RNN for Receding Horizon Control of Redundant Robot Manipulators

Yan, Jingkun; Jin, Long; Zhou, Yuan; Liu, Zhiyi

doi:10.1109/tie.2021.3062257

Cited by 35 publications

(8 citation statements)

References 39 publications

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“…The sum of the losses across all the time steps would thus be the overall loss for a particular series of x and y values. We suppose that the vector of probabilities over the output was obtained by using the ''outputs o(t)'' in the softmax function [37]. We also suppose that, given the current input, the loss L is the ''negative loglikelihood'' of the genuine goal y(t).…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

Body-Worn Sensors for Recognizing Physical Sports Activities in Exergaming via Deep Learning Model

et al. 2023

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Obesity and laziness are some of the common issues in the majority of the youth today. This has led to the development of a proposed exergaming solution where users can play first-person physical games. This research study not only proposes a solution for physical fitness in the form of a game using wearable sensors but also proposes a multi-purpose system that provides different applications when trained for the domain-specific dataset. Critical tasks of gesture recognition and depiction in virtual reality can be applied to many applications in the domains of crime detection, fitness, healthcare, online learning, and sports. In particular, the proposed system enables a user to perform, detect, and depict different gestures in the virtual reality game. First, the system pre-processes input data by applying a median filter to overcome the anomalies. Then, features are extracted through a convolutional neural network, power spectral density, skewness, and kurtosis methods. Further, the system optimizes different features by using the grey wolf optimization. Lastly, the feature set which is optimized is fed to a recurrent neural network for classification. When Compared to the traditional methods, the suggested system gives better results while being easier to use. The IMSporting behaviors (IMSB) dataset includes badminton and other physical activities, the WISDM dataset includes common locomotor motions, and the ERICA dataset which includes a variety of exercises, were used in the experimentation. According to experimental findings, the suggested approach outperformed current methods, which showed detection accuracies of 85.01%, 88.46%, and 93.18% over the IMSB, WISDM, and ERICA datasets, respectively.

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Section: Recurrent Neural Networkmentioning

confidence: 99%

Body-Worn Sensors for Recognizing Physical Sports Activities in Exergaming via Deep Learning Model

et al. 2023

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“…When a is in the range of [1,2], the algorithm is in the exploration state. The exploitation state lasts when a is in the range of [0, 1).…”

Section: The Design Process Of the Adaptive Neural Controller Using G...mentioning

confidence: 99%

“…Adaptive control theory is one of the many fields explored by scientists. Among the techniques applied in this field, neural networks (NNs) are the fastest-growing group-they find use in robotics [1], optimization of complex control schemes, e.g., predictive control [2], or combinations with other intelligent structures such as fuzzy systems [3]. They ensure a model-free design process and recalculation of internal coefficients under changes of the operating point.…”

Section: Introductionmentioning

confidence: 99%

Grey Wolf Optimizer in Design Process of the Recurrent Wavelet Neural Controller Applied for Two-Mass System

2022

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In this paper, an adaptive speed controller of the electrical drive is presented. The main part of the control structure is based on the Recurrent Wavelet Neural Network (RWNN). The mechanical part of the plant is considered as an elastic connection of two DC machines. Oscillation damping and robustness against parameter changes are achieved using network parameters updates (online). Moreover, the various combinations of the feedbacks from the state variables are considered. The initial weights of the neural network and the additional gains are tuned using a modified version of the Grey Wolf Optimizer. Convergence of the calculation is forced using a new definition. For theoretical analysis, numerical tests are presented. Then, the RWNN is implemented in a dSPACE card. Finally, the simulation results are verified experimentally.

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“…Nowadays, with the rapid increase in data processing rates, many static QP problems have become time-varying, which require an online solving. Investigating the time-varying constrained QP (TVCQP) problem and designing effective solving methods for them have been a hot topic [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

An activated variable parameter gradient‐based neural network for time‐variant constrained quadratic programming and its applications

Wang

Hao

et al. 2023

CAAI Trans on Intel Tech

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This study proposes a novel gradient‐based neural network model with an activated variable parameter, named as the activated variable parameter gradient‐based neural network (AVPGNN) model, to solve time‐varying constrained quadratic programming (TVCQP) problems. Compared with the existing models, the AVPGNN model has the following advantages: (1) avoids the matrix inverse, which can significantly reduce the computing complexity; (2) introduces the time‐derivative of the time‐varying parameters in the TVCQP problem by adding an activated variable parameter, enabling the AVPGNN model to achieve a predictive calculation that achieves zero residual error in theory; (3) adopts the activation function to accelerate the convergence rate. To solve the TVCQP problem with the AVPGNN model, the TVCQP problem is transformed into a non‐linear equation with a non‐linear compensation problem function based on the Karush Kuhn Tucker conditions. Then, a variable parameter with an activation function is employed to design the AVPGNN model. The accuracy and convergence rate of the AVPGNN model are rigorously analysed in theory. Furthermore, numerical experiments are also executed to demonstrate the effectiveness and superiority of the proposed model. Moreover, to explore the feasibility of the AVPGNN model, applications to the motion planning of a robotic manipulator and the portfolio selection of marketed securities are illustrated.

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RNN for Receding Horizon Control of Redundant Robot Manipulators

Cited by 35 publications

References 39 publications

Body-Worn Sensors for Recognizing Physical Sports Activities in Exergaming via Deep Learning Model

Body-Worn Sensors for Recognizing Physical Sports Activities in Exergaming via Deep Learning Model

Grey Wolf Optimizer in Design Process of the Recurrent Wavelet Neural Controller Applied for Two-Mass System

An activated variable parameter gradient‐based neural network for time‐variant constrained quadratic programming and its applications

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