Stochastic MPC

Stochastic Model Predictive Control (SMPC) optimizes control strategies for systems with inherent uncertainties, aiming to achieve desired objectives while satisfying constraints probabilistically. Current research emphasizes efficient algorithms for handling multi-modal predictions and high-dimensional state spaces, employing techniques like neural networks (e.g., recurrent and attention-based architectures), normalizing flows, and Gaussian processes for modeling stochastic dynamics and uncertainty. These advancements are crucial for real-time applications in diverse fields, including autonomous driving, robotics, and energy systems, enabling safer and more robust control in complex, uncertain environments.