Adaptive Sliding-Mode H∞ Control of PMLSM Drive System via Interval Type-2 Petri Fuzzy-Neural-Network for a Two-Dimensional X-Y Table

Abstract

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 X-Y table motion control system driven through permanent-magnet linear synchronous motor (PMLSM) servo drives. ASMHC approach includes the sliding-mode controller (SMC), robust H∞ controller, and SEFLIT2PFNN estimator. In ASMHC design, the SMC technique is employed as it has rapid dynamic response with an invariance capability against uncertain dynamics, SEIT2FLFNN estimator is utilized for approximating the uncertain nonlinear functions of the X-Y table 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 X-Y table 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.