LAUSR

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.