TY - JOUR
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 Ali
N1 - Publisher Copyright:
© 1972-2012 IEEE.
PY - 2016/11/1
Y1 - 2016/11/1
N2 - In this paper, an intelligent adaptive tracking contro system (IATCS) based on the mixed H2/H∞approach fo achieving high precision performance of a two-Axis motion contro system is proposed. The two-Axis motion control system is a X-Y table driven by two permanent-magnet linear synchronou motors (PMLSMs) servo drives. The proposed control schem incorporates a mixed H2/H∞controller, a self-organizing recurren fuzzy-wavelet-neural-network controller (SORFWNNC) and a robust controller. The SORFWNNC is used as the mai tracking controller to adaptively estimate an unknown nonlinea dynamic function (UNDF) that includes the lumped paramete uncertainties, external disturbances, cross-coupled interference and frictional force. Furthermore, a robust controller is designed t deal with the approximation error, optimal parameter vectors, an higher order terms in Taylor series. Besides, the mixed H2/H∞controller is designed such that the quadratic cost function is minimize and the worst case effect of the UNDF on the tracking erro must be attenuated below a desired attenuation level. The onlin adaptive control laws are derived based on Lyapunov theorem an the mixed H2/H∞ tracking performance so that the stability o the IATCS can be guaranteed. The experimental results confir that the proposed IATCS grants robust performance and precis dynamic response to the reference contours regardless of externadisturbances and parameter uncertainties.
AB - In this paper, an intelligent adaptive tracking contro system (IATCS) based on the mixed H2/H∞approach fo achieving high precision performance of a two-Axis motion contro system is proposed. The two-Axis motion control system is a X-Y table driven by two permanent-magnet linear synchronou motors (PMLSMs) servo drives. The proposed control schem incorporates a mixed H2/H∞controller, a self-organizing recurren fuzzy-wavelet-neural-network controller (SORFWNNC) and a robust controller. The SORFWNNC is used as the mai tracking controller to adaptively estimate an unknown nonlinea dynamic function (UNDF) that includes the lumped paramete uncertainties, external disturbances, cross-coupled interference and frictional force. Furthermore, a robust controller is designed t deal with the approximation error, optimal parameter vectors, an higher order terms in Taylor series. Besides, the mixed H2/H∞controller is designed such that the quadratic cost function is minimize and the worst case effect of the UNDF on the tracking erro must be attenuated below a desired attenuation level. The onlin adaptive control laws are derived based on Lyapunov theorem an the mixed H2/H∞ tracking performance so that the stability o the IATCS can be guaranteed. The experimental results confir that the proposed IATCS grants robust performance and precis dynamic response to the reference contours regardless of externadisturbances and parameter uncertainties.
KW - Fuzzy wavelet neural network
KW - Lyapunov satiability
KW - mixed H/H performance
KW - permanent-magnet linear synchronous motors (PMLSM)
KW - X-Y table
UR - http://www.scopus.com/inward/record.url?scp=84999648432&partnerID=8YFLogxK
U2 - 10.1109/TIA.2016.2591901
DO - 10.1109/TIA.2016.2591901
M3 - Article
AN - SCOPUS:84999648432
SN - 0093-9994
VL - 52
SP - 5139
EP - 5155
JO - IEEE Transactions on Industry Applications
JF - IEEE Transactions on Industry Applications
IS - 6
M1 - 7514763
ER -