Learn-to-Recover: Retrofitting UAVs with Reinforcement Learning-Assisted Flight Control Under Cyber-Physical Attacks
Fan Fei, Zhan Tu, Dongyan Xu, Xinyan Deng
Abstract
In this paper, we present a generic fault-tolerant control (FTC) strategy via reinforcement learning (RL). We demonstrate the effectiveness of this method on quadcopter unmanned aerial vehicles (UAVs). The fault-tolerant control policy is trained to handle actuator and sensor fault/attack. Unlike traditional FTC, this policy does not require fault detection and diagnosis (FDD) nor tailoring the controller for specific attack scenarios. Instead, the policy is running simultaneously alongside the stabilizing controller without the need for on- detection activation. The effectiveness of the policy is compared with traditional active and passive FTC strategies against actuator and sensor faults. We compare their performance in position control tasks via simulation and experiments on quadcopters. The result shows that the strategy can effectively tolerate different types of attacks/faults and maintain the vehicle's position, outperforming the other two methods.