Aircraft Neural Network Training With Simulated Offset Control Inputs

The patent uses a flight simulator to create virtual copies of flight scenarios instead of relying on actual pilot data collection. The simulator generates synthetic training data that mimics real flight conditions, eliminating the need to physically collect data from human pilots while maintaining training effectiveness.

The system employs self-training through reinforcement learning where the neural network trains itself by interacting with the flight simulator environment. The aircraft autonomously learns from simulated flight experiences without requiring external data collection from human pilots, making the training process self-sufficient and efficient.

The flight simulator creates virtual replicas of flight scenarios, replacing expensive real-world pilot data collection. This copying approach maintains data quality while dramatically reducing the costs associated with hiring pilots, aircraft operations, and physical flight testing.

The reinforcement learning system performs self-training using synthetic data generated by the simulator, eliminating the need for expensive external data collection processes. The system generates its own training data through autonomous interaction with the simulated environment, reducing overall training costs.

The flight simulator dynamically generates diverse flight scenarios including rare and edge cases that would be unlikely to occur in normal pilot operations. The system can adaptively create and adjust scenarios during training, allowing the neural network to learn from a broader range of conditions beyond typical pilot experience.

The system performs preliminary training on a wide variety of simulated scenarios before deployment, including potential future scenarios that haven't been encountered in actual flight history. This advance training on diverse virtual scenarios prepares the aircraft for situations that may arise in the future.