Distributed Group Coordination of Multiagent Systems in Cloud Computing Systems Using a Model-Free Adaptive Predictive Control Strategy

Tan, Haoran; Wang, Yaonan; Wu, Min; Huang, Zijian; Miao, Zhiqiang

doi:10.1109/tnnls.2021.3053016

Cited by 41 publications

(8 citation statements)

References 42 publications

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“…In the control predictor, the latest data sequence at time t is . By using and an incremental control law in [9], the following control increment predictions are generated: Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org.…”

Section: Control Predictor R(t)mentioning

confidence: 99%

“…Theorem 1 [9]: If , the closed-loop system for R-NPC1 is stable with . 2) Stability of R-NPC2: From ( 4) and ( 5), we have…”

Section: T + τCamentioning

confidence: 99%

“…In this case, a data-driven NPC approach is necessary. In [9] and [10], a data-driven NPC method was proposed to compensate for random RTTDs. According to delay compensation ways, another two data-driven NPC methods can also be designed.…”

mentioning

confidence: 99%

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Comparison of Three Data-Driven Networked Predictive Control Methods for a Class of Nonlinear Systems

Pang

Zhao

Sun

et al. 2022

IEEE/CAA J. Autom. Sinica

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Section: Control Predictor R(t)mentioning

confidence: 99%

“…Theorem 1 [9]: If , the closed-loop system for R-NPC1 is stable with . 2) Stability of R-NPC2: From ( 4) and ( 5), we have…”

Section: T + τCamentioning

confidence: 99%

See 1 more Smart Citation

Comparison of Three Data-Driven Networked Predictive Control Methods for a Class of Nonlinear Systems

Pang

Zhao

Sun

et al. 2022

IEEE/CAA J. Autom. Sinica

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“…Where, Φ(k) is given by Lemma 2; Φ(k + j), j = 1, • • • , N i − 1 are given by Table 1 and formula (25).…”

Section: Design Of the Is-mfapc Schemementioning

confidence: 99%

“…Then almost all the subsequent MFAPC approaches kept using the GPC framework for realizing predictive function. So far, the prototype GPC-MFAPC and its extended versions have been successfully applied in many areas such as robot manipulators [19], chemical processes [20], traffic network systems [21][22][23][24], cloud computing systems [25], industrial processes [26], liquid level control [12] and so forth thereafter.…”

Section: Introductionmentioning

confidence: 99%

Design of Model-Free Adaptive Predictive Controllers with Low Online Computational Load for a Class of Discrete-Time Nonlinear Multiple-Input Multiple-Output Systems

Wang

et al. 2022

Preprint

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This paper proposes a novel model-free adaptive predictive control approach with low online computational load for of a class of unknown discretetime nonlinear multiple-input multiple-output systems. First, historical measurable input/output data is utilized to online construct equivalent explicit data models for control design, making the controllers model-free. Second, input structuralization where system inputs are expressed by linear combination of a few basis functions is integrated into predictive controller design for reducing online computational load. Third, a novel Kalman Filter-based regression factor computation approach is developed to predict data model parameters for realizing predictive function of controllers such that closed-loop system robustness can be enhanced when sudden changes from prescribed references are encountered. Last, numerical simulations validate that, by using much less online computational load, the proposed approach can achieve equivalent control performance compared with the existing approaches.

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Dynamic event‐triggered model‐free adaptive control for networked control systems with random packet loss

Sun,

Lu,

Yan

et al. 2024

Intl J Robust & Nonlinear

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A novel dual‐channel dynamic event‐triggered model‐free adaptive control method with compensation is proposed for nonlinear networked control systems (NCSs) subjected to random packet loss. In order to tackle the issue of redundant data transmission, a dual‐channel triggering control framework is designed, where data transmission only occurs upon meeting the triggering conditions. Compared with the traditional static event‐triggered schemes with fixed triggering thresholds, the proposed method allows for dynamically adjustable triggering thresholds. This means that the number of data transmissions in the forward and feedback channels can be further reduced. In addition, considering the detrimental impact of random packet loss and output event‐triggered on the system, compensation is applied to the system's output data using the linear data model of the nonlinear system, which is directly derived from I/O data without incorporating any other mechanistic model information, thereby ensuring optimal control performance. In contrast to existing results, the proposed control strategy can enhance system performance while conserving network communication resources. The effectiveness and superiority of the proposed scheme have been validated through two simulations.

show abstract

Distributed Group Coordination of Multiagent Systems in Cloud Computing Systems Using a Model-Free Adaptive Predictive Control Strategy

Cited by 41 publications

References 42 publications

Comparison of Three Data-Driven Networked Predictive Control Methods for a Class of Nonlinear Systems

Comparison of Three Data-Driven Networked Predictive Control Methods for a Class of Nonlinear Systems

Design of Model-Free Adaptive Predictive Controllers with Low Online Computational Load for a Class of Discrete-Time Nonlinear Multiple-Input Multiple-Output Systems

Dynamic event‐triggered model‐free adaptive control for networked control systems with random packet loss

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