did not actually provide any control adjustments in flight. The outputs of the neural network were run directly to instrumentation for data collection purposes only. Generation 2 Generation 2 IFCS tests were conducted in 2005 and used a fully integrated neural network as described in the theory of operation. It is a direct adaptive system that continuously provides error corrections and then measures the effects of these corrections in order to learn new flight models or adjust existing ones. To measure the aircraft state, the neural network takes 31 inputs from the roll, pitch, and yaw axes and the control surfaces. If there is a difference between the aircraft state and model, the neural network adjusts the outputs of the primary flight computer through a dynamic inversion controller to bring the difference to zero before they are sent to the actuator control electronics which move the control surfaces. See also A different research and development project with the goal of designing an intelligent flight control system is being carried out at University College London. Their prototype is known as the Intelligent Autopilot System which has Artificial Neural Networks capable of learning from human teachers by imitation. The system is capable of handling severe weather conditions and flight emergencies such as engine failure or fire, emergency landing, and performing Rejected Take Off (RTO) in a flight simulator. Aircraft flight control system F-15 McDonnell Douglas F-15 STOL/MTD References Flight control systems