Stochastic Model Predictive

Stochastic Model Predictive Control (SMPC) optimizes control strategies for systems with inherent uncertainties, aiming to achieve desired objectives while satisfying constraints despite unpredictable disturbances. Current research emphasizes robust handling of chance constraints using techniques like Control Barrier Functions (CBFs) and data-driven approaches such as Gaussian Processes and reinforcement learning for online constraint tightening and disturbance estimation. These advancements are improving the safety and efficiency of SMPC in diverse applications, including autonomous driving, multi-agent coordination, and quadrotor control, by enabling more reliable and adaptable control in complex, uncertain environments.