Online Reinforcement Learning is a possible solution for adaptive nonlinear flight control. In this research an Adaptive Critic Design (ACD) based on Dual Heuristic Dynamic Programming (DHP) is developed and implemented on a simulated Cessna Citation 550 aircraft. Using an online identified system model approximation, the method is independent of prior model knowledge. The agent consists of two Artificial Neural Networks (ANNs) which form the Adaptive Critic Design and is supplemented with a Recursive Least Squares (RLS) online model estimation. The implemented agent is demonstrated to learn a near optimal control policy for different operating points, which is capable of tracking pitch and roll rate while actively minimizing the sideslip angle in a faster than real-time simulation. Providing limited model knowledge is shown to increase the learning, performance and robustness of the controller.

As more and more people choose the airplane as a means of transportation, the aviation industry faces new challenges in order to fulfill the increasing demand, while decreasing the costs and emissions. In this report, the authors present a design of a narrow-body aircraft that is meant to provide a 30 % direct operating costs reduction, as well as a 20 % reduction in NOx and CO2 emissions while being ready for market introduction by 2030. Furthermore, the aircraft should provide a noise reduction of 10 % which translates to a reduction of 29 [EPNdB] and it should house 177 passengers.