In this paper, a new approach to the use of genetic algorithms and the predictive control method, for goal tracking is presented. A hypothetical rocket is modelled for the analyses. Rocket guidance algorithm is developed to achieve a desired mission goal according to some performance criteria and the imposed constraints. Given that goals can be fixed or moving, we have focused and expanded on this issue in this study and also the dynamic modelling of flying objects with six-degrees-of-freedom (DOF) is used to make the design more similar to the actual model. The predictive control method is used to predict the next step of rocket and aim movement. At each step of the problem, the rocket distance to the aim is obtained, and a trajectory is predicted to move the rocket towards the purpose. The objective function of this problem, in addition to the distance from the rocket position to the target, are also parameters of the dynamic model of the rocket. Therefore, these parameters are optimized at each step of the problem solving. Ultimately, the rocket strikes the intended aim by following this optimal path. Finally, for the validation of the model, numerical results are obtained for both Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). Simulation results demonstrate the effectiveness and feasibility of the proposed optimization technique.


    Access

    Download


    Export, share and cite



    Title :

    Optimal Routing of Rocket Motion using Genetic Algorithm and Particle Swarm Optimization


    Contributors:


    Publication date :

    2022




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Minimum-Fuel Optimal Trajectory for Reusable First-Stage Rocket Landing Using Particle Swarm Optimization

    Kevin Spencer G. Anglim / Zhenyu Zhang / Qingbin Gao | BASE | 2017

    Free access

    Particle Swarm Optimization Algorithm

    Chen, G.-c. / Yu, J.-s. | British Library Online Contents | 2005


    Solid Rocket Motor Performance Matching Using Pattern Search/Particle Swarm Optimization

    Albarado, Kevin / Hartfield, Roy / Hurston, B. et al. | AIAA | 2011


    AIR COMBAT WITH PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM

    Egemen Berki Çimen | DOAJ | 2014

    Free access

    A Comparison of Particle Swarm Optimization and the Genetic Algorithm

    Hassan, Rania / Cohanim, Babak / de Weck, Olivier et al. | AIAA | 2005