COLLABORATIVE OPTIMIZATION OF REMOTE SENSING SMALL SATELLITE MISSION USING GENETIC ALGORITHMS

This paper focuses on the development of an efficient distributed collaborative optimization method for the design of remote sensing small satellite mission in low earth orbit (LEO). The satellite mission requirement involves the duration in which the satellite is able to take images, send data to the ground station and the amount of information it can store. Conventionally, all at once methods are used in satellite mission analysis, however, design optimization of such systems are multidisciplinary task with multiple conflicting objectives such as cost, performance and reliability. The approach adopted in this paper is based on a distributed collaborative optimization (CO) framework. In this approach, the design optimization problem is divided into two levels; namely system and discipline levels. The discipline level optimization involves payload, power, mission and launch subsystems. The objective function of the system level is to minimize the resolution of the satellite imaging payload 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. As a result of these difficulties, derivative-based optimization techniques cannot be used for the system level optimization. To address these difficulties a robust optimization algorithm, genetic algorithms (GA), are used at the system level, whilst at the discipline level efficient gradient based techniques are utilized. The results show that distributed CO framework using GA has the same level of accuracy as with the conventional all at once approaches, while providing a potential approach for solving complex multidisciplinary design problems such as the design of satellite systems.