Intelligent H∞ Control of Nano-Positioning Systems

This paper applies Hinfin control theory and evolution computing technology to design the optimally robust control system for a nano-positioning stage using piezoelectric actuators as driver components. After shaping the tracking errors and control energy through weighting function matrices, the Hinfin controller possesses the robust stability against the hysteresis effect. Furthermore, the hierarchical genetic algorithms are developed such that the control gene and coefficient gene evolve the structure of sensitivity function weighting and control energy weighting. The order and polynomial coefficients of the weighting function matrices do not need to be set in advance, but they will naturally evolve by artificial intelligence such that the optimal nano-positioning Hinfin controller is approached.