Advanced multi-loop control for 4DOF robotic arms: Integrating digital twins, neural networks, and model predictive control

Abstract

The abstract of this paper presents a novel approach to controlling a four-degree-of-freedom (4DOF) robotic arm by integrating Digital Twin technology, Neural Networks (NN), and Model Predictive Control (MPC). The primary contribution of this work lies in the development of an advanced multi-loop control system that optimizes robotic arm performance, enhancing control precision, energy efficiency, and reliability. The proposed method leverages a Digital Twin model for real-time monitoring and predictive maintenance, while Neural Networks enable adaptive tuning of control parameters. MPC further improves the system’s performance by predicting future states and optimizing control actions. Through extensive simulations conducted in MATLAB/Simulink, the results show significant improvements: overshoot reduced by 18.05 %, settling time decreased by 23.19 %, energy efficiency increased by 14.89 %, and tracking accuracy enhanced by 15.03 %. Additionally, the integration of predictive maintenance via the Digital Twin model led to a 36.42 % reduction in unplanned downtime, thereby increasing system reliability. These results demonstrate the superiority of the proposed method over traditional PI controllers, positioning it as a robust solution for modern robotics applications. • Integration of Digital Twins, Neural Networks, and MPC. • Enhanced Real-Time Performance . • Proactive Predictive Maintenance . • Improved Energy Efficiency . • Validated Performance Metrics .