Variable Structure Rough Neural Network Control for a Class of Non-Linear Systems

Dadvand, Sina; Manthouri, Mohammad; Teshnehlab, Mohammad

Document Type : Review Article

Authors

¹ Department of Electrical Engineering, Science and Research branch, Islamic Azad University, Tehran, Iran

² Electrical and Electronic Engineering Department, Shahed university, Tehran, Iran

³ Industrial Control Center of Excellence, Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran

Abstract

In this paper, a novel rough neural network control system based on the variable structure control developed for a class of SISO canonical nonlinear systems with taking the presence of bounded disturbance into account is presented. We assume that the nonlinear functions of the system are completely unknown. The rough neural network presented here is used to approximate the unknown nonlinear functions to a desired appropriate approximation. A fuzzy soft switching structure is developed to decide the amount of efforts taken by neural network and variable structure control systems based upon the real-time error characteristics. A proper Lyapunov function is defined and used to deduce adaptive laws for tunable parameters of neural network and to achieve the closed loop stability of overall system. The rough family of neural networks have a reputation of better functionality at the presence of noise and disturbance, which comes from their interval characteristic of their parameters. In this study, we utilize this property to achieve better performance. To demonstrate the effect of proposed control structure, it is applied upon three systems (one exemplary system, a dynamical and a chaotic) and the simulated results show the efficiency of this hybrid variable structure control scheme.

Keywords

References

[1] K. Åström†, "Theory and applications of adaptive control—A survey," Automatica, vol. 19, no. 5, pp. 471-486, September 1983.

[2] P. P. V. Kokotović, "Foundations of Adaptive Control," 1991.

[3] M. M. P. J. A. Farrell, Adaptive Approximation Based Control : Unifiying Neural, Fuzzy and Traditional Adaptive Approximation Approaches, Wiley Interscience, 2006.

[4] R. M. Sanners, J. -J. E. Slotine, "Gaussian Networks For Direct Adaptive Control," IEEE Transactions on Neural Network, vol. 3, no. 6, pp. 837-863, Novamber 1992.

[5] J. E. Hogans IV, A. Homaifar, B. Sayyarridsari,, "Fuzzy Inference for Variable Structure Control," Journal of Intelligent and Fuzzy Systems, vol. 2, no. 3, pp. 229-241, 1994.

[6] Y. Pan, K. D. Kumar, G. Liu, K. Furuta, "Design of Variable Structure Control System With Nonlinear Time-Varying Sliding Sector," IEEE Transactions on Automatic Contol, vol. 54, no. 8, pp. 1981-1986, 2009.

[7] J. -P. Su, T. -E, Lee, K. -W. Yu,, "A Combined Hard and Soft Variable Structure Control Scheme for a Class of Nonlinear Systems," IEEE Transactions on Industrial Electronics, vol. 56, no. 9, pp. 3305-3313, 2009.

[8] M. Chen, S. S. Ge, B. V. E. How, "Robust Adaptive Neural Network Control for a Class of Uncertain MIMO Nonlinear Systems With Input Nonlinearities," IEEE Transactions on Neural Networks, vol. 21, no. 5, pp. 796-812, 2010.

[9] M. Mansouri, "Adaptive Variable Structure Hierarchical Fuzzy Control for a Class of High-order Nonlinear Dynamic Systems," ISA Transactions, vol. 56, pp. 28-41, 2014.

[10] H. Wang, P. He, M.Yu, L. Liu, M. T. Do, H. Kong, Z. Man,, "Adaptive Neural Network Sliding Mode Control for Steer-By-Wire-Based Vehicle Stability Control," Journal Of Intelligent & Fuzzy Systems, pp. 1-18, 2016.

[11] Y. -J. Mon, C. -M. Lin,, "Double Inverted Pendulum decoupling control by Adaptive Terminal Sliding-Mode Recurrent Fuzzy Neural Network," Journal of Intelligent & Fuzzy Systems, vol. 26, no. 4, pp. 1723-1729, 2014.

[12] S. Mahjoub, F. Mnif, N. Derbel,, "Radial-Basis-Funcion Neural Network Sliding Mode Control For Underactuated Manipulators," in 10th International Multi-Conference on Systems, Signals & Devices (SSD), Hammamet, 2013.

[13] G. Guoqin, D. Qinqin, W. Wei,, "Sliding Mode Control of Parallel Robot by Optimizing Swithing Gain based on RBF Neural Network," in 31st Chinese Control Conference, Hefei, 2012.

[14] B. Sun, W. Ma,, "Multigranulation Rough Set Theiry Over Two Universes," Journal of Intelligent & Fuzzy Systems, vol. 28, no. 3, pp. 1251-1269, 2015.

[15] L. Song, S. Jin,, "Production Performance Evaluation Based On Rough Set Theory And Wavelet Neural Network," Journal of intelligent & Fuzzy Systems, vol. 29, no. 6, pp. 2429-2437, 2015.

[16] M. Aggarwal,, "Probabilistic Fuzzy Rough Sets," Journal of Intelligent & Fuzzy Systems, vol. 29, no. 5, pp. 1901-11912, 2015.

[17] Z. Tengfei, T. Yaliang, F. Ma,, "Nonlinear Internal Model Control Based On Fuzzy Rough Granular Neural Networks," in The 26th Chinese Control and Decision Conference, Changsha, 2014.

[18] J. Huang, S. Li, C. Man,, "A T-S Type Of Rough Fuzzy Controller Based On Process Input-Output Data," in 4nd IEEE Conference on Decision and Control, 2003.

[19] H. Wang, Y. Rong, T. Wang,, "Rough Control for Hot Rolled Laminar Cooling," in 2nd International Conference on Industrial Mechatronics and Automation (ICIMA), Wuhan, 2010.

[20] M. M. M. T. V. Bahrami, "Conrol and Synchronization of a Class of Chaotic Systems by Using a Lyapunov Based Model Refrenece Rough-RBF Neural Network Controller With Feedback Error Learning," Journal of Nonlinear Systems in Electrical Engineering, vol. 3, no. 1, 2015.

[21] J. Liu, Radial Basis Function (RBF) Neural Network Control for Mechanical Systems, Springer, 2013.

Majlesi Journal of Electrical Engineering

Variable Structure Rough Neural Network Control for a Class of Non-Linear Systems

References

References

Volume 13, Issue 4 - Serial Number 4
December 2019
Pages 99-109

Variable Structure Rough Neural Network Control for a Class of Non-Linear Systems

References

References

Volume 13, Issue 4 - Serial Number 4December 2019Pages 99-109

Volume 13, Issue 4 - Serial Number 4
December 2019
Pages 99-109