Kinematics Analysis of 7-DOF Upper Limb Rehabilitation Robot Based on BP Neural Network

Pang, Zaixiang; Wang, Tongyu; Liu, Shuai; Wang, Zhanli; Gong, Linan

doi:10.1109/ddcls49620.2020.9275138

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…Repeated training is needed to determine the optimal parameters of the BPNN, and the neural network module generated by training is used to replace the inverse system for offline decoupling simulation of the BP NNIS of the PMSM (Yin et al, 2004;Pang et al, 2020). The main parameters of the program to generate BPNN are as follows: net = newff (miN•max (pn) [122], {"tansig", "tansig"}, "trainlm", "learngdm"); net.…”

Section: The Optimized Back Propagation Neural Network Module Is Gene...mentioning

confidence: 99%

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Intelligent Decoupling Control Study of PMSM Based on the Neural Network Inverse System

Gong

Zhi-Qiang²,

Wang³

et al. 2022

Front. Energy Res.

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This study obtains the analytical inverse system of a permanent magnet synchronous motor (PMSM) model based on the traditional magnetic field orientation decoupling control mode by analyzing the inverse quality of the PMSM. Using the neural network’s excellent approximation ability and well learning functions, a neural network inverse system (NNIS) of the decoupling control system was established by identifying and offline training the back propagation neural network (BPNN) and radial basis function neural network (RBFNN). The data collected from the analytical inverse system of the PMSM model are used to analyze and compare the prediction accuracy and running time of the neural network, so as to optimize the structure and parameters of the neural network. The simulation results of three PMSM decoupling control systems show that the PMSM decoupling control system based on RBF NNIS has good dynamic and static decoupling performance, and robustness.

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Section: The Optimized Back Propagation Neural Network Module Is Gene...mentioning

confidence: 99%

“…Reasonable selection of spread values has great influence on the prediction accuracy of the RBFNN. Newrb is used to construct the RBFNN and spread is set as different values for comparison (Wang and Xu, 2012;Pang et al, 2020). The prediction errors of different RBF spreads are shown in Table 6.…”

Section: The Spread Of the Radial Basis Function Neural Networkmentioning

confidence: 99%

Intelligent Decoupling Control Study of PMSM Based on the Neural Network Inverse System

Gong

Zhi-Qiang²,

Wang³

et al. 2022

Front. Energy Res.

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“…It requires first recognizing the motion being performed by the human operator to explore the components that need to be incorporated by the robot. Previous studies made efforts to explore methods of machine learning to enable robots to identify human motion; some researchers implement this work through neural networks [12,13], neuro-fuzzy inference system-based classifiers [14,15]. For instance, the work of [16] integrated a data-driven musculoskeletal model into a robot interaction controller to recognize human movements and classified them according to the model to match the corresponding robot trajectory for application in HRC scenarios.…”

Section: Introductionmentioning

confidence: 99%

Efficient Reachable Workspace Division under Concurrent Task for Human-Robot Collaboration Systems

et al. 2023

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Division of the reachable workspace of upper limbs under different visual and physical conditions, finding the efficient reachable area under concurrent task conditions, and using it as a basis to divide the incorporation boundaries that require robot assistance are the focus of this paper. These could be used to rationalize the allocation of human and robot workspaces to maximize the efficiency of multitask completion, which has significant applications in the enhancement of human–robot collaboration (HRC) capabilities. However, research on this has rarely been conducted due to the complexity and diversity of arm movements. In this paper, we considered the physical and visual restrictions of the human operator, extracted the movement data of 10 participants while completing the reaching task, and divided the workspace into five areas (their angles are 0°~44.761°, 44.761°~67.578°, 67.578°~81.108°, 81.108°~153.173°, and 153.173°~180°). Measuring the concurrent task completion times when the target object is in each area, respectively, we demonstrated that areas Ⅰ ~ Ⅱ are efficient, reachable workspaces for the human. In the non-efficient reachable workspaces, the average completion times for HRC were 86.7% for human operators (in area III) and 70.1% (in area IV), with the average number of warnings reduced from 2.5 to 0.4. The average completion time for HRC in area V was 59.3% for the human operator, and the average number of warnings was reduced from 3.5 to 0.5. Adding robotic assistance in this area could improve the efficiency of the HRC systems. This study provided a quantitative evaluation of human concurrent task completion capabilities and the incorporation boundaries of robots, which is a useful reference for achieving efficient HRC.

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“…Machine learning (ML) has become a key element in current robotic fields for industrial, collaborative, mobile, and social applications [1][2][3][4]. Robots are adopted to provide repeatability, reliability [5] and to guarantee the time-variant movement's accuracy.…”

Section: Introductionmentioning

confidence: 99%

Inverse kinematic solver based on machine learning sequential procedure for robotic applications

Aggogeri

Pellegrini

Taesi

et al. 2022

J. Phys.: Conf. Ser.

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This paper presents an inverse kinematic solver for a robotic arm based on an artificial neural network, ANN, architecture. The motion of the robotic arm is controlled by the kinematics of the ANN. The novelty of the proposed method is the validation using a proprietary robot of a novel procedure that applies three networks in a sequential mode to predict one joint value at a time. The inclusion of the genetic algorithm in the ANN definition and the adoption of sequential technique significantly reduced the manual settings and increased the obtained accuracy with respect to the traditional network deployment. The simulated outcomes proved the efficacy of the proposed approach in robotic motion control. The final architecture has three hidden layers: {40 (tansig), 35 (tansig), 30 (tansig)}. The resultant MSE in joint space is close to 3.235*10-4 rad2 and 0.1318mm2 in Cartesian space for the testing dataset. The maximum trajectory error for the validation curves, a planar circle and a spatial spring, is lower than 0.27mm for each axis.

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Kinematics Analysis of 7-DOF Upper Limb Rehabilitation Robot Based on BP Neural Network

Cited by 7 publications

References 7 publications

Intelligent Decoupling Control Study of PMSM Based on the Neural Network Inverse System

Intelligent Decoupling Control Study of PMSM Based on the Neural Network Inverse System

Efficient Reachable Workspace Division under Concurrent Task for Human-Robot Collaboration Systems

Inverse kinematic solver based on machine learning sequential procedure for robotic applications

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