An adaptive recurrent radial basis function network tracking controller for a two-dimensional piezo-positioning stage.
An adaptive recurrent radial basis function network (ARRBFN) tracking controller for a two-dimensional piezo-positioning stage is proposed in this study. First, a mathematical model that represents the dynamics of the two-dimensional piezo-positioning stage is proposed. In this model, a hysteresis friction force that describes the hysteresis behavior of one-dimensional motion is used; and a nonconstant stiffness with the cross-coupling dynamic due to the effect of bending of a lever mechanism in x and y axes also is included. Then, according to the proposed mathematical model, an ARRBFN tracking controller is proposed. In the proposed ARRBFN control system, a recurrent radial basis function network (RRBFN) with accurate approximation capability is used to approximate an unknown dynamic function. The adaptive learning algorithms that can learn the parameters of the RRBFN on line are derived using Lyapunov stability theorem. Moreover, a robust compensator is proposed to confront the uncertainties, including approximation error, optimal parameter vectors, higher-order terms in Taylor series. To relax the requirement of the value of the lumped uncertainty in the robust compensator, an adaptive law is investigated to estimate the lumped uncertainty. Using the proposed control scheme, the position tracking performance is substantially improved and the robustness to uncertainties, including hysteresis friction force and cross-coupling stiffness, can be obtained as well. The tracking performance and the robustness to external load of the proposed ARRBFN control system are illustrated by some experimental results.
Lin
Faa-Jeng
FJ
Department of Electrical Engineering, Nat. Central Univ., Chungli, Taiwan. linfj@ee.ncu.edu.tw
Shieh
Hsin-Jang
HJ
Huang
Po-Kai
PK
Shieh
Po-Huang
PH
D017076:Computer-Aided Design; D004867:Equipment Design; D019544:Equipment Failure Analysis; D005246:Feedback; D008846:Micromanipulation / Q000295:instrumentation* / Q000379:methods; D036103:Nanotechnology / Q000295:instrumentation* / Q000379:methods; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014463:Ultrasonography / Q000295:instrumentation* / Q000379:methods