Reinforcement Dynamic Learning-Based Tracking Control Strategy for an Unknown 2-DOF Helicopter System
研究了一种结合确定性学习和强化学习的控制策略，用于未知二自由度直升机系统的多轨迹跟踪，通过神经网络识别动态并用强化学习补偿偏差，仿真和实验验证了有效性。
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.