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

الموضوعات :

Reza Tarighi ¹ (Department of Electrical Engineering, University of Applied Science and Technology Tehran, Centre of Poolad Peech Kar (P.P.K), Tehran, Iran)
M.H. Kazemi ² (Department of Electrical Engineering, Shahed University, Tehran, Iran)
mohammad hosein khalesi ³ (Department of Mechanical Engineering, Semnan University, Semnan, Iran)

تاريخ الإرسال : 10 الثلاثاء , جمادى الأولى, 1443 تاريخ التأكيد : 22 السبت , رمضان, 1443 تاريخ الإصدار : 05 الخميس , صفر, 1444

الکلمات المفتاحية: optimal path design, Particle swarm Algorithm, Optimization Algorithms, Predictive control, Genetic Algorithm,

ملخص المقالة :

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.

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