Hysteresis modelling and compensation for piezoelectric actuator using Jaya-BP neural network

2021 ◽  
pp. 095440622110127
Author(s):  
Nguyen Ngoc Son ◽  
Ho Pham Huy Anh
Keyword(s):  
Neural Network ◽  
Piezoelectric Actuator ◽  
Back Propagation ◽  
Neural Model ◽  
Back Propagation Algorithm ◽  
Feed Forward ◽  
Pzt Actuator ◽  
Nonlinear Hysteresis ◽  
The Neural Network ◽  
Hysteresis Nonlinearity

This paper proposes a new training algorithm using a hybrid Jaya-back propagation algorithm (called H-Jaya) to optimize the neural network weights, which is applied to identify the nonlinear hysteresis Piezoelectric actuator based on the experimental input-output data. The identified H-Jaya-neural model will be used to design an advanced feed-forward (FF) controller for compensating the hysteresis nonlinearity. Furthermore as to improve the tracking performance, a feed-forward-feedback control scheme is conducted. To evaluate the effectiveness of the proposed approach, firstly, it is tested through identifying the nonlinear hysteresis of Piezoelectric (PZT) actuator and compared with other meta-heuristic techniques, including differential evolution (DE), particle swarm optimization (PSO), and Jaya. Then, the accuracy of the hysteresis model-based compensator is evaluated under various control experiments using the piezoelectric actuator. The results of experiments executed on PZT   actuator configured with a PZS001 from Thorlabs prove that the proposed approach obtains an excellent performance in hysteresis modeling and compensation.

scholarly journals Hysteresis Modeling and Compensation of Fast Steering Mirrors with Hysteresis Operator Based Back Propagation Neural Networks

Micromachines ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 732
Author(s):  
Kairui Cao ◽  
Guanglu Hao ◽  
Qingfeng Liu ◽  
Liying Tan ◽  
Jing Ma
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Hysteresis Model ◽  
Cooperative Movement ◽  
Hysteresis Operator ◽  
The Neural Network ◽  
Hysteresis Nonlinearity ◽  
Hysteresis Modeling ◽  
Hysteresis Characteristics ◽  
Inverse Hysteresis

Fast steering mirrors (FSMs), driven by piezoelectric ceramics, are usually used as actuators for high-precision beam control. A FSM generally contains four ceramics that are distributed in a crisscross pattern. The cooperative movement of the two ceramics along one radial direction generates the deflection of the FSM in the same orientation. Unlike the hysteresis nonlinearity of a single piezoelectric ceramic, which is symmetric or asymmetric, the FSM exhibits complex hysteresis characteristics. In this paper, a systematic way of modeling the hysteresis nonlinearity of FSMs is proposed using a Madelung’s rules based symmetric hysteresis operator with a cascaded neural network. The hysteresis operator provides a basic hysteresis motion for the FSM. The neural network modifies the basic hysteresis motion to accurately describe the hysteresis nonlinearity of FSMs. The wiping-out and congruency properties of the proposed method are also analyzed. Moreover, the inverse hysteresis model is constructed to reduce the hysteresis nonlinearity of FSMs. The effectiveness of the presented model is validated by experimental results.

Manipulator Inverse Kinematics using an Adaptive Back-propagation Algorithm and Radial Basis Function with a Lookup Table

Robotica ◽  
1998 ◽  
Vol 16 (4) ◽  
pp. 433-444 ◽  
Author(s):  
A. S. Morris ◽  
M. A. Mansor
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Inverse Kinematics ◽  
Back Propagation ◽  
Lookup Table ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
The Neural Network ◽  
Radial Basis

This is an extension of previous work which used an artificial neural network with a back-propagation algorithm and a lookup table to find the inverse kinematics for a manipulator arm moving along pre-defined trajectories. The work now described shows that the performance of this technique can be improved if the back-propagation is made to be adaptive. Also, further improvement is obtained by using the whole workspace to train the neural network rather than just a pre-defined path. For the inverse kinematics of the whole workspace, a comparison has also been done between the adaptive back-propagation algorithm and radial basis function.

Application of neural networks in industrial production

2021 ◽  
pp. 10-17
Author(s):  
S. S. Yudachev ◽  
N. A. Gordienko ◽  
F. M. Bosy
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Effective Means ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Graphics Processors ◽  
Training Scheme ◽  
Network Training ◽  
The Neural Network

The article describes an algorithm for the synthesis of neural networks for controlling the gyrostabilizer. The neural network acts as an observer of the state vector. The role of such an observer is to provide feedback to the gyrostabilizer, which is illustrated in the article. Gyrostabilizer is a gyroscopic device designed to stabilize individual objects or devices, as well as to determine the angular deviations of objects. Gyrostabilizer systems will be more widely used, as they provide an effective means of motion control with a number of significant advantages for various designs. The article deals in detail with the issue of specific stage features of classical algorithms: selecting the network architecture, training the neural network, and verifying the results of feedback control. In recent years, neural networks have become an increasingly powerful tool in scientific computing. The universal approximation theorem states that a neural network can be constructed to approximate any given continuous function with the required accuracy. The back propagation algorithm also allows effectively optimizing the parameters when training a neural network. Due to the use of graphics processors, it is possible to perform efficient calculations for scientific and engineering tasks. The article presents the optimal configuration of the neural network, such as the depth of memory, the number of layers and neurons in these layers, as well as the functions of the activation layer. In addition, it provides data on dynamic systems to improve neural network training. An optimal training scheme is also provided.

