Reinforcement Learning for Dual-Control Aircraft Six-Degree-of-Freedom Attitude Control with System Uncertainty

Abstract

This article proposes a near-optimal control strategy based on reinforcement learning, which is applied to the six-degree-of-freedom (6-DoF) attitude control of dual-control aircraft. In order to solve the problem that the existing reinforcement learning is difficult to apply to the high-dimensional multiple-input multiple-output (MIMO) systems, the Long Short-Term Memory (LSTM) neural network is introduced to replace the polynomial network in the adaptive dynamic programming (ADP) technique. Meanwhile, based on the Lyapunov method, a novel online adaptive updating law of LSTM neural network weights is given, and the stability of the system is verified. In the simulation process, the algorithm proposed in this article is applied to the six-degree-of-freedom attitude control problem of dual-control aircraft with system uncertainty. The simulation results show that the algorithm can achieve near-optimal control.