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Enhanced PID: Adaptive Feedforward RBF Neural Network Control of Robot manipulators with an Optimal Distribution of Hidden Nodes [article]

Qiong Liu, Dongyu Li, Shuzhi Sam Ge, Zhong Ouyang, Wei He
2020 arXiv   pre-print
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

Ye Yang, Chen Chen, Jiangang Lu
2022 Entropy  
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

Muhammad Adel, Sabah M. Ahmed, Mohamed Fanni
2022 IEEE Access  
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

Adrian Gambier
2021 Energies  
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

Ahmed Deabs
2022 figshare.com  
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]

Alexander Hošovský, Ján Piteľ, Monika Trojanová, Kamil Židek
2021 Implementing Industry 4.0 in SMEs  
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

Manuel Baum, Martin Meier, Malte Schilling
2015 2015 International Joint Conference on Neural Networks (IJCNN)  
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

Weiping Wang, Jiyun Zhao
2020 Complexity  
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

Cristian Rotaru, Octav Brudaru
2012 2012 IEEE Congress on Evolutionary Computation  
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

Mohammed Amer, Tomás Maul
2019 Artificial Intelligence Review  
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

Armin Norouzi, Hamed Heidarifar, Mahdi Shahbakhti, Charles Koch, Hoseinali Borhan
2021 Energies  
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  
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  
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  
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  
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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