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ISSN (Print): 2212-7976
ISSN (Online): 1874-477X

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Research Article

Research on PID Neural Network Decoupling Control Among Joints of Hydraulic Quadruped Robot

Author(s): Bingwei Gao, Yongtai Ye and Guihua Han*

Volume 12, Issue 4, 2019

Page: [367 - 377] Pages: 11

DOI: 10.2174/2212797612666190819161320

Price: $65

Abstract

Background: Hydraulic quadruped robot is a representative of the redundant transmission. This is a great challenge for multi-joints coordinated movement of the robot, because of the movement coupling with several freedom degrees among kinematic chains. Therefore, there is an urgent need to realize the decoupling among the joints of the hydraulic quadruped robot.

Objective: The purpose of this study is to provide an overview of controller design from many studies and patents, and propose a novel controller to realize the decoupling control among joints of a hydraulic quadruped robot.

Methods: For the coupling problems between the thigh and calf of a hydraulic quadruped robot, based on the Lagrangian method, dynamics model of the robot’s leg is established. The influence of driven system is considered. The model of the hydraulic servo driven system is built, so as to obtain the coupling relationship between thigh and calf of hydraulic quadruped robot. Based on the multivariable decoupling theory, a PID neural network decoupling controller is designed.

Results: The researches on experiments are executed. The PID neural network decoupling control method is compared with the control that does not use any decoupling method. The decoupling effect of the proposed algorithm is verified on the thigh and the calf of the hydraulic quadruped robot.

Conclusion: The designed PID neural network decoupling control method reduces the crosscoupling between thigh and calf of the hydraulic quadruped robot, and has obvious effect to improve the dynamic characteristics of single joint of robot's leg.

Keywords: Cross-coupling, decoupling control, hydraulic quadruped robot, multi-joints coordinated movement, neural network, PID.

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