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A Survey on CPG-Inspired Control Models and System Implementation

文献类型：期刊论文

作者Yu, Junzhi1; Tan, Min1; Chen, Jian2; Zhang, Jianwei3
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2014-03-01
卷号25期号:3页码:441-456
关键词Bioinspired control central pattern generator (CPG) neural network parameter tuning robotic applications
英文摘要This paper surveys the developments of the last 20 years in the field of central pattern generator (CPG) inspired locomotion control, with particular emphasis on the fast emerging robotics-related applications. Functioning as a biological neural network, CPGs can be considered as a group of coupled neurons that generate rhythmic signals without sensory feedback; however, sensory feedback is needed to shape the CPG signals. The basic idea in engineering endeavors is to replicate this intrinsic, computationally efficient, distributed control mechanism for multiple articulated joints, or multi-DOF control cases. In terms of various abstraction levels, existing CPG control models and their extensions are reviewed with a focus on the relative advantages and disadvantages of the models, including ease of design and implementation. The main issues arising from design, optimization, and implementation of the CPG-based control as well as possible alternatives are further discussed, with an attempt to shed more light on locomotion control-oriented theories and applications. The design challenges and trends associated with the further advancement of this area are also summarized.
WOS标题词Science & Technology ; Technology
类目[WOS]Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
研究领域[WOS]Computer Science ; Engineering
关键词[WOS]CENTRAL PATTERN GENERATOR ; COUPLED NONLINEAR OSCILLATORS ; ADAPTIVE DYNAMIC WALKING ; LOCOMOTION CONTROL ; QUADRUPED ROBOT ; SPINAL-CORD ; NEURAL OSCILLATOR ; UNPREDICTABLE ENVIRONMENT ; TRAJECTORY GENERATION ; ONLINE OPTIMIZATION
收录类别SCI
语种英语
WOS记录号WOS:000331985500001
源URL[http://ir.ia.ac.cn/handle/173211/3480]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队
作者单位1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Hefei Inst Phys Sci, Inst Adv Mfg Technol, Changzhou 213000, Peoples R China
3.Univ Hamburg, Dept Informat, D-22527 Hamburg, Germany
推荐引用方式
GB/T 7714		 Yu, Junzhi,Tan, Min,Chen, Jian,et al. A Survey on CPG-Inspired Control Models and System Implementation[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2014,25(3):441-456.
APA Yu, Junzhi,Tan, Min,Chen, Jian,&Zhang, Jianwei.(2014).A Survey on CPG-Inspired Control Models and System Implementation.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,25(3),441-456.
MLA Yu, Junzhi,et al."A Survey on CPG-Inspired Control Models and System Implementation".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 25.3(2014):441-456.

入库方式： OAI收割

来源：自动化研究所

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