scholarly journals Neural network modeling and single-neuron proportional–integral–derivative control for hysteresis in piezoelectric actuators

2019 ◽  
Vol 52 (9-10) ◽  
pp. 1362-1370 ◽  
Author(s):  
Yuen Liang ◽  
Suan Xu ◽  
Kaixing Hong ◽  
Guirong Wang ◽  
Tao Zeng
Keyword(s):  
Neural Network ◽  
Single Neuron ◽  
Piezoelectric Actuators ◽  
Neural Network Modeling ◽  
Proportional Integral Derivative ◽  
Polynomial Fitting ◽  
Proportional Integral ◽  
The Neural Network ◽  
Fitting Method ◽  
One To One

A new polynomial fitting model based on a neural network is presented to characterize the hysteresis in piezoelectric actuators. As hysteresis is multi-valued mapping, and traditional neural networks can only solve one-to-one mapping, a hysteresis mathematical model is proposed to expand the input of the neural network by converting the multi-valued into one-to-one mapping. Experiments were performed under designed excitation with different driven voltage amplitudes to obtain the parameters of the model using the polynomial fitting method. The simulation results were in good accordance with the measured data and demonstrate the precision with which the model can predict the hysteresis. Based on the proposed model, a single-neuron adaptive proportional–integral–derivative controller combined with a feedforward loop is designed to correct the errors induced by the hysteresis in the piezoelectric actuator. The results demonstrate superior tracking performance, which validates the practicability and effectiveness of the presented approach.

scholarly journals Application of neural network to building environmental prediction and control

2019 ◽  
Vol 41 (1) ◽  
pp. 25-45
Author(s):  
Ruocheng Li ◽  
Xiangpeng Zhang ◽  
Le Liu ◽  
Yifan Li ◽  
Qingyang Xu
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Environmental Parameters ◽  
Proportional Integral Derivative ◽  
Soft Sensing ◽  
Proportional Integral ◽  
Intelligent Buildings ◽  
The Neural Network ◽  
Prediction And Control ◽  
And Control

Energy conservation, environmental protection, and intelligence are topics of interest in intelligent buildings. However, the energy requirement of various electrical equipment in intelligent buildings increases energy consumption. This study presents a neural network-based prediction and control system for the regulation of building environmental parameters. Neural network-based soft sensing technology can detect building environmental parameters through few sensors. The proposed system control algorithm can realize the adaptive adjustment of environmental parameters by using a neural network proportional–integral–derivative controller. Zigbee wireless communication is adopted as the information transmission medium to realize the environmental parameter measurement and network control. The soft sensing technique combined with Zigbee communication technology can effectively reduce energy consumption. The central control system analyzes the data coming from the network and regulates the environmental parameter through lifting temperature, ventilation, and switching curtains by using the neural network proportional–integral–derivative algorithm. The regulation of environmental parameters reduces unnecessary energy consumption. Finally, the effectiveness of the system is verified through simulations. Practical applications: This work reports an energy saving scheme. The building communication system constructed by ZigBee can reduce energy consumption and can be easily expanded. The soft sensing technique based on artificial neural network can predict temperatures by using few sensors. The neural network proportional–integral–derivative control algorithm has good performance in the regulation of environmental parameters for a time-varying system. Building energy consumption can be reduced by conducting these measures.

Generalized regression neural network modeling based on inverse Duhem operator and adaptive sliding mode control for hysteresis in piezoelectric actuators

2021 ◽  
pp. 095965182110646
Author(s):  
Suan Xu ◽  
Zeyu Wu ◽  
Jing Wang ◽  
Kaixing Hong ◽  
Kaiming Hu
Keyword(s):  
Neural Network ◽  
Piezoelectric Actuator ◽  
Sliding Mode ◽  
Piezoelectric Actuators ◽  
Adaptive Sliding Mode Control ◽  
Neural Network Modeling ◽  
Generalized Regression Neural Network ◽  
Adaptive Sliding Mode ◽  
Generalized Regression ◽  
One To One

A dynamic generalized regression neural network model based on inverse Duhem operator is proposed to characterize the rate-dependent hysteresis in piezoelectric actuators. As hysteresis is multi-valued mapping, and traditional neural network can only model the system with one-to-one mapping. An inverse Duhem operator is proposed to extract the dynamic property of the hysteresis. Moreover, it can transform the multi-valued mapping of the hysteresis into a one-to-one mapping to suit the input of neural network. In order to compensate the effect of the hysteresis in piezoelectric actuator, the adaptive sliding mode controller with a feedforward hysteresis compensator is developed for the tracking control of the piezoelectric actuator. Experimental results demonstrate superior tracking performance, which validate the practicability and effectiveness of the presented approach.

