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Enhanced PID: Adaptive Feedforward RBF Neural Network Control of Robot manipulators with an Optimal Distribution of Hidden Nodes
[article]
2020
arXiv
pre-print
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This paper proposes an adaptive feedforward RBFNN control strategy with an optimal distribution of hidden nodes. ...
Adaptive feedforward RBFNN control with lattice distribution of hidden node can improve solve the demerits 1) but just improve demerits 2) and 3) slightly. ...
This paper proposes an adaptive feedforward RBFNN control strategy with an optimal distribution of hidden nodes. ...
arXiv:2005.11501v1
fatcat:de7kcbx5qresjgab7wysoduike
An Enhanced Full-Form Model-Free Adaptive Controller for SISO Discrete-Time Nonlinear Systems
2022
Entropy
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This study focuses on the full-form model-free adaptive controller (FFMFAC) for SISO discrete-time nonlinear systems, and proposes enhanced FFMFAC. ...
EFFMFAC is characterized by utilizing the measured I/O data for the online training of all introduced neural networks and does not involve offline training and specific models of the controlled system. ...
[22] introduced adaptive neural networks (NNs) for control design to suppress the vibrations of a flexible robotic manipulator. ...
doi:10.3390/e24020163
pmid:35205458
pmcid:PMC8871481
fatcat:gwcbqxmigrfhda7blha4qfdg3q
End-Effector Position Estimation and Control of a Flexible Interconnected Industrial Manipulator Using Machine Learning
2022
IEEE Access
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an Ascending distribution of its nodes achieves the best prediction and generalization to unseen environments (the upper bound of the error was 0.15 × 10 −3 m); implying the robust estimation of the position ...
as Neural Networks (NN), Support Vector Machines (SVM), and Gaussian Process (GP) in estimating the deflection error arising due to the flexibility of the robot structure. ...
, and (2) a feedforward NN with 535 neurons with an ascending distribution of its nodes in 6 hidden layers achieves the best prediction and generalization to unseen environments. ...
doi:10.1109/access.2022.3157817
fatcat:k3wl5fhau5hk7jsr74il5al5xa
Pitch Control of Three Bladed Large Wind Energy Converters—A Review
2021
Energies
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Thus, approaches like collective and individual pitch control, tower and blade damping control, and pitch actuator control must coexist in an integrated control system. ...
The present work summarizes control strategies for problem of wind turbines, which are solved by using different approaches of pitch control. ...
In the case of RBF networks, it is more common to find hybrid approaches. For example, an RBF/PI configuration can be found in [49] , an RBF/PID in [50] , and an RBF/FOPID in [51] . ...
doi:10.3390/en14238083
fatcat:kywkawvd75b7vdoy7l2vjqecb4
Parallel Robot
2022
figshare.com
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In the beginning, with an introduction regarding the advantages historical overview and various types of parallel manipulators. ...
analysis, dynamic analysis, robotic components, materials and manufacturing, modelling and simulation, artificial neural networks, control, experimental measurements and new devices, calibration, and ...
the B-spline neural network method to design a PID controller. ...
doi:10.6084/m9.figshare.20173295.v1
fatcat:xytrucsq3jexxe2ffcureua7x4
Computational Intelligence in the Context of Industry 4.0
[chapter]
2021
Implementing Industry 4.0 in SMEs
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The ending part of the chapter is focused on connecting theory and practice in a case study, which lists industrial parts recognition by convolutional neural networks for assisted assembly. ...
This chapter is focused on Computational Intelligence (CI) in the context of Industry 4.0. Each subchapter provides fundamentals of some paradigms, followed by the use of CI in the concrete paradigm. ...
Wong and Yu (2019) used an optimization algorithm to minimize path following error based on Lyapunov direct method controller with RBF neural network estimator. ...
doi:10.1007/978-3-030-70516-9_2
fatcat:3bgkvenwcvcqvnlfmhmjeoyury
Population based Mean of Multiple Computations networks: A building block for kinematic models
2015
2015 International Joint Conference on Neural Networks (IJCNN)
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Following the Mean of Multiple Computations principle, a neural network model (PbMMC) is presented in which the overall complexity is divided into multiple local relationships. ...
Such a representation will be introduced and applied for the case of a redundant manipulator. ...
By learning the weights of the network and the centers as well as deviations of the RBFs, their approach outperformed an MLP network as well as a PID controller. ...
doi:10.1109/ijcnn.2015.7280791
dblp:conf/ijcnn/BaumMS15
fatcat:vuf5waidyvc35bgotb6b2fdqke
Energy-Efficient Robust Control for Direct Drive and Energy Recuperation Hydraulic Servo System
2020
Complexity
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To compensate for the uncertainties, both in the variable supply pressure control circuit and the robust controller, the RBF neural network is employed to approximate the unknown function. ...
