Sliding-Neural Mode Controller Optimized by the PSO Algorithm for a Two-Degree Freedom Helicopter System

  • Iman Heydari Department of Electrical and Electronic Engineering, Tehran Branch, Islamic Azad University, Tehran , Iran
  • Amir Hossein Zaeri Department of Electrical and Electronic Engineering, Shahinshahr Branch, Islamic Azad University, Isfahan, Iran.
Keywords: Two-degree freedom helicopter, sliding mode control, particle swarm optimization algorithm, neural network

Abstract

Today, helicopters are widely used in various industries, including aviation and the military. Therefore, control and guidance of this device is of great importance. A 2-degree laboratory freedom helicopter is used to study small-scale helicopters. This device has much simpler dynamics and various experiments can be performed to check and control its condition. Based on these experiments, studies can be performed on more complex large-scale helicopter systems. In this research, while briefly introducing the 2_degree freedom helicopter, an appropriate controller is designed to improve its performance. The designed controller must be able to maintain its stability in tracking the inputs applied to it as a reference input, in the presence of external disturbances such as wind. Also, in case of uncertainty in system parameters such as weight changes, it should remain stable and track the applied inputs well. The controller designed in this study includes sliding mode control in which a neural network is used. In order to improve the results of the particle swarm optimization algorithm is used to determine the slip control parameters.

References

[1] M. N. Ibrahim and M. K. Hussein, “Load Frequency Control for Two-area Multi-Source Interconnected Power System using Intelligent Controllers,” Tikrit J. Eng. Sci., vol. 7589, pp. 12–19, 2018.
[2] R. GOCHHAYAT, “PSO Based Pi Controller for Load Frequency Control of Interconnected Power Systems,” vol. 88, no. 7, pp. 1–40, 2014.
[3] M. Sarkar, A. Dev, and P. Asthana, “Chattering Free Robust Adaptive Integral Higher Order Sliding Mode Control Chattering free robust adaptive integral higher order sliding mode control for load frequency problems in multi-area power systems,” no. July, 2018.
[4] D. Guha, P. Kumar, and S. Banerjee, “Application of backtracking search algorithm in load frequency control of multi-area interconnected power system,” Ain Shams Eng. J., vol. 9, no. 2, pp. 257–276, 2018.
[5] H. Bevrani, F. Habibi, P. Babahajyani, M. Watanabe, and Y. Mitani, “Intelligent frequency control in an AC microgrid: Online PSO-based fuzzy tuning approach,” IEEE Trans. Smart Grid, vol. 3, no. 4, pp. 1935–1944, 2012.
[6] N. K. Kumar and I. E. S. Naidu, “Load Frequency Control for A Multi Area Power System Involving Wind, Hydro and Thermal Plants,” vol. 3, no. 1, pp. 1008–1013, 2014.
[7] S. R. Krishna, P. Singh, and M. S. Das, “Control of load frequency of power system by PID controller using PSO,” Int. J. Recent Dev. Eng. Technol., vol. 5, no. 6, p. 37, 2016.
[8] K. R. Sudha, “Robust type-2 fuzzy c-means load frequency controller for multi area ( geothermal hydrothermal ) interconnected power system with GRC Anand Gondesi , R . Vijaya Santhi and,” vol. 2, no. 3, 2017.
[9] H. A. Yousef, K. Al-kharusi, and M. H. Albadi, “Load Frequency Control of a Multi-Area Power System : An Adaptive Fuzzy Logic Approach,” vol. 29, no. 4, pp. 1822–1830, 2014.
[10] E. S. Ali, “Electrical Power and Energy Systems Load frequency controller design via BAT algorithm for nonlinear interconnected power system Proportional plus Integral the Integral of Square Error,” vol. 77, pp. 166–177, 2016.
[11] S.Mohammadpoor, “Frequency-load control of multi-zone power system including thermal, wind and water power plants,” Natl. Conf. Electr. Eng. Telecommun. Sustain. Dev., 2014.
[12] M. Zribi and M. Alrifai, “Adaptive decentralized load frequency control of multi-area power systems,” vol. 27, pp. 575–583, 2005.
[13] I. Petrovi, K. Vrdoljak, and N. Peri, “Improved Particle Swarm Optimization Based Load Frequency Control In A Single Area Power System,” vol. 80, pp. 514–527, 2010.
[14] E. F. Camacho, Model predictive contro, 2nd ed. 2004.
[15] J. H. Lee, “Model predictive control: Review of the three decades of development,” Int. J. Control. Autom. Syst., vol. 9, no. 3, pp. 415–424, 2011.
[16] K.Eshghi, Algorithm analysis and design of meta-phrasal methods. 2016.
[17] H. Roshani, “Learning the theory of PSO algorithm,” https://sanaye20.ir, 2016. [Online]. Available: https://sanaye20.ir.
[18] H. Ogata, Modern Control Engineering, 5th ed. 1992.
[19] F. Faris, A. Moussaoui, and B. Djamel, “Design and real-time implementation of a decentralized sliding mode controller for twin rotor multi-input multi-output system,” vol. 231, no. 1, pp. 3–13, 2017.
Published
2021-03-01
How to Cite
Heydari, I., & Zaeri, A. H. (2021). Sliding-Neural Mode Controller Optimized by the PSO Algorithm for a Two-Degree Freedom Helicopter System. Majlesi Journal of Mechatronic Systems, 10(1), 37-53. Retrieved from http://journals.iaumajlesi.ac.ir/ms/index/index.php/ms/article/view/474
Section
Articles