## 1 Introduction
Experimental results demonstrate the system's effectiveness in reducing the operational interference and adapting to dynamic changes. [PERSON] (2018) highlights the transformative role of Industry 4.0 and smart manufacturing in modern production systems. By leveraging automation, artificial intelligence (AI), and data analysis, digital twins serve as virtual replicas of physical assets that synchronize real-time data with virtual environments. [PERSON] addresses synchronization in robotic assembly systems, employing YOLOv8 (Yolov8.1 model) for real-time tracking and deviation analysis. The results underscore how computer vision technologies improve synchronization accuracy, thereby optimizing the efficiency and reliability of manufacturing processes. Focusing on object manipulation, [PERSON] (2018) presents a method for robotic gripping in complex environments with multiple stacked objects--an essential yet challenging task for robots. [PERSON] develops a digital twin model designed to plan and execute multi-object grasping while enabling virtual-real interaction. The model integrates a serial robot information system with a perception method to establish logical relationships between virtual models of stacked objects. The system employs the ROS and Unity3D to monitor and visualize critical elements of the physical robot in real time. Experimental results reveal an 80% accuracy in detecting interactive relationships between objects, validating the model's reliability and effectiveness in complex stacking environments. The existing literature predominantly focuses on serial manipulators and their applications in manufacturing environments, with limited attention given to closed kinematic chain manipulators. This gap can be attributed to the inherent complexity involved in analyzing the kinematics of closed-chain systems. Addressing this limitation, the novelty of this article lies in the kinematic control proposal for a 2 RRR planar parallel manipulator through the implementation of a digital twin framework. The proposed system integrates a virtual model of the manipulator developed using ROS/Gazebo with a human-machine interface (HMI) programmed in MATLAB 2021(r). This configuration provides an intuitive design environment and enables accurate trajectory simulation while accounting for the physical and electronic constraints of the real system. The virtual model plays a key role in extracting inverse kinematics and generating joint data vectors required for real-time trajectory execution. Additionally, the MATLAB-based interface facilitates seamless communication between the virtual model and the physical prototype. This integration allows for the monitoring of system variables in real time, trajectory execution, and comparative statistical analysis between simulated data and the data obtained from the physical system during execution. In the physical environment, the manipulator is equipped with an Arduino MEGA 2560 microcontroller (Arduino, Turin, Italy) and Dynamixel MX-64 actuators (Robotis, California, EEUU), ensuring precise control and effective validation of the proposed digital twin model. By successfully combining these components, this study presents a novel and practical solution for the kinematic control of closed kinematic chain manipulators, an area that has remained underexplored in the digital twin domain. The proposed system demonstrates its potential for trajectory planning, real-time execution, and enhanced system monitoring, contributing to the advancement of digital twin applications in robotics.