A Survey on Application of Data-driven Model Predictive Control in Robot Control

Robotics has rapidly advanced, revolutionizing manufacturing, healthcare, agriculture, and logistics industries. These advances have enabled robots to perform increasingly complex tasks with greater efficiency and adaptability. However, robotic control remains a major challenge due to robotic systems' complex dynamics, nonlinearities, and uncertainties. Traditional control methods often rely on accurate mathematical models, which are difficult to obtain for complex robots. Data-driven model predictive control (DD-MPC) is a promising solution that overcomes the limitations of traditional methods by leveraging data to learn system dynamics. Unlike model-free methods that lack safety guarantees or model-based methods that struggle with complexity, DD-MPC offers a balance between flexibility and performance. It facilitates real-time optimization, adeptly manages multifaceted constraints, and exhibits adaptability to spatiotemporal dynamic changes. This survey explores the application of DD-MPC in robotic control, highlighting its advantages over other control strategies and its potential to address current challenges in the field.