Home Articles List Article Information
AUT Journal of Modeling and Simulation
Journal Archive
Volume Volume 50 (2018)
Volume Volume 49 (2017)
Volume Volume 48 (2016)
Volume Volume 47 (2015)
Volume Volume 46 (2014)
Volume Volume 45 (2013)
Volume Volume 44 (2012)
Volume Volume 43 (2011)
Volume Volume 42 (2010)
Volume Volume 41 (2009)
Karami-Mollaee, A., Shanechi, H. (2018). Dynamic Sliding Mode Control of Nonlinear Systems Using Neural Networks. AUT Journal of Modeling and Simulation, 50(1), 51-60. doi: 10.22060/miscj.2017.12805.5043
A. Karami-Mollaee; H. Shanechi. "Dynamic Sliding Mode Control of Nonlinear Systems Using Neural Networks". AUT Journal of Modeling and Simulation, 50, 1, 2018, 51-60. doi: 10.22060/miscj.2017.12805.5043
Karami-Mollaee, A., Shanechi, H. (2018). 'Dynamic Sliding Mode Control of Nonlinear Systems Using Neural Networks', AUT Journal of Modeling and Simulation, 50(1), pp. 51-60. doi: 10.22060/miscj.2017.12805.5043
Karami-Mollaee, A., Shanechi, H. Dynamic Sliding Mode Control of Nonlinear Systems Using Neural Networks. AUT Journal of Modeling and Simulation, 2018; 50(1): 51-60. doi: 10.22060/miscj.2017.12805.5043

Dynamic Sliding Mode Control of Nonlinear Systems Using Neural Networks

Article 6, Volume 50, Issue 1, Winter and Spring 2018, Page 51-60  XML PDF (553 K)
Document Type: Research Article
DOI: 10.22060/miscj.2017.12805.5043
Authors
A. Karami-Mollaee 1; H. Shanechi2
1Faculty of Electrical and Computer Engineering, Hakim Sabzevari University, Sabzevar, Iran
2Faculty of Electrical and Computer Engineering, Hakim Sabzevari University, Sabzevar, Iran.
Abstract
In this paper, dynamic sliding mode control (DSMC) of nonlinear systems using neural networks is proposed. In DSMC, the chattering is removed due to the integrator placed before the input control signal of the plant. However, in DSMC, the augmented system has higher order than the actual system, i.e. the states number of the augmented system is higher than the actual system and then to control of such a system, we must know and identify the new states, or the plant model should be completely known. To solve this problem, we suggest two online neural networks to identify and to obtain a model for the unknown nonlinear system. In the first approach, the neural network training law is based on the available system states and the bound of the observer error is not proved to converge to zero. The advantage of the second training law is only using the system’s output and the observer error converges to zero based on the Lyapunov stability theorem. To verify these approaches, Duffing-Holmes chaotic systems (DHC) are used.
Keywords
Dynamic Sliding Mode Control; Neural Model; Nonlinear system; Duffing-Holmes Chaotic System
Main Subjects
Adaptive Control; Intelligent based Identification; Neuro Controllers; Robust Control
References

[1] J.-J. E. Slotine, W. Li, Applied nonlinear control, Prentice-Hall, 1991.

[2] H. Lee, V.-I. Utkin, Chattering suppression methods in sliding mode control systems, Elsevier, Annual Review in Control, 31 (2007) 179-188.

[3] A. Karami-Mollaee, N. Pariz, H. M. Shanechi, Position control of servomotors using neural dynamic sliding mode, Transactions of the ASME (American Society of Mechanical Engineering), Journal of Dynamic Systems, Measurement and Control, 133 (6) (2011) 141-150.

[4] W. Perruquetti, J. Pierre-Barbot, Sliding mode control in engineering, Marcel Dekker, 2002.

[5] T. Sun, H. Pei, Y. Pan, H. Zhou, C. Zhang, Neural network-based sliding mode adaptive control for robot manipulators, Elsevier, Neurocomputing, 74(14-15) (2011) 2377-2384.

[6] M.-J. Zhang, Z.-Z. Chu, Adaptive sliding mode control based on local recurrent neural networks for underwater robot, Elsevier, Ocean Engineering, 45 (2012) 56-62.

[7] Y. Zou, X. Lei, A compound control method based on the adaptive neural network and sliding mode control for inertial stable platform, Elsevier, Neurocomputing, 155 (2015) 286-294.

[8] S. Mefoued, A second order sliding mode control and a neural network to drive a knee joint actuated orthosis, Elsevier, Neurocomputing, 155 (2015) 71-79.

[9] H. M. Kim, S. H. Park, S. I. Han, Precise friction control for the nonlinear friction system using the friction state observer and sliding mode control with recurrent fuzzy neural networks, Elsevier, Mechatronics, 19 (2009) 805- 815.

[10] A. Levant, Sliding order and sliding accuracy in sliding mode control, International Journal of Control, 58 (1993) 1247-1263.

[11] G. Bartolini, A. Ferrara, E. Usai, Chattering avoidance by second-order sliding mode control, IEEE Transaction on Automatic Control, 43(2) (1998) 241-246.

[12] A. Levant, Robust exact differentiation via sliding mode techniques, Elsevier, Automatica, 34 (1998) 379-384.

[13] M. Norgaard, O. Ravn, N. K. Poulsen, L. K. Hansen, Neural network for modeling and control of dynamic systems, Springer, New York, 2001.

[14] C.-H. Lin, Recurrent wavelet neural network control of a PMSG system based on a PMSM wind turbine emulator, Turkish Journal of Electrical Engineering & Computer Sciences, 22(4) (2014) 795-824.

[15] O. Kaynak, K. Erbatur, R. Ertugrul, The fusion of computationally Intelligent methodologies and sliding-mode control- a survey, IEEE Transaction on Industrial Electronic, 48(1) (2001) 4-17.

[16] M. K. Sifakis, S. J. Elliott, Strategies for the control of chaos in a Duffing–Holmes oscillator, Elsevier, Mechanical Systems and Signal Processing, 14(6) (2000) 987-1002.

[17] M. K. Sifakis, S. J. Elliott, Adaptive tracking control of Duffing-Holmes chaotic systems with uncertainty, The 5th International Conference on Computer Science & Education, Hefei, China, August 24–27, 2010, pp. 1193- 1197.

Statistics
Article View: 555
PDF Download: 38