Tuning a PD Controller Based on an SVR for the Control of a Biped Robot Subject to External Forces and Slope Variation. (4th March 2014)

• Record Type: Journal Article
• Authors: Ferreira, João P.; Crisóstomo, Manuel; Coimbra, A. Paulo
• Is Part Of: International journal of advanced robotic systems. Volume 11:Number 3(2014)
• Publication Date: 2014-03-04
• Subjects: Tuning -- PD -- SVR -- Biped Robot -- Balance -- ZMP; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.5772/57526
• Languages: English
• ISSNs: 1729-8806

Abstract

Real-time balance control of an eight-link biped robot using a zero moment point (ZMP) dynamic model is difficult to achieve due to the processing time of the corresponding equations. To overcome this limitation an intelligent computing technique based on Support Vector Regression (SVR) is developed and presented in this paper. To implement a PD controller the SVR uses the ZMP error relative to a reference and its variation as inputs, and the output is the correction of the angle of the robot's torso, necessary for its sagittal balance. The SVR was trained based on simulation data generated using a PD controller. The initial values of the parameters of the PD controller were obtained by the second Ziegler-Nichols method. In order to evaluate the balance performance of the biped robot, three performance indexes are used. The ZMP is calculated by reading four force sensors placed under each of the robot's feet. The gait implemented in this biped is similar to a human gait, which is acquired and adapted to the robot's size. The main contribution of this paper is the fine-tuning of the ZMP controller based on the SVR. To implement and test this, the biped robot was subjected to external forces and slope variation. Some experiments are presented and the results show that the implemented gait combined with the correct tuning of the SVR controller is appropriate for use with this biped robot. The SVR controller runs at 0.2 ms, which is about 50 times faster than a corresponding first-order TSK neural-fuzzy network.