Multidisciplinary Modeling and Optimization Method of Remote Sensing Satellite Parameters Based on SysML-CEA

Abstract

To enhance the efficiency of system modeling and optimization in the conceptual design stage of satellite parameters, a system modeling and optimization method based on System Modeling Language and Co-evolutionary Algorithm is proposed. At first, the objectives of satellite mission and optimization problems are clarified, and a design matrix of discipline structure is constructed to process the coupling relationship of design variables and constraints of the orbit, payload, power and quality disciplines. In order to solve the problem of increasing nonlinearity and coupling between these disciplines while using a standard collaborative optimization algorithm, an improved genetic algorithm is proposed and applied to system-level and discipline-level models. Finally, the CO model of satellite parameters is solved through the collaborative simulation of Cameo Systems Modeler (CSM) and MATLAB. The result obtained shows that the method proposed in this paper for the conceptual design phase of satellite parameters is efficient and feasible. It can shorten the project cycle effectively and additionally provide a reference for the optimal design of other complex projects.

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“…Among them, the CO algorithm proposed by Kroo et al [24,25] is considered classic. The CO algorithm is very adaptable and deformable, which can be rapidly deployed and applied to engineering fields, such as aerospace [24,26], vehicle [27,28], engine [29], satellite [30], etc., and many new algorithms have been expanded based on the CO method, such as the introduction of uncertainty and robust into the CO method [31,32], and the combination of multi-objective optimization methods with CO [33][34][35]. However, the original CO model, setting the consistency constraint to zero at the system level, leads to convergence issues in some cases.…”