scholarly journals Adaptive Control Structure with Neural Data Processing Applied for Electrical Drive with Elastic Shaft

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3389
Author(s):  
Marcin Kamiński ◽  
Krzysztof Szabat
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Dynamic Properties ◽  
Experimental Studies ◽  
Drive System ◽  
Particle Swarm Optimizer ◽  
The Neural Network ◽  
Control Concept ◽  
Constant Coefficients ◽  
Selection Of

This paper presents issues related to the adaptive control of the drive system with an elastic clutch connecting the main motor and the load machine. Firstly, the problems and the main algorithms often implemented for the mentioned object are analyzed. Then, the control concept based on the RNN (recurrent neural network) for the drive system with the flexible coupling is thoroughly described. For this purpose, an adaptive model inspired by the Elman model is selected, which is related to internal feedback in the neural network. The indicated feature improves the processing of dynamic signals. During the design process, for the selection of constant coefficients of the controller, the PSO (particle swarm optimizer) is applied. Moreover, in order to obtain better dynamic properties and improve work in real conditions, one model based on the ADALINE (adaptive linear neuron) is introduced into the structure. Details of the algorithm used for the weights’ adaptation are presented (including stability analysis) to perform the shaft torque signal filtering. The effectiveness of the proposed approach is examined through simulation and experimental studies.

A New Adaptive Control Scheme Using Dynamic Neural Networks

2008 ◽  
Vol 20 (1) ◽  
pp. 171-177 ◽  
Author(s):  
Khaled Nouri ◽  
◽  
Rached Dhaouadi ◽  
Naceur Benhadj Braiek ◽  
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Inverse Dynamics ◽  
Drive System ◽  
Motor Drive ◽  
Dynamic Neural Networks ◽  
The Neural Network ◽  
Control Scheme ◽  
On Line ◽  
Motor Drive System

A new adaptive neuro-control structure is proposed for the speed control of a nonlinear motor drive system and the compensation of the nonlinearities. A dynamic artificial neural network is used for the on-line adaptive control of the nonlinear motor drive system with high static and Coulomb friction. The neural network is first trained off-line to learn the inverse dynamics of the motor drive system using a layer decoupled extended Kalman filter algorithm. The proposed control scheme is validated experimentally on a dc motor drive system using a standard personal computer. The results obtained confirm the excellent tracking performance and disturbance rejection properties of the system.

scholarly journals Robotic grasp detection using a novel two-stage approach

2021 ◽  
Vol 1 (1) ◽  
pp. 19-29
Author(s):  
Zhe Chu ◽  
Mengkai Hu ◽  
Xiangyu Chen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Models ◽  
Particle Swarm Optimizer ◽  
Neural Network Models ◽  
Two Stage ◽  
The Neural Network ◽  
End To End ◽  
Small Change ◽  
Robotic Grasp

Recently, deep learning has been successfully applied to robotic grasp detection. Based on convolutional neural networks (CNNs), there have been lots of end-to-end detection approaches. But end-to-end approaches have strict requirements for the dataset used for training the neural network models and it’s hard to achieve in practical use. Therefore, we proposed a two-stage approach using particle swarm optimizer (PSO) candidate estimator and CNN to detect the most likely grasp. Our approach achieved an accuracy of 92.8% on the Cornell Grasp Dataset, which leaped into the front ranks of the existing approaches and is able to run at real-time speeds. After a small change of the approach, we can predict multiple grasps per object in the meantime so that an object can be grasped in a variety of ways.

scholarly journals Weighted Multiple-Model Neural Network Adaptive Control for Robotic Manipulators with Jumping Parameters

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Jiazhi Li ◽  
Weicun Zhang ◽  
Quanmin Zhu
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
External Disturbance ◽  
Lyapunov Stability Theory ◽  
Robotic Manipulators ◽  
Adaptive Observer ◽  
Multiple Model ◽  
Neural Network Controller ◽  
The Neural Network ◽  
The Stability

This study addresses the tracking control issue for n-link robotic manipulators with largely jumping parameters. Based on radial basis function neural networks (RBFNNs), we propose weighted multiple-model neural network adaptive control (WMNNAC) approach. To cover the variation ranges of the parameters, different models of robotic are constructed. Then, the corresponding local neural network controller is constructed, in which the neural network has been used to approximate the uncertainty part of the control law, and an adaptive observer is implemented to estimate the true external disturbance. The WMNNAC strategy with improved weighting algorithm is adopted to ensure the tracking performance of the robotic manipulator system when parameters jump largely. Through the Lyapunov stability theory and the method of virtual equivalent system (VES), the stability of the closed-loop system is proved. Finally, the simulation results of a two-link manipulator verify the feasibility and efficiency of the proposed WMNNAC strategy.

