5 papers
From Shadow to Light: Toward Safe and Efficient Policy Learning Across MPC, DeePC, RL, and LLM Agents
Amin Vahidi-Moghaddam, Sayed Pedram Haeri Boroujeni, Iman Jebellat +3
One of the main challenges in modern control applications, particularly in robot and vehicle motion control, is achieving accurate, fast, and safe movement. To address this, optima…
Safe Data-Driven Predictive Control
Amin Vahidi-Moghaddam, Kaian Chen, Kaixiang Zhang +3
In the realm of control systems, model predictive control (MPC) has exhibited remarkable potential; however, its reliance on accurate models and substantial computational resources…
Data-Enabled Neighboring Extremal: Case Study on Model-Free Trajectory Tracking for Robotic Arm
Amin Vahidi-Moghaddam, Keyi Zhu, Kaixiang Zhang +2
Data-enabled predictive control (DeePC) has recently emerged as a powerful data-driven approach for efficient system controls with constraints handling capabilities. It performs op…
Data-Enabled Predictive Control for Flexible Spacecraft
Huanqing Wang, Kaixiang Zhang, Amin Vahidi-Moghaddam +4
Spacecraft are vital to space exploration and are often equipped with lightweight, flexible appendages to meet strict weight constraints. These appendages pose significant challeng…
Online Reduced-Order Data-Enabled Predictive Control
Amin Vahidi-Moghaddam, Kaixiang Zhang, Xunyuan Yin +2
Data-enabled predictive control (DeePC) has garnered significant attention for its ability to achieve safe, data-driven optimal control without relying on explicit system models. T…