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.
Highlights
A cable-driven parallel robot (CDPR) is a special type of parallel robot, which is actuated by elastic cables instead of rigid links
The tension generated by the winch motor is transmitted to the end-effector through connected elastic cables guided by pulleys
We propose an artificial neural network (ANN)-based indirect end-effector force-estimation method, and design a proportional CDPR force-control algorithm that can compensate for the inaccurate measurements of the CDPR end-effector force and achieve indirect force control for CDPR
Summary
A cable-driven parallel robot (CDPR) is a special type of parallel robot, which is actuated by elastic cables instead of rigid links. The robot end-effector motion is controlled by the length and tension of each cable, and each cable is driven by each respective winch system. In addition to pulley friction, the cable elongation and its nonlinear properties caused tension discrepancies for the cable connection from each winch to the end-effector. The estimated cable tension of the end-effector compensates for this uncertainty by using an ANN model that is derived from the training datasets of the CDPR experiments. The effectiveness of the proposed ANN model is evaluated based on the cable force control achieved in the CDPR system. To the best of the authors’ knowledge, our study is the first ANN effector’s cable tension estimator for estimating the cable–pulley friction and compensating for cable application to use the cable force control of a CDPR by using an ANN-based CDPR end-effector’s cable uncertainties.
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