<![CDATA[The industrial automation sector is rapidly evolving, with a growing need for advanced control strategies to enhance the efficiency and precision of robotic systems. Model Predictive Control (MPC) has emerged as a promising approach due to its ability to handle multivariable control problems and constraints effectively. However, its application in robotic automation remains underexplored. This research aims to implement Model Predictive Control in industrial robotic systems to improve performance, adaptability, and operational efficiency. The study focuses on evaluating the effectiveness of MPC in real-time robotic applications, specifically in tasks requiring high precision and dynamic response. A simulation-based approach was employed, using a robotic arm model as a testbed for implementing MPC. The control algorithm was designed to predict future states of the system based on current measurements and optimize control inputs accordingly. Performance metrics, including tracking error and response time, were evaluated under various operational scenarios. The implementation of MPC resulted in a significant reduction in tracking error and improved response times compared to traditional control methods. The robotic arm demonstrated enhanced adaptability to changes in the environment and task requirements, showcasing the robustness of the MPC approach. The findings indicate that Model Predictive Control is an effective strategy for enhancing the performance of robotic systems in industrial automation. The successful application of MPC not only improves operational efficiency but also provides a framework for future research into more complex robotic applications. This study contributes to the growing body of knowledge on advanced control methods in automation. ]]>
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