Adaptive Sliding-mode Control Based On Decoupled Method for A Class of Underactuated System
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- 英文论文题名：Adaptive Sliding-mode Control Based On Decoupled Method for A Class of Underactuated System
- 论文作者：Guo Jun Su ; Qing Lin Gong ; Hui Yan Li
- 英文论文作者：Guo Jun Su ; Qing Lin Gong ; Hui Yan Li ; School of Automation and Electrical Engineering ; Tianjin University of Technology and Education ; School of track and electrical Engineering ; Chongqing Vocational College of Public Transportation
- 年：2017
- 作者机构：School of Automation and Electrical Engineering,Tianjin University of Technology and Education;School of track and electrical Engineering,Chongqing Vocational College of Public Transportation;
- 英文论文关键词：Underactuated system ; Sliding-mode control ; Decoupled control ; Overhead crane
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（A）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（A）
- 语种：英文
- 分类号：TP273
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：5
- 文件大小：144k
- 原文格式：D
- 会议级别：全国
摘要
An adaptive radial basis function neural network(RBFNN) sliding-mode controller with decoupled method was proposed for a class of underactuated system. Different sub-systems were devided according to the performance of the system, and the corresponding sub-sliding plane was designed for the sub-system; The RBFNN controller was designed for one of the sub-system, and its adaptive algorithm was derived by the reaching conditon, the sub-sliding planes were connected by the intermediate functions, the stability of the control system can be guaranteed when one of the sub-system was stable. At last, overhead crane transporting control was used to illustrate the rapidness and robustness of the proposed approach.
An adaptive radial basis function neural network(RBFNN) sliding-mode controller with decoupled method was proposed for a class of underactuated system. Different sub-systems were devided according to the performance of the system, and the corresponding sub-sliding plane was designed for the sub-system; The RBFNN controller was designed for one of the sub-system, and its adaptive algorithm was derived by the reaching conditon, the sub-sliding planes were connected by the intermediate functions, the stability of the control system can be guaranteed when one of the sub-system was stable. At last, overhead crane transporting control was used to illustrate the rapidness and robustness of the proposed approach.
An adaptive radial basis function neural network(RBFNN) sliding-mode controller with decoupled method was proposed for a class of underactuated system. Different sub-systems were devided according to the performance of the system, and the corresponding sub-sliding plane was designed for the sub-system; The RBFNN controller was designed for one of the sub-system, and its adaptive algorithm was derived by the reaching conditon, the sub-sliding planes were connected by the intermediate functions, the stability of the control system can be guaranteed when one of the sub-system was stable. At last, overhead crane transporting control was used to illustrate the rapidness and robustness of the proposed approach.
引文
[1]Y JOHN,G WEIBING,H JAMES C.Variable Structure Control:A Survey.IEEE Trans on Industrial Electronics,40(1):2–22,1993.
[2]L C HUNG H Y CHUNG,Decoupled control using neural network-based sliding-mode controller for nonlinear systems.Expert Systems with Application,32:1168-1182,2013.
[3]LIN C M,Mon Y G.Decoupling control by hierarchical fuzzy sliding-mode controller,IEEE Trans on Control Systems Technology,13(4):593–598,2011.
[4]GIORGIO C,ALESSANDRO G,GIAMPAOLO U.An implicit gain scheduling controller for cranes.IEEE Trans on Control Systems Technology,6(1):15–20,2013.
[5]WANG Wei,YI.Hierarchical sliding-mode control method for overhead cranes.Acta Automatica Sinica,30(5):784–788,2004.
[6]S Saeedi,L Paull,M Trentini,H Li.Neural Network-Based Multiple Robot Simultaneous Localization and Mapping.IEEE Transactions on Neural Networks,33(1):3–46,2016.
[7]Girshick R,Girshick R.Fast R-CNN.in Proceedings of the International Conference on Computer Vision.2015
[8]REN S,HE K,GIRSHICK R,et al.Faster R-CNN:Towards Real-Time Object Detection with Region Proposal Networks.IEEE Transactions on Pattern Analysis&Machine Intelligence,2016.
[9]Rong-Jong Wai,Li-Chung Shih.Adaptive Fuzzy-NeuralNetwork Design for Voltage Tracking Control of a DC-DC Boost Converter.IEEE Transactions on Power Electronics.2012
[10]Y S KIM,H S SEO,S K SUL,A new anti-sway control scheme for trolley crane system,IEEE-IAC Annu Meeting Chicago:[s.n.],2001:548–552.
[11]X WANG X LI J WANG X FANG X ZHU.Datadriven model-free adaptive sliding mode control for the multi degree-of-freedom robotic exoskeleto L.Information Sciences An International Journal,327(C):246–257,2016.
[12]RE Precup MB Radac RC Roman EM Petriu.Modelfree sliding mode control of nonlinear systems:Algorithms and experiments.Information Sciences,381:176–192,2016.
[13]Girshick R,Donahue J,Darrell T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation.In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition CVPR.2014.
[2]L C HUNG H Y CHUNG,Decoupled control using neural network-based sliding-mode controller for nonlinear systems.Expert Systems with Application,32:1168-1182,2013.
[3]LIN C M,Mon Y G.Decoupling control by hierarchical fuzzy sliding-mode controller,IEEE Trans on Control Systems Technology,13(4):593–598,2011.
[4]GIORGIO C,ALESSANDRO G,GIAMPAOLO U.An implicit gain scheduling controller for cranes.IEEE Trans on Control Systems Technology,6(1):15–20,2013.
[5]WANG Wei,YI.Hierarchical sliding-mode control method for overhead cranes.Acta Automatica Sinica,30(5):784–788,2004.
[6]S Saeedi,L Paull,M Trentini,H Li.Neural Network-Based Multiple Robot Simultaneous Localization and Mapping.IEEE Transactions on Neural Networks,33(1):3–46,2016.
[7]Girshick R,Girshick R.Fast R-CNN.in Proceedings of the International Conference on Computer Vision.2015
[8]REN S,HE K,GIRSHICK R,et al.Faster R-CNN:Towards Real-Time Object Detection with Region Proposal Networks.IEEE Transactions on Pattern Analysis&Machine Intelligence,2016.
[9]Rong-Jong Wai,Li-Chung Shih.Adaptive Fuzzy-NeuralNetwork Design for Voltage Tracking Control of a DC-DC Boost Converter.IEEE Transactions on Power Electronics.2012
[10]Y S KIM,H S SEO,S K SUL,A new anti-sway control scheme for trolley crane system,IEEE-IAC Annu Meeting Chicago:[s.n.],2001:548–552.
[11]X WANG X LI J WANG X FANG X ZHU.Datadriven model-free adaptive sliding mode control for the multi degree-of-freedom robotic exoskeleto L.Information Sciences An International Journal,327(C):246–257,2016.
[12]RE Precup MB Radac RC Roman EM Petriu.Modelfree sliding mode control of nonlinear systems:Algorithms and experiments.Information Sciences,381:176–192,2016.
[13]Girshick R,Donahue J,Darrell T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation.In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition CVPR.2014.