Robust Optimal Trajectory Design of a Launch Vehicle Using Particle Swarm Optimization

In this paper, a robust optimization method is developed to solve the Satellite Launch Vehicle (SLV) trajectory design problem in the presence of uncertainties using a powerful Particle Swarm Optimization (PSO) algorithm. Given the uncertainties such as uncertainties in the actual values ​​of aerodynamic coefficients, engine thrust, and mass in the ascent phase of a SLV, it is important to achieve an optimal trajectory that is robust to these uncertainties; because it improves the flight performance, reduces the workload of the guidance-control system, and increases the reliability of the satellite. For this purpose, first the optimization problem is considered by using the criterion of minimizing the flight time of the SLV as a cost function, and three-dimensional equations of motion as constraints governing the problem. Then, by adding the mean parameters and the standard deviation of uncertainties in the cost function, a robust optimizer model is developed and the algorithm is used to numerically optimize the model. Monte Carlo's perspective has also been used to analyze the results of uncertainties and their continuous feedback to the optimization model. Finally, the optimal trajectory is obtained that is robust to the uncertainties. The resulting simulation results show the accuracy of this claim.