Abstract
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.
Highlights
Zhongshan Institute, School of Computer Science, University of Electronic Science and Technology of China, School of Automation Engineering, University of Electronic Science and Technology of China, Abstract: 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
In [13], a modified walking pattern method was presented by utilizing allowable ZMP variation and both step length and walking period can be independently adjusted without any extra step
Hirabayashi et al [16] proposed a waistrotation-based yaw moment compensation algorithm, while a biped robot was modeled as a 3D inverted pendulum
Summary
Zhongshan Institute, School of Computer Science, University of Electronic Science and Technology of China, School of Automation Engineering, University of Electronic Science and Technology of China, Abstract: 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. How to maintain the zero-moment-point stability and counteract the effect of yaw moment simultaneously is 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. Caron et al [15] defined the pendular support area and presented a whole-body controller for locomotion across arbitrary multicontact stances Despite these contributions, the stability established in [12,13,14,15] depends on an assumption that the effect on stability caused by yaw moment can be ignored, which is a restrictive condition. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
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