TY - JOUR
T1 - Robust dynamic sliding-mode control using adaptive RENN for magnetic levitation system
AU - Lin, Faa Jeng
AU - Chen, Syuan Yi
AU - Shyu, Kuo Kai
N1 - Funding Information:
Manuscript received June 24, 2008; revised January 07, 2009; accepted January 19, 2009. First published May 05, 2009; current version published June 03, 2009. This work was supported by the National Science Council, Taiwan, under Grant NSC 95-2221-E-008-177-MY3.
PY - 2009
Y1 - 2009
N2 - In this paper, a robust dynamic sliding mode control system (RDSMC) using a recurrent Elman neural network (RENN) is proposed to control the position of a levitated object of a magnetic levitation system considering the uncertainties. First, a dynamic model of the magnetic levitation system is derived. Then, a proportional-integral-derivative (PID)-type sliding-mode control system (SMC) is adopted for tracking of the reference trajectories. Moreover, a new PID-type dynamic sliding-mode control system (DSMC) is proposed to reduce the chattering phenomenon. However, due to the hardware being limited and the uncertainty bound being unknown of the switching function for the DSMC, an RDSMC is proposed to improve the control performance and further increase the robustness of the magnetic levitation system. In the RDSMC, an RENN estimator is used to estimate an unknown nonlinear function of lumped uncertainty online and replace the switching function in the hitting control of the DSMC directly. The adaptive learning algorithms that trained the parameters of the RENN online are derived using Lyapunov stability theorem. Furthermore, a robust compensator is proposed to confront the uncertainties including approximation error, optimal parameter vectors, and higher order terms in Taylor series. Finally, some experimental results of tracking the various periodic trajectories demonstrate the validity of the proposed RDSMC for practical applications.
AB - In this paper, a robust dynamic sliding mode control system (RDSMC) using a recurrent Elman neural network (RENN) is proposed to control the position of a levitated object of a magnetic levitation system considering the uncertainties. First, a dynamic model of the magnetic levitation system is derived. Then, a proportional-integral-derivative (PID)-type sliding-mode control system (SMC) is adopted for tracking of the reference trajectories. Moreover, a new PID-type dynamic sliding-mode control system (DSMC) is proposed to reduce the chattering phenomenon. However, due to the hardware being limited and the uncertainty bound being unknown of the switching function for the DSMC, an RDSMC is proposed to improve the control performance and further increase the robustness of the magnetic levitation system. In the RDSMC, an RENN estimator is used to estimate an unknown nonlinear function of lumped uncertainty online and replace the switching function in the hitting control of the DSMC directly. The adaptive learning algorithms that trained the parameters of the RENN online are derived using Lyapunov stability theorem. Furthermore, a robust compensator is proposed to confront the uncertainties including approximation error, optimal parameter vectors, and higher order terms in Taylor series. Finally, some experimental results of tracking the various periodic trajectories demonstrate the validity of the proposed RDSMC for practical applications.
KW - Dynamic sliding-mode control (DSMC)
KW - Elman neural network (ENN)
KW - Magnetic levitation
KW - Robust control
UR - http://www.scopus.com/inward/record.url?scp=67649359712&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=67649359712&partnerID=8YFLogxK
U2 - 10.1109/TNN.2009.2014228
DO - 10.1109/TNN.2009.2014228
M3 - Article
C2 - 19423437
AN - SCOPUS:67649359712
SN - 1045-9227
VL - 20
SP - 938
EP - 951
JO - IEEE Transactions on Neural Networks
JF - IEEE Transactions on Neural Networks
IS - 6
ER -