@article{Feng2024, 
author = {Zhaohan Feng and Jie Chen and Wei Xiao and Jian Sun and Bin Xin and Gang Wang},
title = {Learning Hybrid Policies for MPC with Application to Drone Flight in Unknown Dynamic Environments},
year = {2024},
journal = {Unmanned Systems},
volume = {12},
number = {2},
pages = {429-441},
keywords = {Model predictive control, reinforcement learning, trajectory planning, unmanned aerial vehicle},
url = {https://www.sciopen.com/article/10.1142/S2301385024410206},
doi = {10.1142/S2301385024410206},
abstract = {In recent years, drones have found increased applications in a wide array of real-world tasks. Model predictive control (MPC) has emerged as a practical method for drone flight control, owing to its robustness against modeling errors/uncertainties and external disturbances. However, MPC’s sensitivity to manually tuned parameters can lead to rapid performance degradation when faced with unknown environmental dynamics. This paper addresses the challenge of controlling a drone as it traverses a swinging gate characterized by unknown dynamics. This paper introduces a parameterized MPC approach named hyMPC that leverages high-level decision variables to adapt to uncertain environmental conditions. To derive these decision variables, a novel policy search framework aimed at training a high-level Gaussian policy is presented. Subsequently, we harness the power of neural network policies, trained on data gathered through the repeated execution of the Gaussian policy, to provide real-time decision variables. The effectiveness of hyMPC is validated through numerical simulations, achieving a 100% success rate in 20 drone flight tests traversing a swinging gate, demonstrating its capability to achieve safe and precise flight with limited prior knowledge of environmental dynamics.}
}