A study of advanced mathematical modeling and adaptive control strategies for trajectory tracking in the Mitsubishi RV-2AJ 5-DOF Robotic Arm

Abstract

This study is focused on the advanced mathematical modeling and control strategies for a Mitsubishi RV-2AJ 5-DOF Robotic Arm, with an emphasis on achieving precision in trajectory tracking. Robotic trajectory tracking has long been challenged by the constraints of conventional modeling techniques and control methods. Prior contributions have been limited by the use of traditional dynamic models and classical controllers, which often failed to adapt to complex trajectory requirements. To address these challenges, a comprehensive dynamic model has been developed using Euler–Lagrange equations and validated through a comparison with a 3D CAD Simscape model in MATLAB/Simulink. For the control approach, a PID controller and an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller have been implemented to regulate the robotic arm’s joints. The ANFIS controller has been demonstrated to outperform the PID controller, achieving improved stability and adaptability in trajectory tracking. The robustness of the proposed system has been confirmed through simulation results for various trajectory types, including N-shaped, circular, and complex paths. This study underscores the importance of advanced modeling and hybrid control strategies in overcoming the limitations of traditional methods. By establishing a solid foundation in precise trajectory tracking through advanced mathematical modeling, this work contributes to the development of more adaptive and efficient robotic systems, with potential applications in industrial and research domains.