Moreover, the optimized parameters are obtained by the simulated annealing algorithm. ...
the RBF neural network, W * is the ideal weight, H is the output of the Gaussian basis function, j is the number of nodes in the hidden layer, ε is the approximate error of the RBF neural network, and ...
doi:10.1155/2020/6959273
fatcat:cp7jsowzcfa27algk7ro7kvgqu
Multi-grid cellular genetic algorithm for optimizing variable ordering of ROBDDs
2012
2012 IEEE Congress on Evolutionary Computation
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The approach systematically produces better results than the used basic genetic algorithm and better or similar results with other heuristic methods. ...
This paper presents a cellular genetic algorithm for optimizing the variable order in Reduced Ordered Binary Decision Diagrams. The evolution process is inspired by a basic genetic algorithm. ...
, Ya-Fu Peng and Chih-Hui Chiu, Adaptive Dynamic TSKCMAC Neural Networks for Prediction and Identification 582, Goktug Cinar and Jose Principe, Hidden State Estimation using the Correntropy Filter with ...
doi:10.1109/cec.2012.6256590
dblp:conf/cec/RotaruB12
fatcat:4ly3nrktw5habc6lf5err7d5py
A review of modularization techniques in artificial neural networks
2019
Artificial Intelligence Review
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Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2019 pre-print arXiv:1904.12770v1 fatcat:uk7ck3ferrgplfyirjiku3srey
Modular neural networks (MNNs) are neural networks that embody the concepts and principles of modularity. MNNs adopt a large number of different techniques for achieving modularization. ...
to emphasise the strengths and weaknesses of different modularization approaches in order to highlight good practices for neural network practitioners. ...
Acknowledgements This is a pre-print of an article published in Artificial Intelligence Review. The final authenticated version is available online at: https://doi.org/10.1007/s10462-019-09706-7 ...
doi:10.1007/s10462-019-09706-7
fatcat:g4xp6dktvncu5dao53dcvoexoa
Model Predictive Control of Internal Combustion Engines: A Review and Future Directions
2021
Energies
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Methods of model predictive control (MPC) have shown promising results for real-time multi-objective optimal control of constrained multi-variable nonlinear systems, including ICEs. ...
ICE control and calibration can be enhanced by taking advantage of the recent developments in the field of Artificial Intelligence (AI) in applying Machine Learning (ML) to large-scale engine data. ...
Funds from Natural Sciences and Engineering Research Council of Canada (NSERC) and Canada First Research Excellence (CFRE) are also gratefully acknowledged. ...
doi:10.3390/en14196251
fatcat:qhkjp37py5fjbdpoh47zraws7u
IEEE Robotics & Automation Society
2012
IEEE robotics & automation magazine
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The proposed architecture comprises of an on-line Radial Basis Function (RBF) neural network identifier and a controller, with the signals issued by the latter guaranteeing the satisfaction of a Persistency ...
The feedforward control is utilized to enhance the operability of the control system to high disturbances. ...
doi:10.1109/mra.2012.2230568
fatcat:33actbknxrel3jnag2kx7cncem
IEEE Robotics & Automation Society
2011
IEEE robotics & automation magazine
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The proposed architecture comprises of an on-line Radial Basis Function (RBF) neural network identifier and a controller, with the signals issued by the latter guaranteeing the satisfaction of a Persistency ...
The feedforward control is utilized to enhance the operability of the control system to high disturbances. ...
doi:10.1109/mra.2011.941112
fatcat:owvu2behc5hulpcae2dp5myigm
[IEEE Robotics & Automation Society]
2012
IEEE robotics & automation magazine
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
The proposed architecture comprises of an on-line Radial Basis Function (RBF) neural network identifier and a controller, with the signals issued by the latter guaranteeing the satisfaction of a Persistency ...
The feedforward control is utilized to enhance the operability of the control system to high disturbances. ...
doi:10.1109/mra.2012.2229854
fatcat:rjrxtwk4jbcgjpvjdad6mougsq
IEEE Robotics & Automation Society
2011
IEEE robotics & automation magazine
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
The proposed architecture comprises of an on-line Radial Basis Function (RBF) neural network identifier and a controller, with the signals issued by the latter guaranteeing the satisfaction of a Persistency ...
The feedforward control is utilized to enhance the operability of the control system to high disturbances. ...
doi:10.1109/mra.2011.943480
fatcat:d2wvloyv6jcbzp2yathd52mx2u
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