Compensation of Hysteresis in the Piezoelectric Nanopositioning Stage under Reciprocating Linear Voltage Based on a Mark-Segmented PI Model

An, Duo; Yang, Yixiao; Xu, Ying; Shao, Meng; Shi, J.B. Yu Y.; Yue, Guodong

doi:10.3390/mi11010009

Cited by 6 publications

(6 citation statements)

References 31 publications

(33 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hysteresis compensation is performed using dynamic inversion compensation, which uses NN for dynamic inversion compensation [19]. Other types of hysteresis models, including backlash and electronics, can be identified from references [1][2][3]. However, general history models will not be convenient because they are complex.…”

Section: Hysteresis Nonlinearitymentioning

confidence: 99%

“…Industrial dynamical control systems have generally the structure of a nonlinear system in front of some nonlinearity in the actuator, for example, dead-zone, backlash, and hysteresis, etc. Hysteresis phenomena caused by magnetism, stiction or gear with backlash generally exist in control system [1][2][3] and often severely reduce system performance such as giving rise to oscillations and/or undesirable inaccuracy, even leading to instability. Hysteresis characteristics are usually unknown and/or generally nondifferentiable nonlinearities.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hysteresis Compensation of Dynamic Systems Using Neural Networks

Jang¹

2022

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

A neural networks(NN) hysteresis compensator is proposed for dynamic systems. The NN compensator uses the back-stepping scheme for inverting the hysteresis nonlinearity in the feed-forward path. This scheme provides a general step for using NN to determine the dynamic pre-inversion of the reversible dynamic system. A tuning algorithm is proposed for the NN hysteresis compensator which yields a stable closed-loop system. Nonlinear stability proofs are provided to reveal that the tracking error is small. By increasing the gain we can reduce the stability radius to some extent. PI control without hysteresis compensation requires much higher gains to achieve similar performance. It is not easy to guarantee the stability of such highly nonlinear dynamical system if only a PI controller is used. Initializing the NN weights is simple. The initial weights of hidden layer are randomly selected and initial weights of output layer are set to zero. A PI loop with integerted an unity gain feedforward path keeps the system stable until the NN starts learning. Simulation results show its efficacy of the NN hysteresis compensator on a system. This work is applicable to xy table-like precision control system and also shows neural network stability proofs. Moreover, the NN hysteresis compensation can be further extended and applied to dead-zone, backlash, and other actuator nonlinear compensation.

show abstract

Section: Hysteresis Nonlinearitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hysteresis Compensation of Dynamic Systems Using Neural Networks

Jang¹

2022

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

show abstract

“…Since x-axes and y-axes are decoupled in the process of structural design and incey share the same dynamic characteristics with each other, the one-dimensional motion of the compliant 2-DOF ejector pin mechanism is studied in the following analysis. Due to the presentation of the hysteresis behavior of the piezoelectric actuator, the tracking accuracy of the device is greatly restricted [29]. Even worse, the sensor noise increases the measurement error, which is used to correct the input.…”

Section: Controller Designmentioning

confidence: 99%

A Novel Compliant 2-DOF Ejector Pin Mechanism for the Mass Transfer of Robotic Mini-LED Chips

Huang

Lin

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

The continuous development of mini-LEDs has led to higher requirements for chip transfer technology, which makes it difficult for the intermittent transfer method with a mechanical ejector pin to meet these requirements. To solve this problem, a novel compliant 2-DOF ejector pin mechanism for the mass transfer of robotic mini-LED chips is proposed in this paper. The compliance matrix method and the Newton method are employed for system kinematic modeling and dynamics modeling, respectively. The static and dynamic analyses of the mechanism are carried out via ANSYS Workbench, and the results of FEA are demonstrated the effectiveness of theoretical calculation. Then, an ILC is utilized to control the device via a parameters regulation approach in the frequency domain. Finally, an open-loop test and a trajectory tracking test for the prototype are carried out verify the effectiveness of proposed device. The test results indicate that the working stroke of the mechanism reaches 120 μm, the natural frequency of the device is 250.85 Hz, the coupling rate is less than ±0.5% and the tracking errors of 10 Hz, 20 Hz and 30 Hz sinusoidal signals are all within ±1.5%. According to the results of theoretical analyses, FEA and test, it has been proved that the designed mechanism for the mass transfer of mini-LED chips is superiority and effective.

show abstract

“…Piezoelectric actuators use the inverse piezoelectric effect of piezoelectric materials to convert electrical energy into mechanical energy to achieve movement output, and they are widely used in fields such as robotics [1], precision instrumentation [2], nanoscale positioning [3], and bioengineering [4].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Control of a Linear Piezoelectric Actuator

Li,

Tong,

2024

Actuators

View full text Add to dashboard Cite

To improve the output displacement of piezoelectric actuators, a linear piezoelectric actuator based on a multistage amplifying mechanism with a small volume, large thrust, high resolution, high precision, and fast response speed is proposed. However, inherent nonlinear characteristics, such as hysteresis and creep, significantly affect the output accuracy of piezoelectric actuators and may cause system instability. Therefore, a complex nonlinear hysteresis mathematical model with a high degree of fit was established. A Play operator was introduced into the backpropagation neural network, and a genetic algorithm (GA) was used to reduce the probability of the fitting of the neural network model falling into a local minimum. Moreover, simulation and experimental test platforms were constructed. The results showed that the maximum displacement of the actuator was 558.3 μm under a driving voltage of 150 V and a driving frequency of 1 Hz. The complex GA-BP neural network model of the piezoelectric actuator not only exhibited high modeling accuracy but also solved the problems of strong randomness and slow convergence. Compared with other control algorithms, the GA-BP fuzzy PID control exhibited higher control precision.

show abstract

Compensation of Hysteresis in the Piezoelectric Nanopositioning Stage under Reciprocating Linear Voltage Based on a Mark-Segmented PI Model

Cited by 6 publications

References 31 publications

Hysteresis Compensation of Dynamic Systems Using Neural Networks

Hysteresis Compensation of Dynamic Systems Using Neural Networks

A Novel Compliant 2-DOF Ejector Pin Mechanism for the Mass Transfer of Robotic Mini-LED Chips

Modeling and Control of a Linear Piezoelectric Actuator

Contact Info

Product

Resources

About