Decentralized Identification and Control in Real-Time of a Robot Manipulator via Recurrent Wavelet First-Order Neural Network

Vázquez, Luis; Jurado, Francisco; Alanís, Alma Y.

doi:10.1155/2015/451049

Cited by 11 publications

(7 citation statements)

References 34 publications

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“…For the application of the filtered error algorithm, this study begins under the assumption of an unknown system (3), which can be modeled (identified) by using an RWFONN structure (5). The synaptic weights are adjusted according to the following update law [23,24]…”

Section: Filtered Error Training Algorithmmentioning

confidence: 99%

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Design of a Neural Super-Twisting Controller to Emulate a Flywheel Energy Storage System

et al. 2021

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In this work, a neural super-twisting algorithm is applied to the design of a controller for a flywheel energy storage system (FESS) emulator. Emulation of the FESS is achieved through driving a Permanent Magnet Synchronous Machine (PMSM) coupled to a shaft to shaft DC-motor. The emulation of the FESS is carried out by controlling the velocity of the PMSM in the energy storage stag and then by controlling the DC-motor velocity in the energy feedback stage, where the plant’s states of both electrical machines are identified via a neural network. For the neural identification, a Recurrent Wavelet First-Order Neural Network (RWFONN) is proposed. For the design of the velocity controller, a super-twisting algorithm is applied by using a sliding surface as the argument; the latter is designed based on the states of the RWFONN, in combination with the block control linearization technique to the control of the angular velocity from both machines in their respective operation stage. The RWFONN is trained online using the filtered error algorithm. Closed-loop stability analysis is included when assuming boundedness of the synaptic weights. The results obtained from Matlab/Simulink validate the performance of the proposal in the control of an FESS.

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Section: Filtered Error Training Algorithmmentioning

confidence: 99%

“…This latter will guarantee that the tracking errors be steered to zero in finite time. From Equation (24), selecting the sliding surface s = x 2 arises the system given by [29] ṡ =a pinv 1…”

Section: Pmsm Parameters Valuementioning

confidence: 99%

Design of a Neural Super-Twisting Controller to Emulate a Flywheel Energy Storage System

et al. 2021

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“…Compared with other wavelets such as Haar wavelet, Mexican Hat wavelet and Daubechies wavelet, Morlet wavelet has better local characteristics in the time domain and frequency domain, which is adopted as the activation function of WNN. [17][18] WNN combines the structure model of neural network with the multi-resolution and multi-scale analysis of signals effectively, so it has better performance in signal direction selection. 19 The introduction of WNN in servo control system has attracted wide attention and research.…”

Section: Introductionmentioning

confidence: 99%

Internal model control for rocket launcher position servo system based on improved wavelet neural network

Wang

Gao

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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This paper proposes an improved wavelet neural network-internal model controller (WNN-IMC) for the rocket launcher position servo system. Due to complex nonlinearities and uncertainties of external disturbances in the rocket launcher position servo system, it is vitally challenging to establish its accurate model by the mechanical modeling technique. A wavelet neural network (WNN) identification method is proposed to determine the system mathematical model through test datum, which optimized by the hybrid algorithm of differential evolution (DE) and particle swarm optimization (PSO). Then, the proposed method is applied to identify the semi-physical simulation platform of the rocket launcher velocity servo system. The results demonstrate that the validity of the DEPSO-WNN method is better than that of the WNN and PSO-WNN methods. Finally, compared with the WNN-IMC controller and the ADRC controller, the effectiveness of the improved WNN-IMC controller is verified by the semi-physical simulation experiments.

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“…Wavelet Neural Networks (WNN) are complex machine learning algorithms that use wavelet analysis and neural networks to generate prediction and control models. WNNs have been applied before in several areas, including time-series prediction and control [ 13 , 14 ]. Evolutionary Wavelet Neural Networks (EWNN) are a recently proposed method for training WNNs and have been used to generate models for breast cancer and Parkinson’s disease classification [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Wavelet Neural Network ensembles for breast cancer and Parkinson’s disease prediction

2018

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Wavelet Neural Networks are a combination of neural networks and wavelets and have been mostly used in the area of time-series prediction and control. Recently, Evolutionary Wavelet Neural Networks have been employed to develop cancer prediction models. The present study proposes to use ensembles of Evolutionary Wavelet Neural Networks. The search for a high quality ensemble is directed by a fitness function that incorporates the accuracy of the classifiers both independently and as part of the ensemble itself. The ensemble approach is tested on three publicly available biomedical benchmark datasets, one on Breast Cancer and two on Parkinson’s disease, using a 10-fold cross-validation strategy. Our experimental results show that, for the first dataset, the performance was similar to previous studies reported in literature. On the second dataset, the Evolutionary Wavelet Neural Network ensembles performed better than all previous methods. The third dataset is relatively new and this study is the first to report benchmark results.

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Decentralized Identification and Control in Real-Time of a Robot Manipulator via Recurrent Wavelet First-Order Neural Network

Cited by 11 publications

References 34 publications

Design of a Neural Super-Twisting Controller to Emulate a Flywheel Energy Storage System

Design of a Neural Super-Twisting Controller to Emulate a Flywheel Energy Storage System

Internal model control for rocket launcher position servo system based on improved wavelet neural network

Evolutionary Wavelet Neural Network ensembles for breast cancer and Parkinson’s disease prediction

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