Minimum Cost Perturbed Multi-impulsive Maneuver Methodology to Accomplish an Optimal Deployment Scheduling for a Satellite Constellation

The optimal design of a satellite constellation is a highly constrained, multidisciplinary problem. The satellite constellation requires a flexible optimal deployment algorithm that can consider constraints and perturbations. The mission is cost-effective if there is no need to redevelop the deployment algorithm by changing the number of satellites. Therefore, the main purpose of this paper is to develop an optimal flexible algorithm to deploy m identical satellites to the desired satellite constellation. For this purpose, the Intelligent Optimal Satellite Constellation Deployment (IOSCD) algorithm is proposed. This algorithm's intelligence comes from identifying the feasible scenarios and, regarding the mission requirements such as time synchronization and collision avoidance constraints, is performed via an evolutionary optimization algorithm. The optimization algorithm plays two coupled roles in the deployment mission scheduling: selecting the best feasible deployment scenario and time planning for simulations maneuvers. Developing a proper maneuver is also performed in this paper. For this purpose, a proposed methodology called Multi-Impulsive maneuver combined with Lambert Targeting Problem Correction (MILTPC) is introduced. The LTPC is established in the last maneuver to make the transfer orbit tangential to the final orbit and consider orbital perturbations to reduce fuel consumption. The optimization algorithm is applied to IOSCD and MILTPC simultaneously to achieve the best scenario in terms of fuel consumption. The four meta-heuristic optimization algorithms Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Invasive Weed Optimization (IWO), and the hybrid IWO/PSO are examined once only on MILTPC and once on constellation deployment missions to select the best one for the present investigation.