Online Control for Biped Robot with Incremental Learning Mechanism

In this paper, we develop a new online walking controller for biped robots, which integrates a neural-network estimator and an incremental learning mechanism to improve the control performance in dynamic environment. With the aid of an iteration algorithm for updating, some newly incoming data can b...

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Bibliographic Details
Published inApplied sciences Vol. 11; no. 18; p. 8599
Main Authors Yang, Liang, Lai, Guanyu, Chen, Yong, Guo, Zhihui
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2021
Subjects
Algorithms
biped robot
Kinematics
Neural networks
neural networks incremental learning
Optimization
Robot control
Robots
Velocity
Walking
Work stations
Yaw
Yawing moments
zero-moment-point
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Summary:In this paper, we develop a new online walking controller for biped robots, which integrates a neural-network estimator and an incremental learning mechanism to improve the control performance in dynamic environment. With the aid of an iteration algorithm for updating, some newly incoming data can be used straightforwardly to update into the original well-trained model, in order to avoid a time-consuming retraining procedure. On the other hand, how to maintain the zero-moment-point stability and counteract the effect of yaw moment simultaneously is also a key technical problem to be addressed. To this end, an interval type-2 fuzzy weight identifier is newly developed, which assigns weight for each walking sample to deal with the imbalanced distribution problem of training data. The effectiveness of the proposed control scheme has been verified through a full-dynamics simulation and a practical robot experiment.
ISSN:2076-3417
2076-3417
DOI:10.3390/app11188599