Gaussian Process Model Predictive Control

Gaussian Process Model Predictive Control (GP-MPC) integrates Gaussian processes (GPs) with model predictive control (MPC) to enhance control system performance, particularly in handling uncertainty and adapting to complex, dynamic environments. Current research focuses on improving GP-MPC's efficiency and robustness through techniques like sparse GP approximations, ensemble methods, and tailored optimization algorithms (e.g., sequential quadratic programming), often applied to autonomous vehicle control and robotics. This approach offers significant advantages in safety-critical applications by providing more accurate predictions with associated uncertainty estimates, leading to more reliable and efficient control strategies in scenarios involving human-robot interaction or unpredictable environments.