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A survey on CPG-inspired control models and system implementation
Yu, Junzhi 1; Tan, Min 1; Chen, Jian 2; Zhang, Jianwei 3; Cheng, Long
Source PublicationIEEE Transactions on Neural Networks and Learning Systems
AbstractThis 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.
KeywordRobotic Applications Bioinspired Control Central Pattern Generator (Cpg) Neural Network Parameter Tuning
Document Type期刊论文
Corresponding AuthorCheng, Long
Affiliation1. State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2. Institute of Advanced Manufacturing Technology, Hefei Institutes of Physical Sciences, Chinese Academy of Sciences, Changzhou 213000, China
3. Department of Informatics, University of Hamburg, Hamburg 22527, Germany
Recommended Citation
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 ,&Cheng, Long.(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.
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