Control of precision mechatronics systems

To compensate the precision of the tracking error of the gantry stage associated with the non-linear friction behavior, Dr. Yau based his method on the Hsieh-Pan's non-linear static and dynamic friction models and used plastics and non-linear spring modules for modeling purposes. Genetic algorithm (GA), real-coded genetic algorithm (RGA) and particle swarm optimization (PSO) were used to identify the optimal parameters of the non-linear friction model. In addition to the super-high positioning accuracy, the disturbance observer-based variable structure controller was proposed to improve the robustness of the tracking response to external disturbance and consistency was confirmed by experiments and numerical simulations. This result was published in the SCI journal IEEE/ASME Transactions on Mechatronics, 2013. Dr. Yau also used the static and kinetic friction models to analyze the motion of the ball-screw-driven stage at the micro/nano-level precision. An adaptive sliding controller was used to keep the positioning error of the ball-screw-driven stage within 10 nanometers (nm) for either long-range or short-range travel. On the hysteresis of the piezoelectric actuators, Dr. Yau applied a method based on the optimized evolutionary programming algorithm in combination with the delay differential equation model designed by Banning et al to build a model for the hysteresis of the piezoelectric actuators and experiments showed that the error did not exceed 15nm. A patent application is currently pending for this research result, which was published in the SCI journal IEEE/ASME Transactions on Mechatronics, 2013.