Ruili Dong

Nonlinear Modeling and Decoupling Control of XY Micropositioning Stages With Piezoelectric Actuators

In this paper, a modeling method of XY micropositioning stage with piezoelectric actuators is proposed. In the modeling scheme, a sandwich model consists of both input and output linear submodels, and an embedded neural-network-based hysteresis submodel is used to describe the motion behavior of each axis of the stage. Moreover, a neural-network-based submodel is constructed to describe the nonlinear interactive dynamics caused by the movement of another axis. Then, a tracking control scheme combined with a nonlinear decoupling control is proposed to compensate for the effect of the interactions between axes and track the reference trajectory. Then, the robust design method for the tracking and decoupling control is discussed. Finally, the experimental results on an XY micropositioning stage are presented.

Recursive Identification of Sandwich Systems With Dead Zone and Application

In this paper, a recursive identification approach for a class of nonlinear systems called sandwich systems with the dead zone is proposed. In order to handle the effect of the dead zone, several switch functions are introduced into the model based on the so-called key term separation principle. Hence, the sandwich systems with the dead zone can be transformed into a special model where all the model parameters are separated. Then, a modified recursive general identification algorithm (MRGIA) is applied to the parameter-estimations of the obtained model. Moreover, the convergence of the algorithm for such systems will be discussed. Finally, a simulation example is presented, and the experimental results on an X-Y moving positioning stage are illustrated.

Recursive identification algorithm for dynamic systems with output backlash and its convergence

Recursive identification algorithm for dynamic systems with output backlash and its convergence This paper proposes a recursive identification method for systems with output backlash that can be described by a pseudo-Wiener model. In this method, a novel description of the nonlinear part of the system, i.e., backlash, is developed. In this case, the nonlinear system is decomposed into a piecewise linearized model. Then, a modified recursive general identification algorithm (MRGIA) is employed to estimate the parameters of the proposed model. Furthermore, the convergence of the MRGIA for the pseudo-Wiener system with backlash is analysed. Finally, a numerical example is presented.

Internal Model Control for Dynamic Systems With Preceded Backlash

A discrete-time internal model control approach for nonsmooth nonlinear systems described by a pseudo-Hammerstein model with backlash is presented. In this method, the controlled system is described by the pseudo-Hammerstein model, and the corresponding inverse model is constructed. Considering the existence of the model mismatch, the internal model control is implemented. As the model is switched among the different operating zones, the piecewise robust filters are proposed to improve the robust stability and transient performance of the control system. Finally, the simulation results based on the proposed method for a mechanical transmission system are presented.

Nonsmooth Predictive Control for Wiener Systems With Backlash-Like Hysteresis

In this paper, a nonsmooth predictive control method for Wiener systems with backlash-like hysteresis is proposed. In this type of system, the backlash-like hysteresis is connected in series with a preceded linear dynamic subsystem. A backlash-like hysteresis is modeled as a nonsmooth function with multivalued mapping; the corresponding objective function of the control system is also defined as being nonsmooth. In this case, the implementation of conventional model predictive control for such systems will encounter a challenge, since the gradients of the control criterion function with respect to control variables do not exist at nonsmooth points. In order to solve this problem, a nonsmooth receding horizon strategy based on Clarke subgradients is proposed. Moreover, the robust stability of the predictive control of such nonsmooth Wiener systems is analyzed. Finally, a numerical example and a simulation study on a mechanical transmission system are implemented to validate the proposed method.

Neural network based identification of hysteresis in human meridian systems

Abstract Developing a model based digital human meridian system is one of the interesting ways of understanding and improving acupuncture treatment, safety analysis for acupuncture operation, doctor training, or treatment scheme evaluation. In accomplishing this task, how to construct a proper model to describe the behavior of human meridian systems is one of the very important issues. From experiments, it has been found that the hysteresis phenomenon occurs in the relations between stimulation input and the corresponding response of meridian systems. Therefore, the modeling of hysteresis in a human meridian system is an unavoidable task for the construction of model based digital human meridian systems. As hysteresis is a nonsmooth, nonlinear and dynamic system with a multi-valued mapping, the conventional identification method is difficult to be employed to model its behavior directly. In this paper, a neural network based identification method of hysteresis occurring in human meridian systems is presented. In this modeling scheme, an expanded input space is constructed to transform the multi-valued mapping of hysteresis into a one-to-one mapping. For this purpose, a modified hysteretic operator is proposed to handle the extremum-missing problem. Then, based on the constructed expanded input space with the modified hysteretic operator, the so-called Extreme Learning Machine (ELM) neural network is utilized to model hysteresis inherent in human meridian systems. As hysteresis in meridian system is a dynamic system, a dynamic ELMneural network is developed. In the proposed dynamic ELMneural network, the output state of each hidden neuron is fed back to its own input to describe the dynamic behavior of hysteresis. The training of the recurrent ELM neural network is based on the least-squares algorithm with QR decomposition.

Fault detection of mechanical systems with inherent backlash

This paper proposes a scheme of fault detection for mechanical transmission system described by so-called sandwich models with backlash. In this scheme, the state variables of the system are estimated by a non-smooth observer due to the effect of backlash inherent in the transmission system can not be neglected. The fault detection is based on the estimated residual between the output of the observer and the measured data. The detection of faults happened in actuator, i.e. gearbox in the system is presented. The analysis of the effect of backlash on the fault detection is also illustrated in this paper.

Modeling of rate-dependent hysteresis using extreme learning machine based neural model

In this paper, a modified single hidden layer feedforward neural network (MSLFN) based model to describe the behavior of rate-dependent hysteresis inherent in piezoelectric actuators is proposed. In the proposed scheme, the improved SLFN model combining the weighted sum of simple backlash operators and the weighted sum of linear dynamic operators. According to the technique of the extreme learning machine, all the parameters of both backlash and linear dynamic operators are randomly assigned, while the output weights are determined by the least square (LS) algorithm. Then, the experimental results on a piezoceramic actuator are presented. It is shown that the improved model has obtained satisfactory approximation and generalization.

State Estimation of Micropositioning Stage With Piezoactuators

In this paper, a nonsmooth Kalman filtering method is proposed for noise suppression of micropositioning stages with piezoelectric actuators described by the so-called sandwich model with hysteresis. According to the characteristics of the system, a nonsmooth stochastic state-space equation is constructed. In this model, an autoswitcher is introduced to adapt the nonsmooth operation conditions. Then, the convergence of the novel Kalman filter is discussed. Subsequently, the comparison in simulation between the proposed filtering scheme with the unscented Kalman filter and particle filter is presented. Finally, the experimental results on a micropositioning stage with piezoelectric actuator are illustrated.

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.