Differentiable Predictive Control

Differentiable Predictive Control (DPC) leverages machine learning to approximate the computationally expensive solutions of traditional Model Predictive Control (MPC), aiming for faster, more efficient control strategies. Current research focuses on improving DPC's robustness and safety guarantees, often employing neural networks and techniques like control barrier functions or tube-based MPC architectures to handle uncertainties and constraints. This approach offers significant potential for real-world applications, such as robotics and large-scale traffic management, by enabling high-performance control in systems with limited computational resources or complex dynamics.