This paper focuses on specific issues relative to realtime on-line estimation of aircrafl aerodynamic parameters at nominal and post-actuator failure flight conditions. A specific parameter identification (PID) method, based on Fourier Transform has been applied to an approximated mathematical model of the NASA IFCS F-15 aircraft. In

This paper describes the results of the extension of an existing failure modeling approach for the longitudinal aircraft dynamics to the lateral-directional dynamics. In particular a general formulation is developed for the most common failure scenarios, that is actuator blockage with and without a missing portion of the control surface. The approach consists of modeling the contribution of each individual control device within the expressions for the total external forces and moments using a single "efficiency" parameter that is easily accessible during simulation. Although the methodology can be applied to any force and moment generating control surface the paper describes simulation results relative to failures for elevators/stabilators, ailerons, and rudder occurring separately and simultaneously.

This paper describes a simulation tool developed at West Virginia University (WVU) for on-line aircraft parameter identification (PID) within a specific fault tolerant flight control scheme for the NASA IFCS F-15 program. The design of intelligent controllers capable of handling various types of control system failures is a challenging task that requires extensive simulation support. The simulation package developed by WVU researchers is modular and flexible so that different methods and/or approaches can be used for each of the tasks of the general fault tolerant control system, such as aircraft model, controller, parameter identification method, and on-line data storage. Numerous simulation options are directly available to the user through specific graphical user interface. These options allow to select among different control loop configurations, different versions of the parameter identification method, and different failure scenarios.

No abstract

This paper describes the results of a study focused on enhancing the performance of a non linear dynamic inversion scheme augmented with a neural network to cancell the dynamic inversion error. The approach is based on adding a pre-trained neural network providing the values of the aerodynamic stability and control derivatives required by the dynamic inversion calculations, as the aircraft moves throughout its flight envelope. Additionally, a comparison is performed using two different classes of neural networks (Sigma-Pi and EMRAN algorithms) for the cancellation of the dynamic inversion errors. The study is performed using the WVU IFCS F-15 simulation environment. The results show that the updating of the aerodynamic derivatives reduces the error compensating activity of the neural network. Performance improvements in terms of tracking error are observed for some maneuvers; however, a significant sensitivity to the update rate has been noticed.