A nanosatellite task scheduling framework to improve mission value using fuzzy constraints

Abstract

Task scheduling is an effective approach to increase the value of a satellite mission, which leads to improved resource management and quality of service. This work improves the energy prediction model and a task scheduling formulation, expressed in integer programming to maximize the number of tasks performed in nanosatellite missions. A realistic battery model is introduced in the formulation to extend battery lifetime. This is achieved by a disjunctive program that makes battery charge and discharge more efficient. Furthermore, fuzzy constraints are designed to limit the current rates (for charge and discharge) and the depth of discharge for battery lifetime preservation. Each battery access is penalized in the objective function, thereby stimulating energy consumption to match energy input. For simulation purposes, the varying power input was based on two-line element data of the CubeSat FloripaSat-I, operating in an orbit with J2 perturbation and an attitude that keeps one face of the nanosatellite towards the Earth for the entire orbit, similar to a remote sensing mission. The effectiveness of the task-scheduling methodology was shown by means of simulated experiments of representative scenarios. With the improvements proposed here, a robust and realistic framework for optimal offline scheduling of nanosatellite missions is achieved.