Fuzzy learning variable admittance control for human-robot cooperation

Abstract

This paper presents a method for variable admittance control in human-robot cooperation tasks, that combines a human-like decision making process and an adaptation algorithm. A Fuzzy Inference System is designed that relies on the measured velocity and the force applied by the operator to modify on-line the damping of the robot admittance, based on expert knowledge for intuitive cooperation. A Fuzzy Model Reference Learning Controller is used to adapt the Fuzzy Inference System according to the minimum jerk trajectory model. To evaluate the performance of the proposed controller a point-to-point cooperation task is conducted with multiple subjects using a KUKA LWR robot.

A User Study on Personalized Stiffness Control and Task Specificity in Physical Human–Robot Interaction

“…This is a personalized mode where a linear heuristic is used to adapt the stiffness of the robot online. A similar approach was used in Dimeas and Aspragathos (2014) where a heuristic was used to vary the impedance parameters based on the change in velocity of the robot. In our case, we keep the damping at a constant value and vary the stiffness based on the instantane ous interaction force.…”

“…Various schemes based on variable admittance or impedance control have been proposed to improve the interaction quality, where the user interaction is mapped into robot stiffness, hence trying to reduce the effort in pHRI. Dimeas and Aspragathos (2014) implemented a variable admittance controller that is based on a Fuzzy infer ence system and an adaptation algorithm to vary the admittance parameters. Here, the Fuzzy inference system relies on the meas ured velocity and the human force and proposes suitable control ler gains.…”

Design and Implementation of Admittance Control for a Dual-Arm Robot under Space Limitation

“…Their method can create a safe and compliant robot controller. Impedance and admittance control algorithms have been studied for human-robot cooperation task [7][8][9][10]. Dimeas et al [10] proposed a method of virtual Cartesian constraints to prevent the operator from guiding the manipulator to low-performance configurations.…”

Fuzzy Pi Adaptive Learning Controller for Controlling the Angle of Attack of an Aircraft

“…S Yordanova and E Haralanova [4] designed and implemented a robust multivariable PI fuzzy controller for an aerodynamic system. F. Dimeas and N. Aspragathos [6] proposed a Fuzzy Learning Variable Admittance Control for a Human Robot system. I.S Baruch and S. Hernandez [7] discussed about a decentralised direct I-term Fuzzy-neural controller for controlling an anaerobic digestion bioprocess system.…”