Predictive Control

Model Predictive Control (MPC) is an advanced control technique that optimizes a system's trajectory over a future time horizon by iteratively solving optimization problems. Current research emphasizes improving MPC's efficiency and robustness, particularly through integrating machine learning methods like reinforcement learning and neural networks (e.g., using transformers for faster computation or neural networks to approximate MPC solutions), and addressing challenges posed by model uncertainties and safety constraints using techniques such as control barrier functions and Bayesian optimization. These advancements are significantly impacting various fields, including robotics, autonomous vehicles, and energy systems, by enabling more efficient, safe, and adaptable control of complex systems.