The application of neural networks combined with partial differentiation of the neural outputs is discussed in this chapter to estimate lateral-directional flight stability and control derivatives from flight data. Primary investigation is carried out with simulated data and results are found to be encouraging to apply with flight data.
In a fairly complex system like aircraft, modeling and parameter estimation plays a crucial role in determining its stability and control characteristics.
This chapter discusses the neural modeling of the flexible aircraft and how to extract the aerodynamic derivatives and structural mode shape parameters of flexible modes using the neural partial differentiation (NPD) method. The effects of flexibility on the flight dynamics of an aircraft have been shown to be quite significant, especially as the f...
This chapter focuses on the application of neural networks to the problem of aircraft aerodynamic modeling and parameter estimation. The neural modeling and neural partial differentiation (NPD) method, which are presented in Chap. 2, are directly applied here to estimate the longitudinal dynamics of an aircraft system. Since the model of an aircraf...
Neural partial differentiation (NPD) method is used for the parameter estimation. For this purpose initially, a neural model of multi-input and multi-output (MIMO) aircraft system is established. In addition to that, separate consideration of the longitudinal and lateral-directional dynamics simplifies the model considerably in terms of structure a...
The neural modeling of a dynamic system is presented in this chapter. The former literature reported that ordinary differential equations can be solved by an neural-network-based approach (Lagaris et al. 1998)
A probabilistic flutter analysis of geometrically coupled cantilever wing is carried out using first-order perturbation approach by considering bending and torsional rigidities as Gaussian random variables. The unsteadiness in the aerodynamic flow is modeled using Theodorsen’s thin airfoil theory. The probabilistic response of the wing is obtained ...
The SPLICE project is continuing NASAs efforts to develop precision landing GN&C technologies for future lander missions. One of those technologies is the next generation Hazard Detection (HD) System, which consists of a new HD Lidar and HD Algorithms. The HD System is a modular system that will be adapted to meet specific mission needs in the futu...
The Ejectable Data Record (EDR) subsystem was a unique development opportunity at NASA with challenges that necessitated innovation. EDR employed a skunkworks development approach in which we designed, built, and delivered 47 end items, not including ground support equipment. We used as many COTS components as possible, we looked for process effici...
The International Space Station (ISS) is a laboratory for scientific research, innovative technology development, and global education. The ISS provides a number of facilities and platforms for payload developers and investigators to conduct biological, microgravity, and Earth and space observation science, as well as for performing technology deve...
