TY - GEN
T1 - Self-organizing recurrent fuzzy wavelet neural network-based mixed H2/H∞ adaptive tracking control for uncertain two-axis motion control system
AU - El-Sousy, Fayez F.M.
AU - Abuhasel, Khaled A.
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/14
Y1 - 2015/12/14
N2 - In this paper, an intelligent adaptive tracking control system (IATCS) based on the mixed Jf2AC approach for achieving high precision performance of a two-axis motion control system is proposed. The two-axis motion control system is an X-Y table driven by two permanent-magnet linear synchronous motors (PMLSMs) servo drives. The proposed control scheme incorporates a mixed H2/H∞ controller, a self-organizing recurrent fuzzy-wavelet-neural-network controller (SORFWNNC) and a robust controller. The SORFWNNC is used as the main tracking controller to adaptively estimate an unknown nonlinear dynamic function (UNDF) that includes the lumped parameter uncertainties, external disturbances, cross-coupled interference and frictional force. Furthermore, a robust controller is designed to deal with the approximation error, optimal parameter vectors and higher order terms in Taylor series. Besides, the mixed H2/H∞ controller is designed such that the quadratic cost function is minimized and the worst case effect of the UNDF on the tracking error must be attenuated below a desired attenuation level. The online adaptive control laws are derived based on Lyapunov theorem and the mixed H2/H∞, tracking performance so that the stability of the IATCS can be guaranteed. The experimental results confirm that the proposed IATCS grants robust performance and precise dynamic response to the reference contours regardless of external disturbances and parameter uncertainties.
AB - In this paper, an intelligent adaptive tracking control system (IATCS) based on the mixed Jf2AC approach for achieving high precision performance of a two-axis motion control system is proposed. The two-axis motion control system is an X-Y table driven by two permanent-magnet linear synchronous motors (PMLSMs) servo drives. The proposed control scheme incorporates a mixed H2/H∞ controller, a self-organizing recurrent fuzzy-wavelet-neural-network controller (SORFWNNC) and a robust controller. The SORFWNNC is used as the main tracking controller to adaptively estimate an unknown nonlinear dynamic function (UNDF) that includes the lumped parameter uncertainties, external disturbances, cross-coupled interference and frictional force. Furthermore, a robust controller is designed to deal with the approximation error, optimal parameter vectors and higher order terms in Taylor series. Besides, the mixed H2/H∞ controller is designed such that the quadratic cost function is minimized and the worst case effect of the UNDF on the tracking error must be attenuated below a desired attenuation level. The online adaptive control laws are derived based on Lyapunov theorem and the mixed H2/H∞, tracking performance so that the stability of the IATCS can be guaranteed. The experimental results confirm that the proposed IATCS grants robust performance and precise dynamic response to the reference contours regardless of external disturbances and parameter uncertainties.
KW - Fuzzy wavelet neural network
KW - Lyapunov satiability theorem
KW - mixed H2/H∞ tracking performance
KW - PMLSM
KW - two-axis motion control system
KW - X-Y table
UR - http://www.scopus.com/inward/record.url?scp=84957674927&partnerID=8YFLogxK
U2 - 10.1109/IAS.2015.7356812
DO - 10.1109/IAS.2015.7356812
M3 - Conference contribution
AN - SCOPUS:84957674927
T3 - IEEE Industry Application Society - 51st Annual Meeting, IAS 2015, Conference Record
BT - IEEE Industry Application Society - 51st Annual Meeting, IAS 2015, Conference Record
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 51st Annual Meeting on IEEE Industry Application Society, IAS 2015
Y2 - 11 October 2015 through 22 October 2015
ER -