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Modeling and Control of Robotic Manipulators Based on Artificial Neural Networks: A Review

  • Review Paper
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Iranian Journal of Science and Technology, Transactions of Mechanical Engineering Aims and scope Submit manuscript

Abstract

Recently, robotic manipulators have been playing an increasingly critical part in scientific research and industrial applications. However, modeling of robotic manipulators is extremely difficult due to their complicated structures, nonlinear characteristics, and so forth. Based on the unique black-box characteristics and self-learning capability, artificial neural networks (ANNs) are considered effective tools for modeling and controlling robotic manipulators with uncertain dynamics due to the advantages of both convenient hardware implementation and high-speed parallel distributed calculation. This review attempts to summarize the current research on modeling and control of robotic manipulators based on ANNs. Firstly, the various types of robotic manipulators and the development of ANNs are discussed briefly. Then, the ANN-based modeling methods of robotic manipulators are described. Both traditional and intelligent control methods based on ANNs for robotic manipulators are discussed subsequently. Besides, some potential directions, possibly deserving investigation in a variety of different types of modeling as well as control methods by ANNs, are described and discussed as well. The proposed summary is aimed at aiding researchers to effectively comprehend the characteristics of various ANNs and their applications in the modeling and control of robotic manipulators while providing a reference for future directions related to robotic manipulator research.

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Abbreviations

ADC::

Adaptive control

ANN::

Artificial neural network

APFNN::

Adaptive feed-forward neural network

ANNISMC::

Adaptive neural network integral sliding mode control

BAS::

Beetle Antenna Search

BS::

Back-stepping

BP::

Back propagation

BPTT::

Back propagation through time

CMAC::

Cerebellar model algorithm computer

CNN::

Convolutional neural network

DFNN::

Dynamic fuzzy neural network

DLNN::

Deep learning neural network

DL::

Deep learning

DOF::

Degree of freedom

ENN::

Elman neural network

ESN::

Echo state network

FWN::

Fuzzy wavelet neural network

FL::

Feedback linearization

GRU::

Gated recurrent unit

GD::

Gradient descend

GS::

Gain scheduling

GA::

Genetic algorithm

HGB::

Hybrid gradient boosting

LSTM::

Long short-term memory

LM::

Levenberg–Marquardt

LQR::

Linear quadratic regulator

LQG::

Linear quadratic Gaussian

MPC::

Model predictive control

MFC::

Model-free control

MBC::

Model based control

MLP::

Multi-layer perceptron

MLRNN::

Mix locally recurrent neural network

MIMO::

Multi-input multi-output

MNN::

Multi-layer neural network

NN::

Neural network

NARX::

Nonlinear autoregressive

OF::

Optimal feedback

PID::

Proportional-integral-derivative

PSG::

Predator search genetic

ReLU::

Rectified linear unit

RBFNN::

Radial basis function neural networks

SCARA::

Selective compliant assembly robot arm

SMC::

Sliding mode control

SISO::

Single-input single-output

SVM::

Support vector machine

SNN::

Spike neural network

VMM::

Variable-length Markov model

WNN::

Wavelet neural network

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Acknowledgements

This work is supported by the Open Project Program of Shandong Marine Aerospace Equipment Technological Innovation Center, Ludong University (Grant No. MAETIC2021-02).

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  1. School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, 430070, China

    Zhaobing Liu, Kerui Peng, Lvpeng Han & Shengchuang Guan

  2. Hubei Digital Manufacturing Key Laboratory, Wuhan University of Technology, Wuhan, 430070, China

    Zhaobing Liu

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Liu, Z., Peng, K., Han, L. et al. Modeling and Control of Robotic Manipulators Based on Artificial Neural Networks: A Review. Iran J Sci Technol Trans Mech Eng 47, 1307–1347 (2023). https://doi.org/10.1007/s40997-023-00596-3

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