Stochastic and deterministic analysis of nonlinear missile engagement scenarios using 5-dof 6-dof and adjoint models

Download

index.pdf

Date

2015

Author

Sezer, Emrah

Metadata

Show full item record

Item Usage Stats

183
views

140
downloads

In this study, pseudo five degree of freedom and a linear time varying adjoint models are considered in terms of their fidelity for conceptual design phase of a missile. The models are developed and compared for two analysis types such as, deterministic and stochastic. For the first analysis, pseudo five degree of freedom and adjoint models, which are developed, are compared with fully nonlinear six degree of freedom model for various performance analyses that are essential for conceptual design phases. Adjoint model includes time varying phenomena as an improvement over time invariant utilization, which exists in the literature. In the pseudo five degree of freedom model, roll dynamics is discarded and transfer function is implemented to represent missile acceleration response. The model includes improvements such as, more accurate drag coefficient estimation by using three dimensional incidence angle predictions, and better lateral angular dynamics estimation by using flight path kinematic equations. In addition, state space structured adjoint model is constructed and states are populated by obtaining a nonlinear model to capture the effects of engagement nonlinearity. Therefore, a proper linear time varying model is constructed and validated by comparing with nonlinear model. Finally, an approach is explained for stochastic disturbances for adjoint analysis.

Subject Keywords

Guided missiles., Projectiles, Aerial., Stochastic analysis., Nonlinear theories.

URI

http://etd.lib.metu.edu.tr/upload/12619681/index.pdf
https://hdl.handle.net/11511/25380

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Development of a model updating technique for nonlinear systems Canbaloğlu, Güvenç; Özgüven, Hasan Nevzat; Ünver, Hakkı Özgür; Department of Mechanical Engineering (2015) In structural dynamics, obtaining an accurate numerical model is very crucial. However there are usually discrepancies between calculated dynamic behavior from numerical models and the ones obtained experimentally, and therefore it will be necessary to update the numerical models. In real life applications, structures usually have nonlinearity, and for nonlinear structures, in order to update the numerical model, firstly nonlinearity in the structure can be identified, and then updating procedure may be app...
Missile autopilot design by projective control theory Doruk, Reşat Özgür; Kocaoğlan, Erol; Department of Electrical and Electronics Engineering (2003) In this thesis, autopilots are developed for missiles with moderate dynamics and stationary targets. The aim is to use the designs in real applications. Since the real missile model is nonlinear, a linearization process is required to get use of systematic linear controller design techniques. In the scope of this thesis, the linear quadratic full state feedback approach is applied for developing missile autopilots. However, the limitations of measurement systems on the missiles restrict the availability of ...
Structured neural networks for modeling and identification of nonlinear mechanical systems Kılıç, Ergin; Dölen, Melik; Koku, Ahmet Buğra; Department of Mechanical Engineering (2012) Most engineering systems are highly nonlinear in nature and thus one could not develop efficient mathematical models for these systems. Artificial neural networks, which are used in estimation, filtering, identification and control in technical literature, are considered as universal modeling and functional approximation tools. Unfortunately, developing a well trained monolithic type neural network (with many free parameters/weights) is known to be a daunting task since the process of loading a specific pat...
Comparison of iterative algorithms for parameter estimation in nonlinear regression Musluoğlu, Gamze; Akkaya, Ayşen; Department of Statistics (2018) Nonlinear regression models are more common as compared to linear ones for real life cases e.g. climatology, biology, earthquake engineering, economics etc. However, nonlinear regression models are much more complex to fit and to interpret. Classical parameter estimation methods such as least squares and maximum likelihood can also be adopted to fit the model in nonlinear regression as well, but explicit solutions can not be achieved unlike linear models. At this point, iterative algorithms are utilized to ...
Digital controller design for sampled-data nonlinear systems Üstüntürk, Ahmet; Kocaoğlan, Erol; Department of Electrical and Electronics Engineering (2012) In this thesis, digital controller design methods for sampled-data nonlinear systems are considered. Although sampled-data nonlinear control has attracted much attention in recent years, the controller design methods for sampled-data nonlinear systems are still limited. Therefore, a range of controller design methods for sampled-data nonlinear systems are developed such as backstepping, adaptive and robust backstepping, reduced-order observer-based output feedback controller design methods based on the Eule...

Citation Formats

E. Sezer, “Stochastic and deterministic analysis of nonlinear missile engagement scenarios using 5-dof 6-dof and adjoint models,” M.S. - Master of Science, Middle East Technical University, 2015.