<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>Force/position control strategies provide an effective framework to deal with tasks involving interaction with the environment. One of these strategies proposed in the literature is external force feedback loop control. It fully employs the available sensor measurements by operating the control action in a full dimensional space without using selection matrices. The performance of this control strategy is affected by uncertainties in both the robot dynamic model and environment stiffness. The purpose of this paper is to improve controller robustness by applying a neural network technique in order to compensate the effect of uncertainties in the robot model. We show that this control strategy is robust with respect to payload uncertainties, position and environment stiffness, and dry and viscous friction. Simulation results for a three degrees-of-freedom manipulator and various types of environments and trajectories show the effectiveness of the suggested approach compared with classical external force feedback loop structures.</p></abstract>

Force/position control strategies provide an effective framework to deal with tasks involving interaction with the environment. One of these strategies proposed in the literature is external force feedback loop control. It fully employs the available sensor measurements by operating the control action in a full dimensional space without using selection matrices. The performance of this control strategy is affected by uncertainties in both the robot dynamic model and environment stiffness. The purpose of this paper is to improve controller robustness by applying a neural network technique in order to compensate the effect of uncertainties in the robot model. We show that this control strategy is robust with respect to payload uncertainties, position and environment stiffness, and dry and viscous friction. Simulation results for a three degrees-of-freedom manipulator and various types of environments and trajectories show the effectiveness of the suggested approach compared with classical external force feedback loop structures.

Dynamic External Force Feedback Loop Control of a Robot Manipulator Using a Neural Compensator—Application to the Trajectory Following in an Unknown Environment

Faculty of Electronics and Computer Science, USTHB University, BP 32, El-Alia, 16111, Bab-Ezzouar, Algiers, Algeria

International Journal of Applied Mathematics and Computer Science

Force/position control strategies provide an effective framework to deal with tasks involving interaction with the environment. One of these strategies...

Dynamic External Force Feedback Loop Control of a Robot Manipulator Using a Neural Compensator—Application to the Trajectory Following in an Unknown Environment

Online veröffentlicht: 02. Apr. 2009

Seitenbereich: 113 - 126

DOI: https://doi.org/10.2478/v10006-009-0011-9

Schlüsselwörter
force/position control, external structure, neural control, robot manipulator

This content is open access.

Dynamic External Force Feedback Loop Control of a Robot Manipulator Using a Neural Compensator—Application to the Trajectory Following in an Unknown Environment

Online veröffentlicht: 02. Apr. 2009

Seitenbereich: 113 - 126

DOI: https://doi.org/10.2478/v10006-009-0011-9

Schlüsselwörterforce/position control, external structure, neural control, robot manipulator

This content is open access.

Schlüsselwörter
force/position control, external structure, neural control, robot manipulator