Multidisciplinary Design Optimization of an Unmanned Air Vehicle and Final Solution Selection Based on Fuzzy Satisfaction Degree Function

In this paper, a comprehensive structure is expressed for multidisciplinary design optimization of the unmanned air vehicle and the design optimization of Predator MQ-1 has been studied for validation. The considered modules in the multidisciplinary analysis are performance, aerodynamic, weight, center of gravity, stability and control derivatives, trim and dynamic stability characteristics. The minimization of take-off weight and cruise drag are considered as objective functions. Since two objective functions are considered, therefore multi-objective genetic algorithm (with the non-dominated sorting concept) is used as optimizer which can generate set of optimal solutions as Pareto fronts. A method based on fuzzy logic named satisfaction degree function is expressed for final solution selection. The strengths of this paper are its comprehensiveness (the number of design modules, the number of design variables and constraints) and the presentation of a simple and efficient method for final solution selection. The optimization results show the effectiveness of the structured framework and suggested method.