A new approach of cloud control systems: CCSs based on data-driven predictive control

Gao, Runze; Xia, Yuanqing; Liang, Ma

doi:10.1109/cac.2017.8243371

Cited by 8 publications

(9 citation statements)

References 14 publications

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“…First, the coefficient matrices L w and L u are calculated as (12). Then, the L w and L u are used to compute the optimal data-driven predicted control strategy as (13).…”

Section: A Computation Amount Analysis Of the Dpc Methodsmentioning

confidence: 99%

“…Substituting (7) into (12) and taking derivative with respect to u f , the data-driven predictive control law without constraints can be computed as…”

Section: Preliminary Of Data-driven Predictive Controlmentioning

confidence: 99%

“…In the meanwhile, data-driven predictive control (DPC) method is developed to provide the control service without the need of the prior knowledge of system model. Therefore, data-driven predictive cloud control system (DPCCS) is proposed, to deal with the control problems only relying on the historical input and output data in the cloud control environment [12], [13].…”

Section: Introductionmentioning

confidence: 99%

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Workflow-based Fast Data-driven Predictive Control with Disturbance Observer in Cloud-edge Collaborative Architecture

Gao¹,

Li²,

Li³

et al. 2022

Preprint

Self Cite

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Data-driven predictive control (DPC) has been studied and used in various scenarios, since it could generate the predicted control sequence only relying on the historical input and output data. Recently, based on cloud computing, data-driven predictive cloud control system (DPCCS) has been proposed with the advantage of sufficient computational resources. However, the existing computation mode of DPCCS is centralized. This computation mode could not utilize fully the computing power of cloud computing, of which the structure is distributed. Thus, the computation delay could not been reduced and still affects the control quality. In this paper, a novel cloud-edge collaborative containerised workflowbased DPC system with disturbance observer (DOB) is proposed, to improve the computation efficiency and guarantee the control accuracy. First, a construction method for the DPC workflow is designed, to match the distributed processing environment of cloud computing. But the non-computation overheads of the workflow tasks, such as receiving and sending data, serialization and deserialization, are relatively high and affect the overall processing speed of workflow. Therefore, a cloud-edge collaborative control scheme with DOB is designed. The low-weight data could be truncated to reduce the non-computation overheads. Meanwhile, we design an edge DOB to estimate and compensate the uncertainty caused by the truncation operation in cloud workflow processing, and obtain the cloud-edge composite control variable. The uniformly ultimately bounded stability of the DOB is also proved. Third, to execute the workflow-based DPC controller and evaluate the proposed cloud-edge collaborative control scheme with DOB in the real cloud environment, we design and implement a practical workflow-based cloud control experimental system based on container technology. Finally, a series of evaluations show that, the computation times are decreased by 45.19% and 74.35% for two real-time control examples, respectively, and by at most 85.10% for a high-dimension control example.

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“…First, the coefficient matrices L w and L u are calculated as (12). Then, the L w and L u are used to compute the optimal data-driven predicted control strategy as (13).…”

Section: A Computation Amount Analysis Of the Dpc Methodsmentioning

confidence: 99%

“…Substituting (7) into (12) and taking derivative with respect to u f , the data-driven predictive control law without constraints can be computed as…”

Section: Preliminary Of Data-driven Predictive Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Workflow-based Fast Data-driven Predictive Control with Disturbance Observer in Cloud-edge Collaborative Architecture

Gao¹,

Li²,

Li³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…139 published [30]- [32]. Xia, Y. has the biggest circle size due to his contribution with other authors in different clusters too, which are: (1) Xia, Y.; Ma, L. collaborated with Gao, R. from cluster 3 (dark blue color) in [33],…”

Section: Startmentioning

confidence: 99%

Cloud-based control systems: a systematic literature review

Wijaya¹,

Ramadhan

Andhika³

2023

IJRES

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Control systems and computer science are two distinct and important fields of engineering. The development of cloud computing in computer science has become an enabler for the widely used controller in control systems to migrate to the cloud and has created a new field of research in cloud-based control systems (CCS). The paper used the systematic literature review approach to obtain insight into current CCS research. The objectives include a review in areas such as the demographics, topics of the research, evaluation method, and application domain. To that end, systematic literature review (SLR) has been conducted. The study obtained 64 primary studies from 581 articles. The CCS has a distinct characteristic; despite the fact that the cloud and network dynamics system, when coupled with the controlled plant, is inherently nonlinear, research efforts have used linear models with optimal control to approach it successfully in a limited case of control objectives. Furthermore, cloud-centric and cloud-fog network architecture approaches are considered in the studies—whereas, the quantitative method mainly uses simulation and discussion. Finally, the SLR summarizes open challenges for CCS in the future.

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“…Thus cloud computing and distributed computing are gathering more and more attention in DPC tasks for the possibility of computation acceleration by properly utilizing elastic resources in the cloud. [6], [7] develop a cloud-edge-endpoint DPC prototype, showing the feasibility of cloud-based control systems. To optimize the effort of subspace identification task, which is the basis of data-driven control, [13] decomposes the identification algorithm to interconnected containerized tasks through parallel computing.…”

Section: Related Workmentioning

confidence: 99%

Encrypted Data-driven Predictive Cloud Control with Disturbance Observer

Li¹,

Gao²,

Xia³

2023

Preprint

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In data-driven predictive cloud control tasks, the privacy of data stored and used in cloud services could be leaked to malicious attackers or curious eavesdroppers. Homomorphic encryption technique could be used to protect data privacy while allowing computation. However, extra errors are introduced by the homomorphic encryption extension to ensure the privacy-preserving properties, and the real number truncation also brings uncertainty. Also, process and measure noise existed in system input and output may bring disturbance. In this work, a data-driven predictive cloud controller is developed based on homomorphic encryption to protect the cloud data privacy. Besides, a disturbance observer is introduced to estimate and compensate the encrypted control signal sequence computed in the cloud. The privacy of data is guaranteed by encryption and experiment results show the effect of our cloud-edge cooperative design.

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A new approach of cloud control systems: CCSs based on data-driven predictive control

Cited by 8 publications

References 14 publications

Workflow-based Fast Data-driven Predictive Control with Disturbance Observer in Cloud-edge Collaborative Architecture

Workflow-based Fast Data-driven Predictive Control with Disturbance Observer in Cloud-edge Collaborative Architecture

Cloud-based control systems: a systematic literature review

Encrypted Data-driven Predictive Cloud Control with Disturbance Observer

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