Advanced Control Strategies for Securing UAV Systems: A Cyber-Physical Approach

This paper explores the application of sliding mode control (SMC) as a robust security enhancement strategy for unmanned aerial vehicle (UAV) systems. The study proposes integrating advanced SMC techniques with security protocols to develop a dual-purpose system that improves UAV control and fortifies against adversarial actions. The strategy includes dynamic reconfiguration capabilities within the SMC framework, allowing adaptive responses to threats by adjusting control laws and operational parameters. This is complemented by anomaly detection algorithms that monitor deviations in control signals and system states, providing early warnings of potential cyber-intrusions or physical tampering. Additionally, fault-tolerant SMC mechanisms are designed to maintain control and system stability even when parts of the UAV are compromised. The methodology involves simulation and real-world testing to validate the effectiveness of the SMC-based security enhancements. Simulations assess how the UAV handles attack scenarios, such as GPS spoofing and control signal jamming, with SMC adapting in real-time to mitigate these threats. Field tests further confirm the system’s capability to operate under varied conditions, proving the feasibility of SMC for enhancing UAV security. This integration of sliding mode control into UAV security protocols leverages control theory for security purposes, offering a significant advancement in the robust, adaptive control of UAVs in hostile environments.

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Institutions

Publication Details

Journal
Applied System Innovation
Published
2024-09-06
DOI
https://doi.org/10.3390/asi7050083
Citations
2
Primary Topic
UAV Applications and Optimization
Type
article
Field-Weighted Citation Impact
2.60

Funders

Controls
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article

Advanced Control Strategies for Securing UAV Systems: A Cyber-Physical Approach

Daniela Gîfu, Pablo Flores Peña, Ahmed Refaat Ragab, Mohammad Sadeq Ale Isaac
2 citations
Applied System Innovation
UAV Applications and Optimization
2.60
article

Advanced Control Strategies for Securing UAV Systems: A Cyber-Physical Approach

Daniela Gîfu, Pablo Flores Peña, Ahmed Refaat Ragab, Mohammad Sadeq Ale Isaac
article en
2 citations

Abstract

This paper explores the application of sliding mode control (SMC) as a robust security enhancement strategy for unmanned aerial vehicle (UAV) systems. The study proposes integrating advanced SMC techniques with security protocols to develop a dual-purpose system that improves UAV control and fortifies against adversarial actions. The strategy includes dynamic reconfiguration capabilities within the SMC framework, allowing adaptive responses to threats by adjusting control laws and operational parameters. This is complemented by anomaly detection algorithms that monitor deviations in control signals and system states, providing early warnings of potential cyber-intrusions or physical tampering. Additionally, fault-tolerant SMC mechanisms are designed to maintain control and system stability even when parts of the UAV are compromised. The methodology involves simulation and real-world testing to validate the effectiveness of the SMC-based security enhancements. Simulations assess how the UAV handles attack scenarios, such as GPS spoofing and control signal jamming, with SMC adapting in real-time to mitigate these threats. Field tests further confirm the system’s capability to operate under varied conditions, proving the feasibility of SMC for enhancing UAV security. This integration of sliding mode control into UAV security protocols leverages control theory for security purposes, offering a significant advancement in the robust, adaptive control of UAVs in hostile environments.

Applied System InnovationVol. 7(5)
Centre for Automation and Robotics (ES), October 6 University (EG), Universidad Carlos III de Madrid (ES), Romanian Academy (RO), Universidad Politécnica de Madrid (ES)
Comunidad de Madrid, European Commission, European Regional Development Fund
Openalex Percentile: Top 8%
UAV Applications and Optimization
2.60
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