Adaptive Wavelet Neural Network Control With Hysteresis Estimation for Piezo-Positioning Mechanism

Lin, Faa-Jeng; Shieh, Hsin-Jang; Huang, Po-Kai

doi:10.1109/tnn.2005.863473

Cited by 111 publications

(66 citation statements)

References 41 publications

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“…It is well known that there exist two difficulties in modeling of the hysteresis nonlinearity of piezopositioning mechanism; they are non-local memory phenomenon and asymmetric loop between descending and ascending paths [8]. Therefore, the development of a dynamic model to describe the hysteresis behavior is very important for the improvement of the control performance of the piezo-positioning mechanism.…”

Section: Dynamic Modeling For the Slave Robotmentioning

confidence: 99%

“…Therefore, the development of a dynamic model to describe the hysteresis behavior is very important for the improvement of the control performance of the piezo-positioning mechanism. The hysteresis friction model, called LuGre model, is used due to reasons explained in [8]. Meanwhile, there is a further important benefit leading us to use this model; hysteresis effect of piezo-positioning mechanism can be separately added to the slave linear dynamics.…”

Section: Dynamic Modeling For the Slave Robotmentioning

confidence: 99%

“…For the parameters definition and more details about LuGre model, reader is referred to [19] and [8].…”

Section: Dynamic Modeling For the Slave Robotmentioning

confidence: 99%

“…It is because of the fact that hysteresis loop (and therefore its describing constant parameters) fundamentally depends on the frequency and amplitude of the input signal [8]. That means, the exact slave dynamic parameters m s , b s , and those of , K g is the nonlinear gain, φ is the boundary layer thickness reducing the chattering of the control input, and s(t) is the sliding surface [20].…”

Section: Smbic For the Slavementioning

confidence: 99%

“…LuGre friction model is used; because it is proved that the model estimates the hysteresis behavior of piezo-actuator precisely [8].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Robust Impedance Control of a Delayed Telemanipulator Considering Hysteresis Nonlinearity of the Piezo-actuated Slave Robot

Seifabadi

Rezaei

Shiry

et al. 2008

Haptics: Perception, Devices and Scenarios

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Abstract. The slave robot in this research is a 1-DOF piezo actuator which includes hysteresis nonlinearity. Nonlinear hysteresis behavior makes robot control a complex task. In this research, the nonlinear and uncertain dynamics of the slave robot has been considered through the teleoperation control loop. LuGre friction model is used as the estimator of the hysteresis loop. An impedance controller for the master side and a sliding-mode-based impedance controller for the slave side have been proposed. The latter is a sliding mode controller, because the plant is nonlinear and uncertain. Also, it is an impedance controller providing both high performances during contact and excellent tracking in free space motion. These controllers make teleoperator robustly stable against uncertainties and bounded constant time delay. Meanwhile, scaling factors, known as sources of instability, have no disturbing effect. After canceling the nonlinear term out of the teleoperator by the controllers, stability of the entire system will be guaranteed by Llewellyn's absolute stability criterion. Performance of the proposed controllers is investigated through simulation.

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Section: Dynamic Modeling For the Slave Robotmentioning

confidence: 99%

Section: Dynamic Modeling For the Slave Robotmentioning

confidence: 99%

“…For the parameters definition and more details about LuGre model, reader is referred to [19] and [8].…”

Section: Dynamic Modeling For the Slave Robotmentioning

confidence: 99%

Section: Smbic For the Slavementioning

confidence: 99%

“…LuGre friction model is used; because it is proved that the model estimates the hysteresis behavior of piezo-actuator precisely [8].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Robust Impedance Control of a Delayed Telemanipulator Considering Hysteresis Nonlinearity of the Piezo-actuated Slave Robot

Seifabadi

Rezaei

Shiry

et al. 2008

Haptics: Perception, Devices and Scenarios

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show abstract

Neural network‐based adaptive control of piezoelectric actuators with unknown hysteresis

Xie

Yan

et al. 2008

Adaptive Control & Signal

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This paper proposes a neural network (NN)-based adaptive control of piezoelectric actuators with unknown hysteresis. Based on the classical Duhem model described by a differential equation, the explicit solution to the equation is explored and a new hysteresis model is constructed as a linear model in series with a piecewise continuous nonlinear function. An NN-based dynamic pre-inversion compensator is designed to cancel out the effect of the hysteresis. With the incorporation of the pre-inversion compensator, an adaptive control scheme is proposed to have the position of the piezoelectric actuator track the desired trajectory. This paper has three distinct features. First, it applies the NN to online approximate complicated piecewise continuous unknown nonlinear functions in the explicit solution to Duhem model. Second, an observer is designed to estimate the output of hysteresis of piezoelectric actuator based on the system input and output. Third, the stability of the controlled piezoelectric actuator with the observer is guaranteed. Simulation results for a practical system validate the effectiveness of the proposed method in this paper.

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Intelligent Integral Backstepping Sliding‐mode Control Using Recurrent Neural Network For Piezo‐flexural Nanopositioning Stage

Lin¹,

Lee²,

Chou³

2014

Asian Journal of Control

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In this study, an intelligent integral backstepping sliding-mode control (IIBSMC) system using a recurrent neural network (RNN) is proposed for the three-dimensional motion control of a piezo-flexural nanopositioning stage (PFNS). First, the dynamic model of the PFNS is derived. Then, an integral backstepping sliding-mode control (IBSMC) system is proposed for the tracking of the reference contours. The steady-state response of the control system can be improved effectively due to the addition of the integrator in the IBSMC. Moreover, to relax the requirements of the bound and discard the switching function in the IBSMC, an IIBSMC system using an RNN estimator is proposed to improve the control performance and the robustness of the PFNS. The RNN estimator is proposed to estimate the lumped uncertainty, including the system parameters and external disturbance, online. Furthermore, the online tuning law for the training of the parameters of the RNN is derived using the Lyapunov stability theorem. In addition, a robust compensator is proposed to confront the minimum reconstructed error occurring in the IIBSMC system. Finally, some experimental results for the tracking of various contours are given to demonstrate the validity of the proposed IIBSMC system. From the performance measurements of the proportional-integral control, sliding mode control, IBSMC, and IIBSMC systems, the proposed IIBSMC system has the lowest maximum, average, and standard deviation of the position tracking errors for three-dimensional motion control of the PFNS.

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Adaptive Wavelet Neural Network Control With Hysteresis Estimation for Piezo-Positioning Mechanism

Cited by 111 publications

References 41 publications

Robust Impedance Control of a Delayed Telemanipulator Considering Hysteresis Nonlinearity of the Piezo-actuated Slave Robot

Robust Impedance Control of a Delayed Telemanipulator Considering Hysteresis Nonlinearity of the Piezo-actuated Slave Robot

Neural network‐based adaptive control of piezoelectric actuators with unknown hysteresis

Intelligent Integral Backstepping Sliding‐mode Control Using Recurrent Neural Network For Piezo‐flexural Nanopositioning Stage

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