mehran mirshams; Mohammad Teshneh lab; Morteza Ramezani
Volume 11, Issue 3 , December 2018, , Pages 1-8
Abstract
Modeling and analyzing systems, especially in complex systems with high dynamics, noise and uncertainty in understanding the behavior of systems and decision making is very important problem from long time ago. This paper shows that neuro-fuzzy systems can be used effectively to design the solar arrays ...
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Modeling and analyzing systems, especially in complex systems with high dynamics, noise and uncertainty in understanding the behavior of systems and decision making is very important problem from long time ago. This paper shows that neuro-fuzzy systems can be used effectively to design the solar arrays of electrical power subsystem of a remote sensing satellite in conceptual design phase. In the design of neuro-fuzzy system, Takagi-Sugeno inference system, hybrid training algorithm and Gaussian membership functions are used. The simulation results obtained in this modeling have an accurate accuracy compared to the experimental data and classical calculations of remote sensing satellites.
A. Jafarsalehi; M. Mirshams; R. Emami
Volume 7, Issue 1 , April 2014, , Pages 1-12
Abstract
This paper focuses upon the development of an efficient method for conceptual design optimization of a satellite. There are many option for a satellite subsystems that could be choice, as acceptable solution to implement of a space system mission. Every option should be assessment based on the different ...
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This paper focuses upon the development of an efficient method for conceptual design optimization of a satellite. There are many option for a satellite subsystems that could be choice, as acceptable solution to implement of a space system mission. Every option should be assessment based on the different criteria such as cost, mass, reliability and technology contraint (complexity). In this research, mass and technology constraints, which have a direct impact on the satellite life cycle cost, are considerd as system level objective function to obtain the system optimal solution during the coceptual design phase. The approach adopted in this paper is based on a distributed collaborative optimization (CO) framework. At system level, multiobjective optimization goal is to minimize the dry mass of the satellite and, simultaneously, minimize the system technology complexity which is subject to equality constraints. The use of equality constraints at the system level in CO to represent the disciplinary feasible regions, introduces numerical and computational difficulties as the discipline level optima are non-smooth and noisy functions of the system level optimization parameters.To address these difficulties robust optimization algorithms such as genetic algorithms (GA) are used at the system level. The results show that the CO framework has the same level of accuracy as the conventional All-At-Once approaches.
