Reinforcement Dynamic Learning-Based Tracking Control Strategy for an Unknown 2-DOF Helicopter System

Abstract

This study investigates a multitrajectory tracking control strategy for an unknown 2-DOF helicopter system, integrating deterministic learning (DL) and reinforcement learning (RL). Initially, DL theory is applied to identify the local unknown dynamics of a 2-DOF helicopter system using radial basis function neural networks (RBFNNs). Subsequently, the identified dynamic knowledge is expressed and stored using constant RBFNNs. To mitigate the issue of partial knowledge failure due to deviations between the actual and learned trajectories, we introduce a RL framework for dynamic compensation. Finally, a composite control strategy incorporating both nominal and auxiliary components is designed to achieve multitrajectory tracking control. The stability of the closed-loop system is analyzed and demonstrated using the Lyapunov direct method. The simulation and experimental results demonstrate the effectiveness of the proposed control strategy.