Deep representation-based packetized predictive compensation for networked nonlinear systems
文献类型：期刊论文
| 作者 | Chen, Shaofeng2,3; Cao, Yang1; Kang, Yu1; Sun, Bingyu2 ; Wang, Xuefeng1
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| 刊名 | NEURAL COMPUTING & APPLICATIONS
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| 出版日期 | 2020-10-15 |
| 关键词 | Deep representation Networked control systems Data packet dropouts Stabilization |
| ISSN号 | 0941-0643 |
| DOI | 10.1007/s00521-020-05346-z |
| 通讯作者 | Kang, Yu(kangduyu@ustc.edu.cn) |
| 英文摘要 | The design of networked nonlinear control system is a very challenging problem due to the coupling of the system uncertainties (e.g., model accuracy, noise, nonlinearity) and network effects (e.g., packet dropouts, time delay). In this paper, a deep representation-based predictive compensation method is proposed for networked nonlinear systems with packet-dropouts in the feedback and forward channel. Different from the existing compensation methods based on open-loop prediction, the proposed method is based on feedback compensation and does not require a nominal system model, so that it can avoid the coupling of system uncertainties and network effects as well as the occupation of network bandwidth. Specifically, a deep sequence to sequence learning scheme is firstly employed to encode the correlations of state and control sequences into deep feature representations. Furthermore, within the embedding space spanned by the learned features, according to the state-control sequence of each sampling step, a prediction of the next control command is generated as compensation for packet dropout. The stability of the overall system is rigorously proved by the Lyapunov theory, which reveals that the control errors for the networked control systems with packet dropouts asymptotic converge to a small neighborhood of the origin. We further evaluate the performance of the proposed strategy on a wheeled mobile robots simulation platform, and the experimental results demonstrate that our method can achieve high compensation accuracy and robustness concerning packet dropouts, even in the case of the maximum continuous packet dropouts specified by the network communication protocol. |
| WOS关键词 | SPARSE ; ROBUST ; DELAY |
| 资助项目 | National Natural Science Foundation of China[61725304] ; National Natural Science Foundation of China[61673361] ; National Natural Science Foundation of China[61773360] ; National Key Research and Development Projects of China[2018AAA0100800] ; National Key Research and Development Projects of China[2018YFE0106800] ; Major Science and Technology Projects of Anhui Province[912198698036] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000577917200002 |
| 出版者 | SPRINGER LONDON LTD |
| 资助机构 | National Natural Science Foundation of China ; National Key Research and Development Projects of China ; Major Science and Technology Projects of Anhui Province |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/104539]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Kang, Yu |
| 作者单位 | 1.Univ Sci & Technol China, Dept Automat, Hefei, Peoples R China 2.Chinese Acad Sci, Hefei Inst Phys Sci, Inst Intelligence Machines, Hefei, Peoples R China 3.Univ Sci & Technol China, Hefei, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Shaofeng,Cao, Yang,Kang, Yu,et al. Deep representation-based packetized predictive compensation for networked nonlinear systems[J]. NEURAL COMPUTING & APPLICATIONS,2020. |
| APA | Chen, Shaofeng,Cao, Yang,Kang, Yu,Sun, Bingyu,&Wang, Xuefeng.(2020).Deep representation-based packetized predictive compensation for networked nonlinear systems.NEURAL COMPUTING & APPLICATIONS. |
| MLA | Chen, Shaofeng,et al."Deep representation-based packetized predictive compensation for networked nonlinear systems".NEURAL COMPUTING & APPLICATIONS (2020). |
入库方式： OAI收割
来源：合肥物质科学研究院
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