A neural-fuzzy walking control of an autonomous biped robot

Abstract

In this paper, an adaptive neural-fuzzy walking control of an autonomous biped robot is proposed. This control system uses a feed forward neural network based on nonlinear regression. The general regression neural network is used to construct the base of an adaptive neuro-fuzzy system. The neural network uses an iterative grid partition method for the initial structure identification of the controller parameters. Comparison results are done between the proposed method and the ANFIS tool provided in the fuzzy MATLAB toolbox. The robot's control system uses an inverted pendulum to balance of the gaits. The effectiveness of the proposed control system is demonstrated by simulation and experimental tests