One of the most important factors that bring success in modern warfare is to show air superiority. Unmanned aerial vehicles (UAVs) have now become an essential component of military air operations. UAVs can be operated in two ways: by pilots from remote control stations or by flying autonomously. Under the condition of disconnection from the control station, UAVs have trouble maintaining navigation and maneuverability. By applying multisensor data fusion, an escape path prediction algorithm was developed and presented as an engagement escape method in this study. To develop the algorithm for prediction of the optimal escape route, data from various sensors are collected and processed under the influence of noise. The data from the distance and angle sensors are interpreted in the Extended Kalman Filter and estimations are made. The instant optimal escape route is created by applying the constrained optimization method on the estimations made. The main motivation of this study is developing a deterministic-based method to get the certification of it in aviation. Therefore, instead of stochastic-based learning approaches, a deterministic approach is preferred. Nonlinear programming is used as the constraint optimization method because the constraints and objective function are nonlinear. In the selected scenarios, it can be seen in the simulation results that the proposed method shows a promising result in terms of escape from engagement.

Saccharamyces cerevisia known as baker’s yeast is a product used in various food industries. Worldwide economic competition makes it a necessity that industrial processes be operated in optimum conditions, thus maximisation of biomass in production of saccharamyces cerevisia in fedbatch reactors has gained importance. The facts that the dynamic fermentation model must be considered as a constraint in the optimisation problem, and dynamics involved are complicated, make optimisation of fed-batch processes more difficult. In this work, the amount of biomass in the production of baker’s yeast in fed-batch fermenters was intended to be maximised while minimising unwanted alcohol formation, by regulating substrate and air feed rates. This multiobjective problem has been tackled earlier only from the point of view of finding optimum substrate rate, but no account of air feed rate profiles has been provided. Control vector parameterisation approach was applied the original dynamic optimisation problem which was converted into a NLP problem. Then SQP was used for solving the dynamic optimisation problem. The results demonstrate that optimum substrate and air feeding profiles can be obtained by the proposed optimisation algorithm to achieve the two conflicting goals of maximising biomass and minimising alcohol formation.