ArticleSensors (Basel, Switzerland)2019
Indirect Force Control of a Cable-Driven Parallel Robot: Tension Estimation using Artificial Neural Network trained by Force Sensor Measurements.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 35 citations in OpenAlex.
- A Two-Stage Grip Force Estimation Method for Surgical Robots Based on BiLSTM-iTransformer.Annals of the New York Academy of Sciences · 2026Article
- Kinematic Modeling and Solutions for Cable-Driven Parallel Robots Considering Adaptive Pulley Kinematics.Sensors (Basel, Switzerland) · 2025Article
- A Tension Sensor Array for Cable-Driven Surgical Robots.Sensors (Basel, Switzerland) · 2024Article
- New Sensor Device to Accurately Measure Cable Tension in Cable-Driven Parallel Robots.Sensors (Basel, Switzerland) · 2021Article
- Neural Network Based Contact Force Control Algorithm for Walking Robots.Sensors (Basel, Switzerland) · 2021Article
- Robotic Localization Based on Planar Cable Robot and Hall Sensor Array Applied to Magnetic Capsule Endoscope.Sensors (Basel, Switzerland) · 2020Article
- Universal Adaptive Neural Network Predictive Algorithm for Remotely Piloted Unmanned Combat Aerial Vehicle in Wireless Sensor Network.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
In a cable-driven parallel robot (CDPR), force sensors are utilized at each winch motor to measure the cable tension in order to obtain the force distribution at the robot end-effector. However, because of the effects of friction in the pulleys and the unmodeled cable properties of the robot, the measured cable tensions are often inaccurate, which causes force-control difficulties. To overcome this issue, this paper presents an artificial neural network (ANN)-based indirect end-effector force-estimation method, and its application to CDPR force control. The pulley friction and other unmodeled effects are considered as black-box uncertainties, and the tension at the end-effector is estimated by compensating for these uncertainties using an ANN that is developed using the training datasets from CDPR experiments. The estimated cable tensions at the end-effector are used to design a P-controller to track the desired force. The performance of the proposed ANN model is verified through comparisons with the forces measured directly at the end-effector. Furthermore, cable force control is implemented based on the compensated tensions to evaluate the performance of the CDPR in wrench space. The experimental results show that the proposed friction-compensation method is suitable for application in CDPRs to control the cable force.
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Registered trials
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