Yangqiu Xie

Recursive Identification of Micropositioning Stage Based on Sandwich Model With Hysteresis

In this brief, a recursive identification method for a micropositioning stage with piezo actuators is proposed. This method utilizes a sandwich model for describing nonlinear dynamics of the stage. In this model, both power amplifier with filtering circuit and the flexure hinge with load are described by linear dynamic submodels, respectively, whereas a Duhem hysteresis submodel is employed for describing the performance of the piezo actuator. An extended recursive identification algorithm is proposed for estimating the corresponding parameters of the sandwich model, and the convergence of the proposed algorithm is analyzed. Finally, the experimental results of a real positioning stage with piezo actuators are presented and discussed.

Identification of sandwich systems with backlash with hybrid inputs

An approach of identification for sandwich systems with backlash is presented in this paper. In this paper, a hybrid input is designed to degenerate the backlash into a linearized function. In this case, the so-called degenerated model with linearized parameters is obtained. Then, the Recursive General Identification Algorithm (RGIA) is employed to estimate the parameters of the model. After that, the parameters of the backlash are estimated. Finally, a simulation example based on the proposed scheme is presented.

Nonsmooth Recursive Identification of Sandwich Systems with Backlash-Like Hysteresis

A recursive gradient identification algorithm based on the bundle method for sandwich systems with backlash-like hysteresis is presented in this paper. In this method, a dynamic parameter estimation scheme based on a subgradient is developed to handle the nonsmooth problem caused by the backlash embedded in the system. The search direction of the algorithm is estimated based on the so-called bundle method. Then, the convergence of the algorithm is discussed. After that, simulation results on a nonsmooth sandwich system are presented to validate the proposed estimation algorithm. Finally, the application of the proposed method to an X-Y moving positioning stage is illustrated.

A model based compensator for rate-dependent hysteresis in piezoelectric actuators

A feedforward compensator for rate-dependent hysteresis in piezoelectric actuators (PEA) is proposed. In this method, a model with parallel structure is proposed to approximate both hysteretic behavior and rate-dependent feature of the PEA. In the model, the rate-independent hysteretic performance is approximated by a neural network based on the so called expanded input space method, while the rate-dependent behavior of the hysteresis is approximated by a network with the sum of weighted first-order difference operators. To transform the multi-valued mapping of the hysteresis into a one-to-one mapping, the expanded input space with a hysteretic operator is constructed. Then, the corresponding neural inverse sub-model is obtained. After that, a compensator is constructed to compensate for the effect of the rate-dependent hysteresis in the PEA. Finally, the experimental results of applying the proposed method to a piezoelectric actuator are presented.