Position Control of a Fully Constrained Planar Cable-Driven Parallel Robot With Unknown or Partially Known Dynamics

Sancak, Caner; İtik, Mehmet; Nguyen, Thang

doi:10.1109/tmech.2022.3228444

Cited by 14 publications

(1 citation statement)

References 30 publications

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“…Moreover, closed-loop control methods can converge the errors caused by uncertainty. Various control methods are used in CDPRs, including PID [17, 18], robust control [19], reinforcement learning [20], neural networks [21], and adaptive control [22]. Sliding mode control (SMC) is an effective method for controlling nonlinear and uncertain systems and has considerable robustness to external disturbances and uncertain dynamics modelling [23].…”

Section: Introductionmentioning

confidence: 99%

Research on workspace visual-based continuous switching sliding mode control for cable-driven parallel robots

Qian,

Zhao,

Qian

et al. 2023

Robotica

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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.

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Section: Introductionmentioning

confidence: 99%

Research on workspace visual-based continuous switching sliding mode control for cable-driven parallel robots

Qian,

Zhao,

Qian

et al. 2023

Robotica

View full text Add to dashboard Cite

show abstract

Disturbance observer‐based control for cable‐driven parallel robots with external disturbance using deep reinforcement learning

Lu,

Yao,

et al. 2024

Asian Journal of Control

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In cable‐driven parallel robots (CDPRs) practical applications, external disturbances inevitably influence the actuator performance, leading to low positioning accuracy of end‐effector (EE), and even system crashes. This paper addresses a disturbance observer‐based control scheme using deep reinforcement learning (RL) to suppress the effect of external disturbances. A controller via the non‐singular terminal sliding mode (NTSM) is proposed to enhance the system robustness. The estimation of the disturbance is investigated, and the deep RL is employed to enhance the model‐based disturbance observer, namely, the RLDO, and a corresponding controller via NTSM (RLDO‐NTSMC) is proposed. Simulations and experimental results validate that the RLDO effectively improves the estimation accuracy, and the RLDO‐NTSMC significantly increases the control accuracy of CDPRs. The root mean square errors of the EE's positioning errors controlled by the RLDO‐NTSMC decreased by over 93% compared with the classical augmented proportional–derivative (APD) controller.

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Interval type-2 fuzzy sliding mode control for a cable-driven parallel robot with elastic cables using metaheuristic optimization methods

Aghaseyedabdollah,

Abedi,

Pourgholi

2024

Mathematics and Computers in Simulation

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Position Control of a Fully Constrained Planar Cable-Driven Parallel Robot With Unknown or Partially Known Dynamics

Cited by 14 publications

References 30 publications

Research on workspace visual-based continuous switching sliding mode control for cable-driven parallel robots

Research on workspace visual-based continuous switching sliding mode control for cable-driven parallel robots

Disturbance observer‐based control for cable‐driven parallel robots with external disturbance using deep reinforcement learning

Interval type-2 fuzzy sliding mode control for a cable-driven parallel robot with elastic cables using metaheuristic optimization methods

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