Bridging the Sim-to-real Gap: A Control Framework for Imitation Learning of Model Predictive Control

To address the computational challenges of Model Predictive Control (MPC), recent research has studied using imitation learning to approximate the MPC to a computationally efficient Deep Neural Network (DNN). However, this introduces a common issue in learning-based control, the simulation-to-reality (sim-to-real) gap, and Domain Randomization (DR) has been widely used to mitigate this gap by introducing perturbations in the source domain. However, DR inevitably leads to low data collection efficiency and an overly conservative control strategy. This study proposes a new control framework that addresses this issue from a control perspective inspired by Robust Tube MPC. The framework ensures the DNN operates in the same environment as the source domain, handling the sim-to-real gap with great data collection efficiency. Moreover, a parameter governor is introduced to address the DNN's inability to adapt to variations in model parameters, enabling the system to satisfy MPC constraints more robustly under changing conditions. The proposed framework was validated through two case studies: cart-pole control and vehicle collision avoidance control, which analyzed the principles of the proposed framework in detail and demonstrated its application to a vehicle control case.