Black box Modeling of Twin Rotor MIMO System by Using Neural Network

International Journal of Electrical and Electronics Engineering

© 2021 by SSRG - IJEEE Journal

Volume 8 Issue 6

Year of Publication : 2021

Authors : Huong T.M. Nguyen, Mai Trung Thai

10.14445/23488379/IJEEE-V8I6P103

How to Cite?

Huong T.M. Nguyen, Mai Trung Thai, "Black box Modeling of Twin Rotor MIMO System by Using Neural Network," SSRG International Journal of Electrical and Electronics Engineering, vol. 8,  no. 6, pp. 15-22, 2021. Crossref, https://doi.org/10.14445/23488379/IJEEE-V8I6P103

Abstract:

In model predictive control, building the correct model and solving the optimal problem are two jobs that always require a lot of time and effort. These are also two issues that many scientists are interested in studying when applying model-driven reporting control to certain objects. With a TRMS object we can build a white box model, a gray box model or a black box model. Some authors have built TRMS model published in [2], [3], [4], [5]. We have studied the optimal problem solving methods in model predictive control in articles [6], [7], [8]. In [9], we builds a white box model of TRMS object according to Newton method. Studying the effects of the interchannel effects of the white box model TRMS. In this paper, authors bulding black box modeling of Twin Rotor MIMO System by using neural network, compare the results of the black box model with the real model in order to choose a suitable algorithm and provide the ability to apply that model in simulation and object control.

Keywords:

Black box model, Neural network, Yaw angle, Pitch angle, Gradient descent back-propagation.

References:

[1] Twin Rotor MIMO System 33-220 User Manual, 1998 (Feedback Instruments Limited, Crowborough, UK).
[2] A. Rahideh, M.H. Shaheed, Mathematical dynamic modelling of a twin rotor multiple input–multiple output system, Proceedings of the IMechE, Part I. Journal of Systems and Control Engineering 221 (2007) 89–101.
[3] Ahmad, S. M., Shaheed, M. H., Chipperfield, A. J., and Tokhi, M. O. Nonlinear modelling of a twin rotor MIMO system using radial basis function networks. IEEE National Aerospace and Electronics Conference, (2000) 313–320.
[4] Ahmad, S. M., Chipperfield, A. J., and Tokhi, M. O. Dynamic modelling and optimal control of a twin rotor MIMO system. IEEE National Aerospace and Electronics Conference, (2000) 391–398.
[5] Shaheed, M. H. Performance analysis of 4 types of conjugate gradient algorithm in the nonlinear dynamic modelling of a TRMS using feedforward neural networks. IEEE International Conference on Systems, man and cybernetics, (2004) 5985–5990.
[6] Huong T.M. Nguyen, Thai. Mai.T, Anh. Do.T.T, Lai Lai K. (2014), Stabilization for Twin Rotor MIMO System based on BellMan’s Dynamic Programming Method, Journal of science and Technology of Thai Nguyen University, 128(14) (2014) 161-165.
[7] Huong. Nguyen.T.M, Thai. Mai.T, Chinh. Nguyen. H, Dung. Tran.T and Lai. Lai.K, Model Predictive Control for Twin Rotor MIMO system, The University of Da Nang Journal of science and Technology, 12(85) (2014) 39 – 42 .
[8] Huong T.M. Nguyen, Thai Mai T., Lai Lai K., Model Predictive Control to get Desired Output with Infinite Predictive Horizon for Bilinear Continuous Systems, International Journal of Mechanical Engineeringand Robotics Research, 4(4) (2015) 299 - 303.
[9] Huong T.M. Nguyen, Interchannel Interference in While box Model of Twin Rotor MIMO System. SSRG International Journal of Electrical and Electronics Engineering (SSRG-IJEEE), 8(4) (2021) 36-40.