Rules of Change of Weight Matrix in objective Function in Model Predictive Control in order to Consider Stability for Twin Rotor MIMO System Based on Bellman’s Dynamic Programming Method

International Journal of Electrical and Electronics Engineering

© 2020 by SSRG - IJEEE Journal

Volume 7 Issue 2

Year of Publication : 2020

Authors : Huong T.M. Nguyen

10.14445/23488379/IJEEE-V7I2P106

How to Cite?

Huong T.M. Nguyen, "Rules of Change of Weight Matrix in objective Function in Model Predictive Control in order to Consider Stability for Twin Rotor MIMO System Based on Bellman’s Dynamic Programming Method," SSRG International Journal of Electrical and Electronics Engineering, vol. 7,  no. 2, pp. 29-33, 2020. Crossref, https://doi.org/10.14445/23488379/IJEEE-V7I2P106

Abstract:

A paper [1] indicated that the survey results apply Model Predictive Control for the TRMS based on Bellman’s dynamic programming method in order to consider the stability with the predictive horizon goes to infinity. The results of [1] has just proved the stability of system but they did not satisfy constraint conditions of state parameters. In this article, author provide rules of change of weight matrix in objective function in order to satisfy constraint conditions of the inputs, the outputs and the state parameters of this system. All of the state parameters reach to zero and satisfied with constraint conditions, the stable global of the system when the predictive horizon (NP) goes to infinity proved a reasonable rule with simulation results.

Keywords:

Constraint conditions, dynamic programming, model predictive control, stability, state parameters, Twin rotor MIMO system (TRMS).

References:

[1] 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, pp. 161- 165, issue. 14, vol. 128, 2014.
[2] Phuoc. Nguyen.D, “Analysis and control of nonlinear systems”. Publishing Technology, Ha Noi University of Sciences and Technology, 2012.
[3] Nocedal. J and Wright. S.J, Numerical Optimization. Springer- New York, 1996.
[4] Grüne. L and Pannek. J, “Nonlinear model predictive control,” Theory and Algorithms, Springer, 2010.
[5] A. Rahideh, M.H. Shaheed. “Constrained output feedback model predictive control for nonlinear systems”, Control Engineering Practive 20, 2012, pp. 431-443.
[6] 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], pp. 39 – 42, 2014 .
[7] A. Rahideh and M.H. Shaheed, “Mathematical dynamic modelling of a twin rotor multiple input–multiple output system,”, Proc. of the IMeche, Part I. Journal of Systems and Control Engineering 221, 2007, pp.89–101.
[8] Phuoc. Nguyen.D, (2016) “Optimization in control and Optimal control”. Publishing Technology, Ha Noi University of Sciences and Technology.
[9] Huong T.M. Nguyen, Thai Mai T., Lai Lai K. (2015), “Model Predictive Control to get Desired Output with Infinite Predictive Horizon for Bilinear Continuous Systems”, International Journal of Mechanical Engineeringand Robotics Research, Vol. 4, No. 4, pp. 299 - 303.