Reinforcement Learning-based Optimal Control and Software Rejuvenation for Safe and Efficient UAV Navigation

TL;DR

This paper introduces a Deep Reinforcement Learning-based method for optimizing control and software rejuvenation in UAVs, enhancing safety and performance during cyber-attack mitigation.

Contribution

It presents a novel Deep RL approach integrating software rejuvenation with UAV control, improving safety and efficiency over traditional methods.

Findings

Enhanced UAV tracking performance during software rejuvenation.

Improved safety constraints adherence compared to traditional methods.

Demonstrated effectiveness in UAV simulations.

Abstract

Unmanned autonomous vehicles (UAVs) rely on effective path planning and tracking control to accomplish complex tasks in various domains. Reinforcement Learning (RL) methods are becoming increasingly popular in control applications, as they can learn from data and deal with unmodelled dynamics. Cyber-physical systems (CPSs), such as UAVs, integrate sensing, network communication, control, and computation to solve challenging problems. In this context, Software Rejuvenation (SR) is a protection mechanism that refreshes the control software to mitigate cyber-attacks, but it can affect the tracking controller's performance due to discrepancies between the control software and the physical system state. Traditional approaches to mitigate this effect are conservative, hindering the overall system performance. In this paper, we propose a novel approach that incorporates Deep Reinforcement Learning (Deep RL) into SR to design a safe and high-performing tracking controller. Our approach optimizes safety and performance, and we demonstrate its effectiveness during UAV simulations. We compare our approach with traditional methods and show that it improves the system's performance while maintaining safety constraints.