A study of model predictive control and reinforcement learning control system

Parking in a parking lot is a challenging task for most drivers since it requires precise operations within limited space and visibility constraints. Automatic parking technology utilizes advanced sensors, robotics, and artificial intelligence to offer a solution to enhance driver convenience. Various studies have explored different control methods for autonomous parking systems. This paper focuses on investigating the advantages of control systems based on Model Predictive Control (MPC) and Reinforcement Learning (RL) Control. Meanwhile, the paper simultaneously explores the operational mechanisms of MPC and RL Control methods during the parking process, like the equations they used to modify the feedback. Additionally, the control system was trained by using the Proximal Policy Optimization (PPO) algorithm. After three rounds of training, a notable improvement in the parking success rate is observed. The paper also explores the optimization possibilities of the PPO algorithm by modifying the reward function. The results indicate the need for a larger sample size to draw conclusive findings.