research-article

Solving the Forward Kinematics Problem in Parallel Manipulators Using Neural Network

Authors:

Hossein Faraji,

Hamidreza Hajimirzaalian,

Mohammad Hossein SabourAuthors Info & Claims

ICCMA 2017: Proceedings of the 2017 The 5th International Conference on Control, Mechatronics and Automation

Pages 23 - 29

https://doi.org/10.1145/3149827.3149853

Published: 11 October 2017 Publication History

Abstract

A parallel manipulator is a closed kinematic structure with the necessary rigidity to provide a high payload to self-weight ratio suitable for many applications in manufacturing, flight simulation systems, and medical robotics. Because of its closed structure, the kinematic control of such a mechanism is difficult. The inverse kinematics problem for such manipulators has a mathematical solution but, the forward kinematics problem (FKP) is mathematically intractable. This paper presents a Stewart platform with asymmetric payload and proposes a neural-network-based strategy that solves the problem of FKP to a desire level of accuracy, and can apply the solution for any other desire trajectory. The Neural-network concepts with backpropagation learning is implemented. The inverse kinematic is used to obtain the required force for desire trajectory. Then, neural network is taken into account to take actuator forces as inputs and train the system in order to reach the desire path. It is concluded that, neural network results are acceptable and can be applied for any desire path.

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Digital Library

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Cited By

Moosavi SZafar MSanfilippo F(2022)Forward Kinematic Modelling with Radial Basis Function Neural Network Tuned with a Novel Meta-Heuristic Algorithm for Robotic ManipulatorsRobotics10.3390/robotics1102004311:2(43)Online publication date: 2-Apr-2022
https://doi.org/10.3390/robotics11020043

Index Terms

Solving the Forward Kinematics Problem in Parallel Manipulators Using Neural Network
1. Applied computing
  1. Physical sciences and engineering

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ICCMA 2017: Proceedings of the 2017 The 5th International Conference on Control, Mechatronics and Automation

October 2017

156 pages

ISBN:9781450353397

DOI:10.1145/3149827

Copyright © 2017 ACM.

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Concordia University: Concordia University
University of Alberta: University of Alberta

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Association for Computing Machinery

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Published: 11 October 2017

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ICCMA 2017

ICCMA 2017: 2017 The 5th International Conference on Control, Mechatronics and Automation

October 11 - 13, 2017

AB, Edmonton, Canada

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Cited By

Moosavi SZafar MSanfilippo F(2022)Forward Kinematic Modelling with Radial Basis Function Neural Network Tuned with a Novel Meta-Heuristic Algorithm for Robotic ManipulatorsRobotics10.3390/robotics1102004311:2(43)Online publication date: 2-Apr-2022
https://doi.org/10.3390/robotics11020043

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