Robust Attitude Control of an Agile Aircraft Using Improved Q-Learning

Abstract

Attitude control of a novel regional truss-braced wing (TBW) aircraft with low stability characteristics is addressed in this paper using Reinforcement Learning (RL). In recent years, RL has been increasingly employed in challenging applications, particularly, autonomous flight control. However, a significant predicament confronting discrete RL algorithms is the dimension limitation of the state-action table and difficulties in defining the elements of the RL environment. To address these issues, in this paper, a detailed mathematical model of the mentioned aircraft is first developed to shape an RL environment. Subsequently, Q-learning, the most prevalent discrete RL algorithm, will be implemented in both the Markov Decision Process (MDP) and Partially Observable Markov Decision Process (POMDP) frameworks to control the longitudinal mode of the proposed aircraft. In order to eliminate residual fluctuations that are a consequence of discrete action selection, and simultaneously track variable pitch angles, a Fuzzy Action Assignment (FAA) method is proposed to generate continuous control commands using the trained optimal Q-table. Accordingly, it will be proved that by defining a comprehensive reward function based on dynamic behavior considerations, along with observing all crucial states (equivalent to satisfying the Markov Property), the air vehicle would be capable of tracking the desired attitude in the presence of different uncertain dynamics including measurement noises, atmospheric disturbances, actuator faults, and model uncertainties where the performance of the introduced control system surpasses a well-tuned Proportional–Integral–Derivative (PID) controller.

Full-Body Control of an Aerial Manipulator for Advance Physical Interaction Using Fuzzy Reinforcement Learning: A Road Map

“…There are other approaches to controlling a full-body aerial manipulator without decoupling or gainadjusting techniques. In [13] attitude control of a novel aircraft with low stability characteristics is addressed using Fuzzy Qlearning (FQL). Although the reward function definition in [13], and [12] are analogous, the robustness performance of the proposed FQL was outstanding against actuator faults, atmospheric disturbances, model parameter uncertainties, and sensor measurement errors in comparison with well-known PID and Dynamic Inversion methods.…”

“…In [13] attitude control of a novel aircraft with low stability characteristics is addressed using Fuzzy Qlearning (FQL). Although the reward function definition in [13], and [12] are analogous, the robustness performance of the proposed FQL was outstanding against actuator faults, atmospheric disturbances, model parameter uncertainties, and sensor measurement errors in comparison with well-known PID and Dynamic Inversion methods. Furthermore, the FQL eliminates the problem of computational resources that are needed for Deep Reinforcement Learning algorithms.…”