Stochastic Nonlinear Model Predictive Control

Stochastic Nonlinear Model Predictive Control (SNMPC) aims to optimize control strategies for systems with inherent uncertainties and nonlinear dynamics, ensuring robust performance and constraint satisfaction. Current research emphasizes efficient computational methods, such as zero-order algorithms and techniques limiting uncertainty propagation horizons, to enable real-time implementation in high-dimensional systems like autonomous vehicles and robots. Furthermore, research focuses on developing methods that provide probabilistic guarantees of performance and handle complex, non-Gaussian uncertainty distributions, moving beyond traditional approximations. These advancements are crucial for deploying robust and reliable control in safety-critical applications.