Determining pre-mark angles on a projectile to hit an air target using neural network

Document Type : Original Article

Authors

1 Malik Ashtar University of Technology, Tehran, Iran.

2 Professor, Electrical Control Engineering, Malik Ashtar University of Technology, Tehran, Iran.

3 Associate Professor, Electrical Control Engineering, Malik Ashtar University of Technology, Tehran, Iran.

Abstract

The proper angle of launching air defense balls is very important in the probability of the projectile hitting the air target. If the projectile hits the air target, defense costs are saved and damage is not allowed by the enemy. In other words, the ability to hit the target is mainly The reason for cost reduction is essential for modern air cannon systems. The purpose of determining the pre-aiming angles of a projectile is the initial orientation of a bullet firing system in order to hit the intended target, which is addressed in fire control issues, in such a way that the required information is given to the fire control system, and the system after analysis The information selects the most suitable angle and fires the projectile at the target. In this research, we are looking for a solution to determine the pre-target angles of a projectile to hit an aerial target, or in other words, to control the fire to hit an air defense ball on the target. In order to prevent further damage by the enemy while reducing defense costs. In this regard, the problem is first examined in two-dimensional mode, then the problem is considered in three dimensions, in order to calculate the most suitable angle in the fastest time, MLP neural network will be used in this research.

Keywords

References

[2]   خدادادی, ن., کنترل آتش توپ ضد هوایی ناو. دانشگاه صنعتی شریف, 1375.
[14]  رهبر, ن., به کارگیری شبکه های عصبی مصنوعی در تحلیل عملکرد بالستیک داخلی موتور راکت های سوخت جامد. 2013.
[1]  Zarchan, P., Tactical and strategic missile guidance. 2012: American Institute of Aeronautics and Astronautics, Inc.
[3]  Blakelock, J.H., Automatic control of aircraft and missiles. 1991: John Wiley & Sons.
[4]  Lee, Y.W., Neural solution to the target intercept problems in a gun fire control system. Neurocomputing, 2007. 70(4-6): p. 689-696.
[5]  Elnashar, G.A., A mathematical model derivation of general fire control problem and solution scenario. Int. J. Model. Identif. Control., 2013. 20: p. 223-233.
[6]  Weiss, I.M. and R. Cross, SHIP MOTION EFFECTS ON GUN FIRE CONTROL SYSTEM DESIGN. Naval Engineers Journal, 1979. 91: p. 75-80.
[7]  Zhu, K., et al. GeniusRoute: A new analog routing paradigm using generative neural network guidance. in 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD). 2019. IEEE.
[8]  Lee, H., et al., Missile guidance using neural networks. Control Engineering Practice, 1997. 5(6): p. 753-762.
[9]  Wang, C.-H. and K.-N. Hung, Intelligent Adaptive Law for Missile Guidance Using Fuzzy Neural Networks. International Journal of Fuzzy Systems, 2013. 15(2).
[10]      Zhao, B., et al., Integrated strapdown missile guidance and control based on neural network disturbance observer. Aerospace Science and Technology, 2019. 84: p. 170-181.
[11]      Li, Z., et al., Missile guidance law based on robust model predictive control using neural-network optimization. IEEE transactions on neural networks and learning systems, 2014. 26(8): p. 1803-1809.
[12]      Zhang, K., G. Wan, and X. Xi, Enhanced Loran skywave delay estimation based on artificial neural network in low SNR environment. IET Radar, Sonar & Navigation, 2020. 14(1): p. 127-132.
[13]      Walczak, S., Artificial neural networks, in Advanced methodologies and technologies in artificial intelligence, computer simulation, and human-computer interaction. 2019, IGI global. p. 40-53.
[15]      Roh, M.-S., B.-S.J.I.J.o.P.E. Kang, and Manufacturing, Dynamic accuracy improvement of a MEMS AHRS for small UAVs. 2018. 19(10): p. 1457-1466.
[16]      Yoo, T.S., et al., Gain-scheduled complementary filter design for a MEMS based attitude and heading reference system. 2011. 11(4): p. 3816-3830.
[17]      Li, W. and J.J.T.J.o.N. Wang, Effective adaptive Kalman filter for MEMS-IMU/magnetometers integrated attitude and heading reference systems. 2013. 66(1): p. 99-113.

Files

Share

How to cite

Statistics