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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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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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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DOI: https://doi.org/10.1007/s40997-023-00596-3