Regular Papers

International Journal of Control, Automation and Systems 2021; 19(1): 548-561

Published online August 5, 2020

https://doi.org/10.1007/s12555-019-1056-7

© The International Journal of Control, Automation, and Systems

A Generalized Vision-based Stiffness Controller for Robot Manipulators with Bounded Inputs

Carlos Vidrios-Serrano, Marco Mendoza*, Isela Bonilla, and Berenice Maldonado-Fregoso

Autonomous University of San Luis Potosi

Abstract

Generally, stiffness and impedance control schemes require knowledge of the location of any object with which a robot interacts within its workspace; therefore, the integration of a computer vision system within the control loop allows us to know the location of the robot end effector and the object (target) simultaneously. In this paper, a generalized and saturating vision-based stiffness controller with adaptive gravity compensation is presented. The proposed control algorithm is designed to regulate robot-environment interaction in task-space, where the contact force is modeled as a vector of generalized bounded spring-like forces. In order to control nonredundant robots, the proposed controller has a nonlinear proportional-derivative structure with static model-based compensation of gravitational forces, as it includes a regressor-based adaptive term. To support the proposal, the Lyapunov stability analysis of the closed-loop equilibrium vector is presented. Finally, the suitable performance of the proposed scheme was verified by numerical simulations and experimental tests.

Keywords Adaptive control, bounded inputs, robot manipulator, stiffness, stability, vision.

Article

Regular Papers

International Journal of Control, Automation and Systems 2021; 19(1): 548-561

Published online January 1, 2021 https://doi.org/10.1007/s12555-019-1056-7

Copyright © The International Journal of Control, Automation, and Systems.

A Generalized Vision-based Stiffness Controller for Robot Manipulators with Bounded Inputs

Carlos Vidrios-Serrano, Marco Mendoza*, Isela Bonilla, and Berenice Maldonado-Fregoso

Autonomous University of San Luis Potosi

Abstract

Generally, stiffness and impedance control schemes require knowledge of the location of any object with which a robot interacts within its workspace; therefore, the integration of a computer vision system within the control loop allows us to know the location of the robot end effector and the object (target) simultaneously. In this paper, a generalized and saturating vision-based stiffness controller with adaptive gravity compensation is presented. The proposed control algorithm is designed to regulate robot-environment interaction in task-space, where the contact force is modeled as a vector of generalized bounded spring-like forces. In order to control nonredundant robots, the proposed controller has a nonlinear proportional-derivative structure with static model-based compensation of gravitational forces, as it includes a regressor-based adaptive term. To support the proposal, the Lyapunov stability analysis of the closed-loop equilibrium vector is presented. Finally, the suitable performance of the proposed scheme was verified by numerical simulations and experimental tests.

Keywords: Adaptive control, bounded inputs, robot manipulator, stiffness, stability, vision.

IJCAS
May 2024

Vol. 22, No. 5, pp. 1461~1759

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