Adaptive neural control of quadruped robots with input deadzone

Abstract

Abstract In this paper, neural network control are presented for a quadruped robot with input deadzone. The study is aiming at the compensation for input deadzone and the control of trajectory using radial basis function neural networks under both full state feedback and output feedback. Neural networks are utlized to estimate the unknown parameters in the quadruped robot model and compensate for input deadzone. The closed-loop stability is proved by Lyapunov’s stability theorem. The extensive simulations are conducted to show the validity of the proposed control.