TY - GEN
T1 - Adaptive sliding-mode H∞ control via self-evolving function-link interval type-2 petri fuzzy-neural-network for XY-stage nonlinear system
AU - El-Sousy, Fayez F.M.
AU - Amin, Mahmoud M.
AU - Aziz, Ghada A.Abdel
AU - Mohammed, Osama A.
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - This paper proposes a novel adaptive sliding mode H∞ control (ASMHC) via self-evolving function-link interval type-2 Petri fuzzy-neural-network (SEFLIT2PFNN) for XYstage motion control system driven through two linear synchronous motors servo drives. The ASMHC approach includes a sliding-mode controller (SMC), a robust H∞ controller, and an SEFLIT2PFNN estimator. In the ASMHC design, the SMC technique is employed as it has rapid dynamic response with an invariance capability against uncertain dynamics, the SEIT2FLFNN estimator is utilized for approximating the uncertain nonlinear functions of the XYstage and the H∞ controller is developed for compensating the effects of the SEFLIT2PFNN approximation errors and external disturbances at a definite attenuation level. Furthermore, H∞ control theory and Lyapunov stability analysis are employed for online adaptive control laws, so that the stability of the ASMHC scheme can be assured. The validity of the proposed control system is verified by experimental analysis. The dynamic response of the XY-stage motion control system using ASMHC promises closed-loop stability and promises the H∞ tracking performance for the whole system. The experimental validation results endorsed that the proposed ASMHC has robust control response even the presence of system disturbances and parameter uncertainties.
AB - This paper proposes a novel adaptive sliding mode H∞ control (ASMHC) via self-evolving function-link interval type-2 Petri fuzzy-neural-network (SEFLIT2PFNN) for XYstage motion control system driven through two linear synchronous motors servo drives. The ASMHC approach includes a sliding-mode controller (SMC), a robust H∞ controller, and an SEFLIT2PFNN estimator. In the ASMHC design, the SMC technique is employed as it has rapid dynamic response with an invariance capability against uncertain dynamics, the SEIT2FLFNN estimator is utilized for approximating the uncertain nonlinear functions of the XYstage and the H∞ controller is developed for compensating the effects of the SEFLIT2PFNN approximation errors and external disturbances at a definite attenuation level. Furthermore, H∞ control theory and Lyapunov stability analysis are employed for online adaptive control laws, so that the stability of the ASMHC scheme can be assured. The validity of the proposed control system is verified by experimental analysis. The dynamic response of the XY-stage motion control system using ASMHC promises closed-loop stability and promises the H∞ tracking performance for the whole system. The experimental validation results endorsed that the proposed ASMHC has robust control response even the presence of system disturbances and parameter uncertainties.
UR - http://www.scopus.com/inward/record.url?scp=85090398337&partnerID=8YFLogxK
U2 - 10.1109/AIM43001.2020.9159013
DO - 10.1109/AIM43001.2020.9159013
M3 - Conference contribution
AN - SCOPUS:85090398337
T3 - IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
SP - 1356
EP - 1361
BT - 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2020
Y2 - 6 July 2020 through 9 July 2020
ER -