Simulation Training System for Parafoil Motion Controller Based on Actor–Critic RL Approach

Abstract

The unique ram air aerodynamic shape and control rope pulling course of the parafoil system make it difficult to realize its precise control. At present, the commonly used control methods of the parafoil system include proportional–integral–derivative (PID) control, model predictive control, and adaptive control. The control precision of PID control and model predictive control is low, while the adaptive control has the problems of complexity and high cost. This study proposes a new method to improve the control precision of the parafoil system by establishing a parafoil motion simulation training system that trains the neural network controllers based on actor–critic reinforcement learning (RL). Simulation results verify the feasibility of the proposed parafoil motion-control-simulation training system. Furthermore, the test results of the real flight experiment based on the motion controller trained by the proximal policy optimization (PPO) algorithm are presented, which are close to the simulation results.