<![CDATA[This study presents the design and implementation of a predictive model control (MPC) framework tailored for articulated robot arms, enabling precise local motion control during complex manipulations and interdepartmental transport tasks. The research begins with a systematic formulation of the robot arm’s kinematics and dynamics, establishing mathematical models that serve as the foundation for advanced control strategies. A five-degree-of-freedom robotic arm, composed of motor and arm components, is employed as a case study to demonstrate the modeling process and validate the MPC design. Unlike conventional control approaches, the proposed framework emphasizes adaptability and robustness, offering improved trajectory tracking and disturbance rejection. Simulation results highlight the effectiveness of MPC in achieving smoother motion and reduced error margins, with quantitative indicators such as tracking accuracy and computational efficiency underscoring its performance advantages. While the outcomes confirm the feasibility of the approach, limitations related to hardware scalability and real-time implementation are acknowledged. Future work will focus on extending the framework to collaborative robotic systems, integrating sensor fusion for enhanced perception, and exploring deployment in industrial environments. Overall, this research contributes a structured methodology for predictive control in robotics, bridging theoretical modeling with practical application..]]>
Copyrights © 2026
