Controller design for high-frequency cutting using a piezo-driven microstage

Modeling and Compensation for Hysteresis Nonlinearity of a Piezoelectrically Actuated Fast Tool Servo Based on a Novel Linear Model

“…As for compensation operations, it is essential to obtain the compensated control signal u c (k) corresponding to the desired displacement x d (k). As for conventional hysteresis models, the model inversions are difficult to be achieved due to their complex nonlinear operators [6,10,11,13]. However, by taking advantage of the linear frame of the LFDH model in this paper, the analytical description of the inverse LFDH model can be directly accessed as…”

“…The frequency of the command signal is especially chosen as 1 Hz. The low frequency chosen here can avoid possible separations between the PEA and the tool holder during rapid expansions and retractions of the PEA [6], and the hypothesis that the displacement of the PEA is equivalent to that of the mechanism can come into existence.…”

“…However, due to the intrinsic friction among the material crystals of the PEA, there always exists nonlinear hysteresis effects when electric voltages are applied to drive the PEA [4,5]. Apparently, the hysteresis nonlinearity would significantly limit the positioning performance of the cutting tool of the FTS and even lead to the instability of the servo system, consequently limiting developments of FTS-based micro/nano machining [5][6][7]. To overcome these limitations, accurate models for hysteresis behaviors should be critically constructed to accordingly develop compensation strategies or enhance the performance of designed controllers.…”

“…Motivated by this, extensive mathematical models for hysteresis have been developed, and they may be classified into two aspects: physics-based [5] and phenomenological models [6][7][8]. The main disadvantage of physics-based models is that they require a large number of materialrelated parameters.…”

“…The phenomenological models are more relatively suitable and have found widespread acceptance for practical applications in engineering. Various types of such models have been introduced, such as the Preisach model [6,9], the Prandtl-Ishlinskii (PI) model [10,11], the Bouc-Wen differential model [12], the turning voltage-based model [7,13], and the neural network-based intelligent model [14,15]. Essentially, almost all of these currently developed models are based on complex nonlinearity operators.…”