PIDNN control for Vernier-gimballing magnetically suspended flywheel under nonlinear change of stiffness and disturbance

2020 ◽  
pp. 095965182097757
Author(s):  
Jiqiang Tang ◽  
Mengyue Ning ◽  
Xu Cui ◽  
Tongkun Wei ◽  
Xiaofeng Zhao
Keyword(s):  
Neural Network ◽  
Attitude Control ◽  
Magnetic Force ◽  
Magnetic Bearing ◽  
Network Control ◽  
Gradient Descent Method ◽  
Neural Network Control ◽  
Proportional Integral Derivative ◽  
Tracking Accuracy ◽  
Proportional Integral

Vernier-gimballing magnetically suspended flywheel is often used for attitude control and interference suppression of spacecrafts. Due to the special structure of the conical magnetic bearing, the radial component generated by the axial magnetic force and the change of the magnetic air gap will cause the nonlinearity of stiffness and disturbance. That will lead to not only poor stability of the suspension control system but also unsatisfactory tracking accuracy of the rotor position. To solve the nonlinear problem of the system, this article proposes a proportional–integral–derivative neural network control scheme. First, the rotor model considering the nonlinear variation of disturbance and stiffness parameters is established. Then, the weight of neural network is adjusted by the gradient descent method online to ensure the accurate output of magnetic force. Finally, the convergence analysis is carried out based on the Lyapunov stability theory. Compared with the general proportional–integral–derivative control and the radial basis function neural network control, the simulation results demonstrate that the proposed method has the highest tracking accuracy and excellent performance in improving stability. The experimental results prove the correctness of the theoretical analysis and the validity of the proposed method.

scholarly journals Development of precision blank carriage for manufacturing large gratings and echelles

2019 ◽  
Vol 11 (1) ◽  
pp. 168781401881954
Author(s):  
Xinfang Ge ◽  
Biao Chu
Keyword(s):  
High Performance ◽  
Single Neuron ◽  
Dynamic Properties ◽  
Hertzian Contact ◽  
Ball Screw ◽  
Proportional Integral Derivative ◽  
Precision Positioning ◽  
Long Distance ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral

A long-travel positioning device, which combines a ball-screw-driven coarse stage with a fine one, driven by piezoelectric translator, to achieve the long-distance traveling, up to 500 mm, as well as high-precision positioning, is crucial in the field of diffraction grating fabrication at nanometer scale. In this article, we present the design of the fine-feed drive stage with resolution as high as 20 nm and propose a single neuron-based proportional–integral–derivative controller to realize ultra-precision positioning. A high-performance piezoelectric translator is used to drive the mechanism, and the parallel leaf springs are used to guide the moving platform with preload force. A dynamic model of the precision positioning mechanism has been established by considering the Hertzian contact. In addition, the static and dynamic properties are investigated with the laser interferometry tracking methodology. The experimental results indicate that the positioning accuracy of less than 10 nm is obtained with the single neuron-based proportional–integral–derivative controller and also demonstrate the excellent performance of the proposed mechanism and control strategy.

scholarly journals SUPERIORITY OF ARTIFICIAL NEURAL NETWORKS OVER STATISTICAL METHODS IN PREDICTION OF THE OPTIMAL LENGTH OF ROCK BOLTS

2012 ◽  
Vol 18 (5) ◽  
pp. 655-661 ◽  
Author(s):  
Hadi Hasanzadehshooiili ◽  
Ali Lakirouhani ◽  
Jurgis Medzvieckas
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Statistical Models ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Rock Bolts ◽  
Optimal Length ◽  
The Neural Network ◽  
Rock Bolting ◽  
Geological Conditions

Rock bolting is one of the most important support systems used for rock structures. Rock bolts are widely used in underground excavations as they are suitable for a wide range of geological conditions and allow using progressive design methods; besides, they help economising in the use of materials and manpower. Thus, to provide the most effective support at minimum cost by means of rock bolting, it is essential to optimise the elements contributing to bolt design, including their length, as well as bolt density and tension during installation. This paper considers the length of bolts for optimisation of the design phase, which is one of the most important parameters impacting the entire design procedure. Presenting and comparing results of some statistical models, neural network modeling is introduced as powerful means in prediction of the optimal length of rock bolts. Subsequent to training and testing of a large number of 1-layer and 2-layer backpropagation neural networks, it was reported that the optimal model was the network with the architecture of 6-18-3-1 as it demonstrated the minimum RMSE and MAE as well as the maximum R2. In comparison to statistical models (0.7182 for the value of R2 in the multiple linear regression model, 0.68 in the polynomial model and 0.7 in the dimensionless model), the results obtained by the neural network modeling – i.e. the coefficient of determination R2 of 0.9259, the value of mean absolute error MAE of 0.068, and the root mean squared error RMSE of 0.078 – not only proved their superiority but also introduced the neural network modelling as a highly capable prediction tool in forecasting the optimal length of rock bolts. Furthermore, sensitivity analysis was used to obtain parameters that have the greatest and the least impact on the optimal bolt length: the effect of the overburden thickness, tensile strength, cohesion and Poisson's ratio on the optimal bolt length was almost the same while the friction angle had the least influence.

