Advancing Robotic Control: Data-Driven Model Predictive Control for a 7-DOF Robotic Manipulator

Abstract

In this study, we applied deep learning to improve the control of a KUKA LBR4 7 Degrees of Freedom (DOF) robotic arm. We developed a dynamic model using a comprehensive dataset of joint angles and actuator torques obtained from pick-and-place operations. This model was incorporated into a Model Predictive Control (MPC) framework, enabling precise trajectory tracking without the need for traditional analytical dynamic models. By integrating specific constraints within the MPC, we ensured adherence to operational and safety standards. Experimental results demonstrate that deep learning models significantly enhance robotic control, achieving precise trajectory tracking. This approach not only surpasses traditional control methods in terms of accuracy and efficiency but also opens new avenues for research in robotics, showcasing the potential of deep learning models in predictive control techniques.