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This paper presents a novel sensing information forecasting algorithm based on time series analysis for the power assist walking legs (PAWL). The goal of this algorithm is to improve the dynamic response of the exoskeleton. The algorithm is built up with the autoregressive (AR) model, the recursive least square (RLS) method and the final prediction error (FPE) criterion. The method of RLS is utilized...
The power assist walking legs (PAWL) is an autonomous exoskeleton robot which is designed for assisting activities of daily life. In order to improve the dynamic response of the exoskeleton robot, a novel sensing information forecasting algorithm is proposed based on the time series analysis. The algorithm is built up with the autoregressive (AR) model, the recursive least square (RLS) method and...
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