scholarly journals Temporal solar irradiance variability analysis using neural networks

2015 ◽  
Vol 11 (S320) ◽  
pp. 333-338
Author(s):  
Ambelu Tebabal ◽  
Baylie Damtie ◽  
Melessew Nigussie
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Solar Irradiance ◽  
Dominant Role ◽  
Back Propagation ◽  
Total Solar Irradiance ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
Surface Magnetism ◽  
Feed Forward

AbstractA feed-forward neural network which can account for nonlinear relationship was used to model total solar irradiance (TSI). A single layer feed-forward neural network with Levenberg-marquardt back-propagation algorithm have been implemented for modeling daily total solar irradiance from daily photometric sunspot index, and core-to-wing ratio of Mg II index data. In order to obtain the optimum neural network for TSI modeling, the root mean square error (RMSE) and mean absolute error (MAE) have been taken into account. The modeled and measured TSI have the correlation coefficient of about R=0.97. The neural networks (NNs) model output indicates that reconstructed TSI from solar proxies (photometric sunspot index and Mg II) can explain 94% of the variance of TSI. This modeled TSI using NNs further strengthens the view that surface magnetism indeed plays a dominant role in modulating solar irradiance.

scholarly journals A Modified Neural Network system based on Morphological operations for detection of images with variation in Gray level intensity and facial expressions

2012 ◽  
pp. 56-61
Author(s):  
Pratibha Rani ◽  
Anshu Sirohi ◽  
Manish Kumar Singh
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Network System ◽  
Morphological Operations ◽  
Gray Level ◽  
Back Propagation Algorithm ◽  
Translation Invariant ◽  
The Neural Network ◽  
Neural Network System ◽  
Level Shifts

We introduce an algorithm based on the morphological shared-weight neural network. Which extract the features and then classify them. This type of network can work effectively, even if the gray level intensity and facial expression of the images are varied. The images are processed by a morphological shared weight neural network to detect and extract the features of face images. For the detection of the edges of the image we are using sobel operator. We are using back propagation algorithm for the purpose of learning and training of the neural network system. Being nonlinear and translation-invariant, the morphological operations can be used to create better generalization during face recognition. Feature extraction is performed on grayscale images using hit-miss transforms that are independent of gray-level shifts. The recognition efficiency of this modified network is about 98%.

A Neural Network-Based Prediction of the Weld Lines in Resin Transfer Molded Parts

1999 ◽  
Author(s):  
Faezeh Soltani ◽  
Souran Manoochehri
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Simulation Models ◽  
Optimization Methods ◽  
Computational Time ◽  
Back Propagation Algorithm ◽  
Rtm Process ◽  
The Neural Network ◽  
Molded Parts ◽  
The Neural Networks

Abstract A model is developed to predict the weld lines in Resin Transfer Molding (RTM) process. In this model, the preforms are assumed to be thin flat with isotropic and orthotropic permeabilities. The position of the weld lines formed by multiple specified inlet ports are predicted using a neural network-based back propagation algorithm. The neural network was trained with data obtained from simulation and actual molding experimentation. Part geometry is decomposed into smaller sections based on the position of the weld lines. The variety of preforms and processing conditions are used to verify the model. Applying the neural networks reduced the amount of computational time by several orders of magnitude compared with simulations. The models developed in this study can be effectively utilized in iterative optimization methods where use of numerical simulation models is cumbersome.

Neural Network Based Automated System for Diagnosis of Cervical Cancer

2020 ◽  
pp. 1422-1436
Author(s):  
Seema Singh ◽  
V. Tejaswini ◽  
Rishya P. Murthy ◽  
Amit Mutgi
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Cervical Cancer ◽  
Back Propagation ◽  
Accurate Method ◽  
Automated System ◽  
Low Grade ◽  
Back Propagation Algorithm ◽  
The Neural Network ◽  
Processing Techniques

Cervical Cancer is one of the most common cancers among women worldwide. Few concerns have arisen such as the shortage of skilled pathologists leading to increase in burden on them. This requires a need for efficient and accurate method that diagnoses cervical cancer without human intervention. In this paper, an automated system is developed for diagnosis of cervical cancer using image processing techniques and neural networks. The system is developed using Cytology images taken from Bangalore based cancer pathologist. MATLAB image processing toolbox is used to extract features from cytology images that are used for discriminating various stages of cervical cancer. The dominant features used for diagnosis are Nucleus to cytoplasm ratio, shape, and color intensity along with nucleus area, perimeter and eccentricity. These features are used to train the neural network using Back-propagation algorithm of supervised training method. The cytology cells were then successfully classified as non-cancerous, low- grade and high-grade cancer cells.

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