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 the final prediction error (FPE) criterion. The method of RLS is used to make the on-line parameters estimation, and the FPE criterion is used to select the order of AR model. Because of the real-time requirement, the forecasting algorithm is designed to be used on-line and to make predictions of force sensor's information to ensure the real-time quality of the whole system. According to requirements, the algorithm can be categorized into two types: one step forecasting method and multi-step forecasting method. Meanwhile, we make some correlative simulations and experiments, and the experiments demonstrate the sensing information forecasting algorithm can predict the value and the trend of the sensing signal, the results of simulations and experiments illustrate the validity and effectiveness of the algorithm.

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 to make the on-line parameters estimation, and the FPE criterion is used to select the order of AR model. The forecasting algorithm is designed to be used on-line and to make predictions of force sensing information to ensure the real-time quality of the whole system . The algorithm can be categorized into two types : one step forecasting method and multi-step forecasting method. Meanwhile, we make some simulations to verify the validity of this algorithm. At the end of this paper, the correlative experiments have been carried out, and the results demonstrate this sensing information forecasting algorithm can predict the value and the trend of the sensing signal precisely and effectively.