.png){kind=icon}
返回
Workflow-Based Fast Data-Driven Predictive Control With Disturbance Observer in Cloud-Edge Collaborative Architecture
.png){kind=icon}
OA
AI
DOI：10.1109/TASE.2023.3270203.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Data-driven predictive control (DPC) has been studied and applied in various scenarios. However, the challenge of computational efficiency remains. With the development of cloud computing, it provides potential solutions to the computational problem. Hence, this paper proposes a workflowbased fast DPC method in cloud-edge collaborative architecture. First, a workflow construction method of DPC is designed to make full use of the distributed computing ability of cloud computing. Next, to tackle the uncertainty in the cloud workflow processing, we design a cloud-edge collaborative scheme. In this scheme, a edge data-driven disturbance observer is proposed to estimate and compensate the uncertain event with guaranteed UUB stability. Then, An autonomous cloud control experimental system based on container technology is designed and implemented to execute the workflow-based DPC controller. Evaluations demonstrate that computation times are reduced by 45.19% and 74.35% for two real-time control examples, and by a maximum of 85.10% for a high-dimensional control example. Note to Practitioners-This work is motivated by the challenge of the further combination of cloud computing and control system such as DPC. In the existing cloud-based control system, the computation mission of native control algorithm is deployed in a single cloud server directly. However, the structure of cloud environment is distributed, and the existing computation mode could not make full use of the parallel computing of cloud computing. Thus, the computation time could not be reduced significantly, and would still have serious effects on the control quality. In this work, a novel workflow-based DPC system in cloud-edge collaborative architecture is proposed, which decompose the native control mission into distributed cloud workflow with multiple smaller computation tasks. Then, an edge disturbance observer is designed to compensate the uncertainty occurring in the cloud workflow processing. As the evaluations show, the proposed workflow-based control system in cloud-edge collaborative architecture could be applied in the control mission with real-time requirement and the highdimension control mission with intensive data.
Keyword:
Data-driven predictive control
cloud control system
cloud-edge collaborative control
container technology
cloud workflow processing
期刊
IF:
6.4
论文数:
5.2K
被引数:
1.6W
机构
引用论文
Succinylated copper, zinc superoxide dismutase. A novel approach to the problem of active subunits琥珀酰化铜，锌超氧化物歧化酶。一种解决活性亚基问题的新方法.png)
Biochemistry
BiochemistryIF0
PREAI
分享收藏
查看更多内容