Achieving the high-precision control of cable-driven parallel robots (CDPRs) is complex because of their structural properties. In this paper, a quintessential redundant CDPR is designed as the research subject, and a continuous switching sliding mode controller based on workspace vision is implemented to enhance the accuracy and stability of trajectory tracking. In addition, a virtual prototype of the CDPR with uncertainties is created in the simulation analysis software ADAMS, and co-simulation is performed with the control system designed in Simulink to validate the effectiveness of the proposed control strategy. Furthermore, a CDPR platform is established for trajectory tracking experiments using the visual-based position feedback method. The trajectory tracking performance with the three control schemes is then evaluated. The experimental results show that the continuous switching sliding mode control algorithm can significantly decrease trajectory tracking errors and exhibit superior trajectory tracking performance compared to the other control strategies.

This paper presents a novel cable-driven parallel robot with the aim of rehabilitating/exercising young and disabled children in drawing and writing tasks. The pyramidal topology was identified as providing the required three active translational Degrees of Freedom with a redundant set of five cables in a compact portable shape. In addition, this robot was designed with new features, as it is inexpensive, easy to control, easy to move to any place at home or school as it is fitting a home desk or classroom table. In this paper, we present the main steps for designing the proposed cable-driven pyramidal parallel robot. Specific attention is addressed to the design of the structure and the end effector as well as to establishing proper simulation models. Several simulations are proposed by implementing both kinematic and dynamic models to demonstrate the feasibility of multiple writing/drawing tasks. A specific user interface is proposed allowing both continuous and intermittent trajectories. A sliding mode control is implemented to achieve suitable tracking accuracy on a desired path. Finally, an experimental validation is successfully carried out by considering multiple trajectories for demonstrating the engineering feasibility and effectiveness of the proposed design and simulation models.

Flexible cables in cable-driven parallel robots (CDPRs) are easy to be excited and vibrate. Cable vibration will react on the end-effector, causing attitude deviation of the end-effector. The main objective of this study is to accurately model axially moving flexible cables and characterize the dynamic behaviors of associated compliant CDPRs. Firstly, a model for transverse vibration of the axially moving length-variable cable is developed. On this basis, an original nonlinear dynamic model of the CDPRs able to capture the vibration of the cables and the dynamics of the end-effector is proposed. Secondly, the frequency–amplitude relationship of the CDPR is obtained. Moreover, the significance of the excitation effect caused by the axially moving length-variable cables is demonstrated, by comparing the results with and without excitation effect at different frequencies. It turns out that, as the oscillation frequency of the end-effector increases, the end-effector and cables exhibit the dynamics process from steady state to unstable large-amplitude vibration and finally to stable small-amplitude vibration. This indicates that the dynamics of the CDPR exhibit non-linear characteristics, due to the influence of flexible cables. Finally, the proposed dynamic model of compliant CDPRs is validated by experiments performed in the laboratory.

One of the major challenges in the science of maxillofacial radiology imaging is the various artifacts created in images taken by cone beam computed tomography (CBCT) imaging systems. Among these artifacts, motion artifact, which is created by the patient, has adverse effects on image quality. In this paper, according to the conditions and limitations of the CBCT imaging room, the goal is the design and development of a cable-driven parallel robot to create repeatable movements of a dry skull inside a CBCT scanner for studying motion artifacts and building up reference datasets with motion artifacts. The proposed robot allows a dry skull to execute motions, which were selected on the basis of clinical evidence, with 3-degrees of freedom during imaging in synchronous manner with the radiation beam. The kinematic model of the robot is presented to investigate and describe the correlation between the amount of motion and the pulse width applied to DC motors. This robot can be controlled by the user through a smartphone or laptop wirelessly via a Wi-Fi connection. Using wireless communication protects the user from harmful radiation during robot driving and functioning. The results show that the designed robot has a reproducibility above 95% in performing various movements.

In this paper, control of a suspended cable-driven parallel robot has been experimentally investigated based on the dynamic model of the robot for object tracking purpose. In order to improve the tracking ability of the robot, three control approaches, namely kinematic PID, dynamic PD, and a kinematic sliding mode control (SMC), have been implemented, both on the Simscape and on the robot constructed at the Human and Robot Interaction Laboratory. Neural network controller and dynamic SMC have been implemented on the Simscape model. Afterward, the effectiveness of each approach has been investigated by employing the root mean square error (RMSE) index. Simulation and experimental results reveal the ability of each controller for precise and smooth control. For precise and real-time object tracking, YOLOv5-s and YOLOv4-tiny model are trained. By comparing the obtained index values, the kinematic PID demonstrates the best performance with the maximum RMSE value of 0.018 compared to other methods.

This study proposes a method based on reinforcement learning (RL) for point-to-point and dynamic reference position tracking control of a planar cable-driven parallel robots, which is a multi-input multi-output system (MIMO). The method eliminates the use of a tension distribution algorithm in controlling the system’s dynamics and inherently optimizes the cable tensions based on the reward function during the learning process. The deep deterministic policy gradient algorithm is utilized for training the RL agents in point-to-point and dynamic reference tracking tasks. The performances of the two agents are tested on their specifically trained tasks. Moreover, we also implement the agent trained for point-to-point tasks on the dynamic reference tracking and vice versa. The performances of the RL agents are compared with a classical PD controller. The results show that RL can perform quite well without the requirement of designing different controllers for each task if the system’s dynamics is learned well.

This paper develops the conceptual design and error analysis of a cable-driven parallel robot (CDPR). The earlier error analysis of CDPRs generally regarded the cable around the pulley as a center point and neglected the radius of the pulleys. In this paper, the conceptual design of a CDPR with pulleys on its base platform is performed, and an error mapping model considering the influence of radius of the pulleys for the CDPR is established through kinematics analysis and a full matrix complete differential method. Monte Carlo simulation is adopted to deal with the sensitivity analysis, which can directly describe the contribution of each error component to the total orientation error of the CDPR by virtue of the error modeling. The results show that the sensitivity coefficients of pulleys’ geometric errors and geometric errors of the cables are relatively larger, which confirms that the cable length errors and pulleys’ geometric errors should be given higher priority in design and processing.

In this paper, a new mobile cable-driven parallel robot is proposed by mounting a spatial cable robot on a wheeled mobile robot. This system includes all the advantages of cable robots such as high ratio of payload to weight and good stiffness and accuracy while its deficiency of limited workspace is eliminated by the aid of its mobile chassis. The combined system covers a vast workspace area whereas it has negligible vibrations and cable sag due to using shorter cables. The dynamic equations are derived using Gibbs–Appell formulation considering viscoelasticity of the cables. Therefore, the more realistic viscoelastic cable model of the robot reveals the system flexibility effect and shows the requirements needed to control the end-effector in the conditions with cable elasticity. The viscoelastic system stability is investigated based on the input–output feedback linearization and using only the actuators feedback data. Feedback linearization controller is equipped by two additional controllers, that is, the optimal controller based on Linear Quadratic Regulator (LQR) method and finite horizon model predictive approach. They are used to control the system compromising between the control effort and error signals of the feedback linearized system. The applied control input to the robot plant is the voltage signal limited to a specified band. The validity of modeling and the designed controller efficiency are investigated using MATLAB simulation and its verification is accomplished by experimental tests conducted on the manufactured cable robot, ICaSbot.