Force/Position Hybrid Control for a Hexa Robot Using Gradient Descent Iterative Learning Control Algorithm

• Published in: IEEE access Vol. 7; pp. 72329 - 72342
• Main Authors: Huynh, Ba-Phuc, Wu, Cheng-Wei, Kuo, Yong-Lin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Control algorithms; Control theory; Controllers; double-loop PID; Hexa robot; Hybrid control; Impedance; impedance control; iterative learning; Iterative methods; Kinematics; Machine learning; Manipulators; Mathematical models; Mechanical systems; Parallel robots; Parameter estimation; Parameter modification; Position control; Position errors; Proportional integral derivative; Robot control; Robot kinematics; Robots; Unknown environments
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2920020

This paper presents an approach of the force/position hybrid control for a hexa parallel robot to guarantee a safe and accurate interaction when touching the object surface. A double-loop PID controller is proposed to replace the common PID controller in the position control to eliminate position errors due to the dynamics coupling effect between the arms and the vibration of the mechanical system. An impedance control model is used to guarantee a safe and accurate interaction when touching the object surface. In addition, a gradient descent iterative learning control algorithm is used and modified to determine the optimal impedance parameters in unknown environments. A model of the robot is built in SimMechanics to simulate and estimate system parameters. After that, the experimental work was conducted on a real robot to verify the effectiveness and feasibility of the proposed method.