Learning Control Of Compensative Trunk Motion For Biped Walking Robot Based On ZMP Stability Criterion

Abstract

The authors have been using the ZMP (Zero Mo- ment Point) as a criterion to distinguish the stability of walk- ing for a biped walking robot that has a trunk. In this paper, the authors propose a learning control algorithm of the com- pensative trunk motion that makes the actual ZMP get closer to the desired ZMP. The convergency of the algorithm is con- firmed by computer simulation and learning experiments with the biped robot. The change of the convergence rate with the change of the weight coefficient multiplied to the errors be- tween the measured ZMP and the desired ZMP is confirmed by the simulation and the experiments. And also the reasons are discussed.