Enhancing fault detection and performance for UAVs with digital twin systems in search and rescue missions

Abstract This study presents the development of a Digital Twin for the "Made in UU" Field-based Autonomous LiDAR Control for Obstacle Navigation (FALCON), enabling advanced control systems and robust fault detection. The Digital Twin integrates real-time flight data and fault scenarios to enhance UAV stability under challenging conditions. The FALCON was modelled using real-time flight data, with traditional control methods, including Proportional-Integral-Derivative (PID), Linear Quadratic Regulator (LQR), and Linear Quadratic Gaussian (LQG), combined with optimization techniques such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Mayfly Algorithm (MA) to tune state feedback gains. Simulations showed GA-based tuning outperformed manual tuning, PSO, and MA in improving UAV stability and fault recovery. For PID, manual tuning achieved the fastest pitch settling with a 73.8% improvement, while PSO-tuned PID delivered the quickest roll (52.8%) and yaw (47.2%) responses. The PSO-tuned LQG controller minimized settling times across all dynamics. Full State Feedback and PID controllers performed comparably, with GA achieving the best roll settling and both GA and PSO reaching 0.1s in yaw. Overall, LQR with GA tuning provided the most balanced performance. These findings highlight GA’s robustness in challenging conditions, significantly improving UAV safety and efficiency in search and rescue, environmental monitoring, and disaster response. FALCON UAV and its Digital Twin offer a low-cost, IoT-integrated platform with real-time fault detection and optimal control, paving the way for next-generation UAV systems. Future work involves integrating machine learning for dynamic fault detection and real-world deployments.