Frequency domain approach for dynamics identification of the actuator with asymmetric hysteresis

Micro/nano-manipulation systems have been developed and utilized for decades due to their irreplaceable roles in fields such as MEMS/NEMS fabrication and biological studies. Generally, the motion precision of a micro/nano-manipulator highly depends on its actuator, whose performance can be enhanced by proper control strategies. To design satisfactory controllers, an accurate plant model is ideal. For micro/nano-manipulators, the implemented actuators are mostly Smart Materials (SMs), which exhibit strong hysteretic and dynamic coupling characteristics. The construction of linear dynamics preceded by hysteresis is a prevalent representation for describing SM actuators' behaviors. To effectively and accurately model SM actuators, this paper employs the Extended Unparallel Prandtl-Ishlinskii (EUPI) model to describe complicated hysteretic behaviors. For modeling dynamics of SM actuators, firstly, the EUPI inverse is implemented to compensate the hysteretic effect of the plant; subsequently, the Weighted Complex Least-Squares (WCLS) identification method is proposed to estimate parameters of the dynamic part in the form of complex number function. To guarantee stability of the identified model, the Particle Swarm Optimization based WCLS (PSO-WCLS) optimization approach is proposed. The advantage of the proposed modeling scheme is that, it is capable of accurately describing complicated hysteresis of SM actuators and does not require the drive signal to be small while modeling its dynamics; besides this scheme contains frequency domain identification merits, such as easy noise reduction and easy combination of data from different experiments. The modeling and identification scheme is verified through comparison tests conducted on a piezoelectric actuator platform.