Adaptive dynamic programming–based fault-tolerant control of multi-UAV formation system

Abstract

With the increasing utilization of unmanned aerial vehicle (UAV) swarms in civilian and military field applications, it is crucial to eliminate the potential impacts of UAV faults on task completion and efficiency. To address this issue, the present paper investigates a fault-tolerant control scheme based on the adaptive dynamic programming (ADP) method and fault observer. The whole closed-loop system is composed of a position loop and an attitude loop. An ADP-based position-tracking controller is established with an improved cost function that can simultaneously represent actuator faults, control inputs, and tracking errors. In addition, a single-layer critic neural network is constructed to estimate the cost function and approximate control inputs. In the process of adjusting the weights of the neural network, a unique adjustment method combining policy gradient and prioritized experience replay is adopted to improve the data usage efficiency and speed up the weight convergence. Furthermore, a sliding mode–based attitude controller is utilized for the inner-loop attitude subsystem. The significant feature of this scheme is that it has the ability to self-learn and self-adapt. A Lyapunov stability analysis is performed to verify whether the signals are uniformly ultimately bounded. Finally, simulation experiments are conducted to demonstrate the effectiveness of the proposed scheme.