Application of Deep Reinforcement Learning in Reconfiguration Control of Aircraft Anti-Skid Braking System

Abstract

The aircraft anti-skid braking system (AABS) plays an important role in aircraft taking off, taxiing, and safe landing. In addition to the disturbances from the complex runway environment, potential component faults, such as actuators faults, can also reduce the safety and reliability of AABS. To meet the increasing performance requirements of AABS under fault and disturbance conditions, a novel reconfiguration controller based on linear active disturbance rejection control combined with deep reinforcement learning was proposed in this paper. The proposed controller treated component faults, external perturbations, and measurement noise as the total disturbances. The twin delayed deep deterministic policy gradient algorithm (TD3) was introduced to realize the parameter self-adjustments of both the extended state observer and the state error feedback law. The action space, state space, reward function, and network structure for the algorithm training were properly designed, so that the total disturbances could be estimated and compensated for more accurately. The simulation results validated the environmental adaptability and robustness of the proposed reconfiguration controller.