A Rapid Base Parameter Physical Feasibility Test Algorithm for Industrial Robot Manipulator Identification Using a Recurrent Neural Network

A rapid recurrent neural network (RNN)-based physical feasibility test algorithm for base parameters during the identification process of a robot dynamic model is proposed in this paper. Firstly, related physical constraints such as inertia tensor, drive chain inertia, and friction are combined into...

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Bibliographic Details
Published in:IEEE access Vol. 11; p. 1
Main Authors: Huang, Hao-Lun, Cheng, Ming-Yang, Huang, Tzu-Yuan
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Chain drives
Dynamic models
Dynamic parameter identification
Evolutionary algorithms
Feasibility
Feasibility studies
Heuristic algorithms
Identification
Identification methods
industrial robot manipulator
Industrial robots
Inertia
Linear matrix inequalities
Manipulator dynamics
Manipulators
Neural networks
Object recognition
Optimization
Parameter identification
physical feasibility
recurrent neural network
Recurrent neural networks
Robot arms
Robots
Semidefinite programming
Stability analysis
Tensors
Torque
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:A rapid recurrent neural network (RNN)-based physical feasibility test algorithm for base parameters during the identification process of a robot dynamic model is proposed in this paper. Firstly, related physical constraints such as inertia tensor, drive chain inertia, and friction are combined into the formulation of linear matrix inequality (LMI) to examine the physical consistency of the base parameters. The optimization problem of LMI is then solved by a matrix-oriented gradient-type RNN. Since the network structure of this type of RNN is simple and the process for solving the optimization problem of LMI is parallel distributed, the physical feasibility test can be completed more quickly than when utilizing commonly used semi-definite programming techniques. By taking advantage of highly efficient computation capabilities, the proposed physical feasibility test algorithm is particularly suitable for identification approaches that require rapid feasibility assessment such as on-line identification methods or optimization-based identification methods. An evolutionary algorithm-based identification method is used as an illustrative example to assess the performance of the proposed approach. In addition, a stability proof for the proposed gradient-type RNN is also provided. Results of the experiments conducted on a 6-DOF industrial robot manipulator verify the effectiveness of the proposed approach.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3344490