Time delay prediction for space telerobot system with a modified sparse multivariate linear regression method

Compared with general telerobot system, space telerobot system (STS) faces with more serious and complex time delay. In time delay prediction, the traditional sparse multivariate linear regressive (SMLR), auto regressive (AR), neural network (NN) and cubic polynomial model based (CBMB) approaches share lower efficiency or precision, when time delay is serious and complex. To solve this problem, time delay prediction for STS is discussed in this paper. Through analysis, time delay in STS can be divided to three parts: a) communication time delay; b) transmission time delay; c) processing time delay. Then, detailed varying rule and accurate statistics analysis are given for each parts of time delay. Based on the previous work, a modified SMLR is proposed and realizes the prediction of time delay. Compared with previous SMLR, AR, NN and CBMB approaches, the modified SMLR method shares more higher prediction efficiency or precision. Finally, a simulation example is given and the simulation results show the superiorities.