Slope Gait Control Method for Biped Robot Based on Adaptive Neural Network

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Zhiwei Wang et al.


In order to solve the problem of slow convergence and poor real-time control of current biped robot slope gait control methods, an adaptive neural network based slope gait control method for biped robot is proposed. Using D-H representation, the kinematics model of biped robot is established, and the adaptive neural network technology is used to import the kinematics model of biped robot to predict the gait of robot locomotion. By using the judging factor, the lag adjustment mechanism of the rhythm generating layer is determined, the phase gait is corrected, and the Van der is used. Pol vibrator is used to alleviate the phase mutation after the phase reset, and the phase mutation in the phase correction is dispersed in the subsequent motion vibration to realize the slope gait control of the biped robot. The experimental results show that the gait control method designed in this paper has good real-time performance and can effectively speed up the convergence speed.

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