Machine Learning-Based Autonomous Flight Vehicle Trajectory Rectification System: A Comprehensive Framework for Real-Time Path Correction

Modern autonomous flight guidance systems face unprecedented challenges in maintaining accurate flight trajectories under diverse operational conditions including atmospheric turbulence, wind shear, aerodynamic uncertainties, and dynamic reference trajectory changes. Traditional proportional navigation (PN) and augmented proportional navigation (APN) methods, while effective, exhibit limitations in adaptability and robustness when confronted with nonlinear disturbances and rapidly changing environmental parameters. This paper presents a comprehensive machine learning-based autonomous flight vehicle trajectory rectification system that integrates deep reinforcement learning, neural network-based adaptive control, and ensemble prediction models to achieve superior trajectory correction performance. We propose a hybrid architecture combining Long Short-Term Memory (LSTM) networks for temporal trajectory prediction, Deep Q-Networks (DQN) for optimal control policy learning, and Physics-Informed Neural Networks (PINNs) to enforce fundamental flight dynamics constraints. Extensive simulation results demonstrate that our ML-based approach achieves 34.7% reduction in terminal tracking error, 42.3% improvement in trajectory smoothness, and 28.6% faster convergence compared to conventional guidance algorithms across 15 distinct flight scenarios. The system exhibits robust performance under sensor noise levels up to 15% and maintains real-time computational efficiency with inference latency below 8 milliseconds on embedded hardware platforms.