularly perturbed systems via an improved event‐triggered approach

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Data-Based Predictive Control for Networked Nonlinear Systems With Network-Induced Delay and Packet Dropout

Cited by 156 publications

References 39 publications

A data-based hybrid driven control for networked-based remote control applications

A data-based hybrid driven control for networked-based remote control applications

Observer‐based adaptive event‐triggered neural tracking control for nonlinear cyber‐physical systems with incomplete measurements

Networked‐based H∞ control for discrete‐time slow sampling singularly perturbed systems via an improved event‐triggered approach

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Networked‐based H_∞ control for discrete‐time slow sampling singularly perturbed systems via an improved event‐triggered approach