Adaptive Tracking Control of Surface Vessel Using Optimized Backstepping Technique

doi:10.1109/TCYB.2018.2844177

CASIA OpenIR > 离退休人员

	Adaptive Tracking Control of Surface Vessel Using Optimized Backstepping Technique
	Wen, Guoxing 1; Ge, Shuzhi Sam 2,3; Chen, C. L. Philip 4,5,6; Tu, Fangwen 2; Wang, Shengnan 7
发表期刊	IEEE TRANSACTIONS ON CYBERNETICS
ISSN	2168-2267
	2019-09-01
卷号	49 期号:9 页码:3420-3431
通讯作者	Wen, Guoxing(gxwen@live.cn)
摘要	In this paper, a tracking control approach for surface vessel is developed based on the new control technique named optimized backstepping (OB), which considers optimization as a backstepping design principle. Since surface vessel systems are modeled by second-order dynamic in strict feedback form, backstepping is an ideal technique for finishing the tracking task. In the backstepping control of surface vessel, the virtual and actual controls are designed to be the optimized solutions of corresponding subsystems, therefore the overall control is optimized. In general, optimization control is designed based on the solution of Hamilton-Jacobi-Bellman equation. However, solving the equation is very difficult or even impossible due to the inherent nonlinearity and complexity. In order to overcome the difficulty, the reinforcement learning (RL) strategy of actor-critic architecture is usually considered, of which the critic and actor are utilized for evaluating the control performance and executing the control behavior, respectively. By employing the actor-critic RL algorithm for both virtual and actual controls of the vessel, it is proven that the desired optimizing and tracking performances can be arrived. Simulation results further demonstrate effectiveness of the proposed surface vessel control.
关键词	Actor-critic architecture Lyapunov stability optimized backstepping (OB) reinforcement learning (RL) surface vessel
DOI	10.1109/TCYB.2018.2844177
关键词[WOS]	NONLINEAR-SYSTEMS ; ROBUST-CONTROL ; STABILIZATION
收录类别	SCI
语种	英语
资助项目	Shandong Provincial Natural Science Foundation, China[ZR2018MF015] ; Binzhou University[2016Y14] ; National Natural Science Foundation of China[61572540] ; National Natural Science Foundation of China[61603094] ; National Natural Science Foundation of China[61703050] ; Macau Science and Technology Development Fund[019/2015/A] ; Macau Science and Technology Development Fund[024/2015/AMJ] ; Macau Science and Technology Development Fund[079/2017/A2] ; University Macau MYR Grant ; Shandong Provincial Natural Science Foundation, China[ZR2018MF015] ; Binzhou University[2016Y14] ; National Natural Science Foundation of China[61572540] ; National Natural Science Foundation of China[61603094] ; National Natural Science Foundation of China[61703050] ; Macau Science and Technology Development Fund[019/2015/A] ; Macau Science and Technology Development Fund[024/2015/AMJ] ; Macau Science and Technology Development Fund[079/2017/A2] ; University Macau MYR Grant
项目资助者	Shandong Provincial Natural Science Foundation, China ; Binzhou University ; National Natural Science Foundation of China ; Macau Science and Technology Development Fund ; University Macau MYR Grant
WOS研究方向	Automation & Control Systems ; Computer Science
WOS类目	Automation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号	WOS:000470988800017
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：134[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/27849
专题	离退休人员
通讯作者	Wen, Guoxing
作者单位	1.Binzhou Univ, Dept Math, Binzhou 256600, Peoples R China 2.Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117576, Singapore 3.Natl Univ Singapore, Social Robot Lab, Interact Digital Media Inst, Singapore 117576, Singapore 4.Univ Macau, Fac Sci & Technol, Dept Comp & Informat Sci, Macau 99999, Peoples R China 5.Dalian Maritime Univ, Nav Coll, Dalian 116026, Peoples R China 6.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100080, Peoples R China 7.Binzhou Univ, Sch Econ & Management, Binzhou 256600, Peoples R China
推荐引用方式 GB/T 7714	Wen, Guoxing,Ge, Shuzhi Sam,Chen, C. L. Philip,et al. Adaptive Tracking Control of Surface Vessel Using Optimized Backstepping Technique[J]. IEEE TRANSACTIONS ON CYBERNETICS,2019,49(9):3420-3431.
APA	Wen, Guoxing,Ge, Shuzhi Sam,Chen, C. L. Philip,Tu, Fangwen,&Wang, Shengnan.(2019).Adaptive Tracking Control of Surface Vessel Using Optimized Backstepping Technique.IEEE TRANSACTIONS ON CYBERNETICS,49(9),3420-3431.
MLA	Wen, Guoxing,et al."Adaptive Tracking Control of Surface Vessel Using Optimized Backstepping Technique".IEEE TRANSACTIONS ON CYBERNETICS 49.9(2019):3420-3431.