STDP Provides the Substrate for Igniting Synfire Chains by Spatiotemporal Input Patterns

2008 ◽  
Vol 20 (2) ◽  
pp. 415-435 ◽  
Author(s):  
Ryosuke Hosaka ◽  
Osamu Araki ◽  
Tohru Ikeguchi
Keyword(s):  
Neural Network ◽  
Experimental Studies ◽  
Spike Timing ◽  
Inhibitory Neurons ◽  
Spatiotemporal Pattern ◽  
Spiking Neural Network ◽  
Synfire Chains ◽  
The Neural Network ◽  
Stdp Rule ◽  
Neural Network Structure

Spike-timing-dependent synaptic plasticity (STDP), which depends on the temporal difference between pre- and postsynaptic action potentials, is observed in the cortices and hippocampus. Although several theoretical and experimental studies have revealed its fundamental aspects, its functional role remains unclear. To examine how an input spatiotemporal spike pattern is altered by STDP, we observed the output spike patterns of a spiking neural network model with an asymmetrical STDP rule when the input spatiotemporal pattern is repeatedly applied. The spiking neural network comprises excitatory and inhibitory neurons that exhibit local interactions. Numerical experiments show that the spiking neural network generates a single global synchrony whose relative timing depends on the input spatiotemporal pattern and the neural network structure. This result implies that the spiking neural network learns the transformation from spatiotemporal to temporal information. In the literature, the origin of the synfire chain has not been sufficiently focused on. Our results indicate that spiking neural networks with STDP can ignite synfire chains in the cortices.

Study on RBF Neural Network PID Control for Hydro-Viscous Drive System

2013 ◽  
Vol 470 ◽  
pp. 668-672
Author(s):  
Qing Rui Meng ◽  
Kai Wang ◽  
Dao Ming Wang ◽  
Jian Wang ◽  
Bao Cheng Song ◽  
...  
Keyword(s):  
Neural Network ◽  
Error Rate ◽  
Control Strategy ◽  
Pid Control ◽  
Rbf Neural Network ◽  
Drive System ◽  
Output Curve ◽  
Strategy Selection ◽  
Neural Network Pid ◽  
Selection Of

To verify the applicability of RBF neural network PID control on speed regulating start control for hydro-viscous drive system, analyze the principle of RBF neural network PID control, the simulation model is established based on SIMULINK and the control characteristics are analyzed based on the AMESim/MATLAB co-simulation. The results show that RBF neural network PID control has a good self-correcting effect on speed regulating start of hydro-viscous; it can make right judgments according to the error and error rate and adjust the output speed towards opposite direction of error; meanwhile, it ensures the smoothness of output curve and avoids excessive mechanical impact. The results play a guiding role for control strategy selection of speed regulating start.

scholarly journals Selection of the optimal type of thermal insulation structure based on the neural network modelling

2020 ◽  
Vol 216 ◽  
pp. 01037
Author(s):  
Irina Akhmetova ◽  
Elena Balzamova ◽  
Veronika Bronskaya ◽  
Denis Balzamov ◽  
Konstantin Lapin ◽  
...  
Keyword(s):  
Neural Network ◽  
Web Application ◽  
District Heating ◽  
Heat Network ◽  
Network Modelling ◽  
Query Log ◽  
Neural Network Modelling ◽  
The Neural Network ◽  
Selection Of ◽  
Optimal Type

A software package with the user interface for calculating, analyzing and predicting the parameters of cogeneration-based district heating based on the neural network modelling is presented in order to optimize and ensure the reliability of heat networks. The package is the basis for a web-application that allows to calculate the characteristics of the heat network in accordance with the model, keep a query log and provide the possibility of administration.

scholarly journals Assessment of Technical Condition of Polyurethane Foam Thermal Insulation Pipelines of Heating Networks Using Neural Network Technologies

2018 ◽  
Vol 7 (4.7) ◽  
pp. 241 ◽  
Author(s):  
Emil R. Saifullin ◽  
Shamil G. Ziganshin ◽  
Yury V. Vankov ◽  
Airat R. Zagretdinov
Keyword(s):  
Neural Network ◽  
Polyurethane Foam ◽  
Experimental Studies ◽  
Back Propagation ◽  
Acoustic Signals ◽  
Technical Condition ◽  
The Neural Network ◽  
Network Technologies ◽  
Heating Networks ◽  
Neural Network Technologies

Pipelines of heat networks are an important element of heat supply to cities and industrial facilities. To increase the reliability of the operation of pipelines of heating networks, reducing the number of their accidents and increasing the economic parameters of transportation of heat energy, it is required to constantly increase the volumes and quality of complex diagnostics. The instruments currently used for the diagnosis of pipelines have many shortcomings. Among them, low reliability of detection of defects and subjectivity of decision-making, as well as lack of funds for diagnostics of pre-insulated pipelines (in polyurethane foam insulation). To simplify, accelerate and improve the reliability of monitoring the technical condition of pipelines, the authors set the goal of diagnosing the object of research using acoustic methods, using neural network technologies to process acoustic signals. The article describes experimental studies of pipelines of heating networks in polyurethane foam insulation with various sizes of defects and an analysis of the acoustic signals obtained at the same time is made. The frequency of natural oscillations of the pipeline is chosen as the determining parameter of the acoustic signal. To process and analyze the frequencies obtained as a result of the experiments, a neural network of back propagation of the error was constructed.The results of the classification of the neural network of back propagation of the error trained by the neural network showed its good ability to analyze unknown samples and a high degree of reliability of their recognition.   

Sign in / Sign up

Export Citation Format

Share Document