Impedance Force Control of Manipulator Based on Variable Universe Fuzzy Control

Author:

Kong Dexin1,Huang Qingjiu2

Affiliation:

1. School of Information and Electronic Engineering, Zhejiang Gongshang University, Hangzhou 310018, China

2. Control System Laboratory, Graduate School of Engineering, Kogakuin University, Tokyo 163-8677, Japan

Abstract

Impedance control is a classic and straightforward control method that finds wide applications in various fields. However, traditional constant impedance control requires prior knowledge of the environment’s stiffness and position information. If the environmental information is unknown, constant impedance control is not capable of handling the task. To address this, this paper proposes a variable universe fuzzy model reference adaptive impedance control method that achieves effective force tracking even in the presence of unknown environmental information. A variable universe fuzzy controller was employed to determine the impedance parameters. The force tracking error and its rate of change were used as two input parameters for the variable universe fuzzy controller, which utilizes fuzzy inference to obtain the incremental values of the impedance parameters. For the introduced model reference controller, a novel adaptive law was employed to obtain the coefficients for contact force and torque. Subsequently, the contact force of the manipulator in Cartesian space was taken as the research object, and a simulation model of a six-joint manipulator was established in MATLAB/Simulink. By comparing it with the constant impedance control method, the feasibility and effectiveness of this control approach were validated.

Publisher

MDPI AG

Subject

Control and Optimization,Control and Systems Engineering

Reference28 articles.

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4. Adaptive neural network-based finite-time impedance control of constrained robotic manipulators with disturbance observer;Li;IEEE Trans. Circuits Syst. II Express Briefs,2021

5. Adaptive neural impedance control for electrically driven robotic systems based on a neuro-adaptive observer;Peng;Nonlinear Dyn.,2020

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