Vibration Control of Dual Wheel Nose Landing Gear: Applications of AI ML

Abstract

The Vibration management in dual-wheel nose landing-gear systems remains a major challenge in the aerospace industry, particularly due to the presence of nonlinear torsional dynamics and shimmy effects that affect safety and comfort during takeoff and landing. This study presents a comprehensive review of recent advances in prognostics, deep learning and adaptive fuzzy logic control systems for improved real-time system responsiveness and structural safety. This study borrows nonlinear modeling techniques such as multibody simulations, finite element analysis and torsional nonlinear energy sink (NES) mechanisms, exploring hybrid artificial intelligence-regulated approaches for shimmy performance evaluation. Advanced digital twin technologies, magnetorheological dampers and fiber Bragg grating (FBG) strain sensors enable adaptive control and real-time diagnostic capabilities, hence intelligent acoustic performance predictions and landing impact loads analysis. Employing deep reinforcement learning, model predictive control and grey signal prediction methodologies, this study emphasizes the application of AI and machine learning in ensuring system stability in varied terrain and flight dynamics. The application of explainable artificial intelligence (XAI) ensures that failure diagnosis is traceable, while Lyapunov-based fuzzy control models ensure robust stabilization in uncertain environments. The application of machine learning algorithms in certification towards safe-life evaluations is emphasized, in addition to semi active control systems that offer flexibility to the airframe. Employing data-driven modeling and extensive monitoring, this paper presents the optimized design of nose landing-gear assemblies, with emphasis on predictive analytics, structural integrity and improved ride quality.