Training a model-free reinforcement learning controller for a 3-degree-of-freedom helicopter under multiple constraints

Abstract

The purpose of the article is to design data-driven attitude controllers for a 3-degree-of-freedom experimental helicopter under multiple constraints. Controllers were updated by utilizing the reinforcement learning technique. The 3-degree-of-freedom helicopter platform is an approximation to a practical helicopter attitude control system, which includes realistic features such as complicated dynamics, coupling and uncertainties. The method in this paper first describes the training environment, which consists of user-defined constraints and performance expectations by using a reward function module. Then, actor–critic-based controllers were designed for helicopter elevation and pitch axis. Next, the policy gradient method, which is an important branch of the reinforcement learning algorithms, is utilized to train the networks and optimize controllers. Finally, from experimental results acquired by the 3-degree-of-freedom helicopter platform, the advantages of the proposed method are illustrated by satisfying multiple control constraints.