scholarly journals FORECASTING OF THE SURFACE ROUGHNESS IN FINISHING MILLING USING NEURAL NETWORKS

2019 ◽  
Vol 4 (10) ◽  
pp. 135-141
Author(s):  
Е. Ерыгин ◽  
E. Erygin ◽  
Т. Дуюн ◽  
T. Duyun
Keyword(s):  
Neural Network ◽  
Cutting Tool ◽  
Learning Algorithm ◽  
Network Modeling ◽  
Training Sample ◽  
Neural Network Modeling ◽  
Network Learning ◽  
The Neural Network ◽  
And Training ◽  
Positive Results

This article describes the task of predicting roughness when finishing milling using neural network modeling. As a basis for the creation and training of an artificial neural network, a progressive formu-la for determining the roughness during finishing milling is chosen. The thermoEMF of the processing and processed materials is used as one of the parameters for calculating the roughness. The use of thermoEMF allows to take into account the material of the workpiece and the cutting tool, which af-fects the accuracy of the results. A training sample is created with data for five inputs and one output. The architecture, features and network learning algorithm are described. A neural network that de-termines the roughness for finishing milling has been created and configured. The process of learning and debugging of the neural network by means of graphs is clearly displayed. The network operability is checked on the test data, which allows obtaining positive results.

scholarly journals Neural network modeling of the rheology of the AlMg6 alloy under the dispersoid barrier effect and the inhibition of dynamic relaxation processes

2020 ◽  
pp. 10-26
Author(s):  
A. S. Smirnov ◽  
◽  
A. V. Konovalov ◽  
V. S. Kanakin ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Strain Rate ◽  
Strain Rate Range ◽  
Neural Network Modeling ◽  
Relaxation Processes ◽  
Dynamic Relaxation ◽  
Rate Range ◽  
The Neural Network ◽  
Hidden Layer ◽  
Temperature Strain

The paper deals with a neural network to model the flow stress of the AlMg6 alloy at temperatures ranging between 300 and 500 C and strain rates from 1 to 25 s−1. In this temperature–strain-rate range, the movement of free dislocations is blocked and dynamic relaxation processes are inhibited. The results of training the neural network and its verification at a temperature not used in the training show that neural networks with a single hidden layer can correctly approximate and predict the rheological behavior of the AlMg6 alloy for the studied temperature–strain-rate range of deformation.

Electric power steering nonlinear problem based on proportional–integral–derivative parameter self-tuning of back propagation neural network

2020 ◽  
Vol 234 (23) ◽  
pp. 4725-4736 ◽  
Author(s):  
Yuanyuan Li ◽  
Guofei Wu ◽  
Liqun Wu ◽  
Shaotang Chen
Keyword(s):  
Neural Network ◽  
Electric Power ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Steering System ◽  
Proportional Integral Derivative ◽  
Electric Power Steering ◽  
Proportional Integral ◽  
Power Steering ◽  
Electric Power Steering System

Aiming at the problem of nonlinear power steering in the automobile electric power steering system, an advanced control algorithm is required for the practical system. This paper introduces back propagation neural network arbitrary nonlinear approximations to discretize the vehicle’s power assistance characteristic. Steering power is also realized in the whole range of speed, which overcomes the steering blind zone and lays a foundation for the design of subsequent controllers. In addition, considering the nonlinear frictional resistance problem of the electric power steering system, the traditional proportional–integral–derivative remote control algorithm will result in poor dynamic performance or system instability. Therefore, this paper proposes a control algorithm based on back propagation neural network proportional–integral–derivative parameter self-tuning. Using the error between the expected current and the actual motor current, the back propagation neural network algorithm is used to learn and realize the adaptive adjustment of proportional–integral–derivative parameters. Simulation results show that the proposed control system effectively realizes the nonlinear steering assistance in the whole vehicle range speed, eliminates the influence of nonlinear friction in the electric power steering system, and improves the robustness of the control system.

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