Improved Online Adjustment of Step Timing and Location for Legged Locomotion

Sun, Hao; Yang, Junjie; An, Hao; Wang, Changhong

doi:10.1007/s10846-021-01406-1

Cited by 15 publications

(11 citation statements)

References 41 publications

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“…Adding the best PID controller with the improved phoenix algorithm greatly improves its response and impedance response. The research results can provide some reference for the intelligent development of shear wheels [20].…”

Section: Results and Discussion Of Shearer Drum Elevation Control Bas...mentioning

confidence: 89%

Retracted: Research on intelligent elevation control method of shearer drum based on deep learning

Zhang

2023

IET Software

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Research on deep learning and shearer drum elevation control is a hot topic in recent years. The shearer drum elevation control based on deep learning has been paid attention to by the majority of researchers and a series of studies have been carried out. In order to solve the problem that the traditional memory cutting method of shearer is not accurate, the research on intelligent elevation control method of shearer drum based on deep learning is proposed. In this essay, the intelligent control system of drum elevation of shearer is modelled mathematically, and the improved deep-learning firefly algorithm is used to optimise the PID parameters of the drum control system, and the Simulink simulation model of the intelligent control system of drum elevation is built. The response of the system using the improved and basic firefly algorithm is analysed. The experimental results show that the overshoot of the optimised system decreases by 21.6% under the step signal. Under the ramp signal, the overshoot of the optimised system decreased by 38.4%. Under the pulse interference signal, the overshoot of the optimised system is reduced by 21.9%. Under the step interference signal, the overshoot of the optimised system is reduced by 33.2%. Under the random interference signal, the wave range of the optimised response curve is reduced by 40.1%. Conclusion: After adding the PID controller optimised by the improved deep learning firefly algorithm to the drum elevation control system, the response performance and anti-interference performance of the system are greatly improved and enhanced. The research of this article provides important guidance for deep learning and shearer drum elevation control. K E Y W O R D S biomedical electronics, combinatorial mathematics | INTRODUCTIONThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Results and Discussion Of Shearer Drum Elevation Control Bas...mentioning

confidence: 89%

Retracted: Research on intelligent elevation control method of shearer drum based on deep learning

Zhang

2023

IET Software

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“…[ 67,68 ] Backpropagation is used in the process of neural network simulation, which is a well‐researched method, a three‐layer network including a hidden layer is used for backpropagation as shown in Figure a. [ 69,70 ] The schematic of the crossbar array of the synaptic weight layer is shown in Figure 4b. The TNOASs are used memory elements in a crossbar array and their channel conductance changes are treated as the weight update for executing the backpropagation algorithm.…”

Section: Resultsmentioning

confidence: 99%

All‐Solid‐State Vertical Three‐Terminal N‐Type Organic Synaptic Devices for Neuromorphic Computing

Xie

Zhuge

Zhao

et al. 2022

Adv Funct Materials

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Artificial synaptic devices are the basic composition units for neuromorphic computing processors that realize massive parallel computing. However, the n-type organic transistors have failed to achieve good performance as an artificial synaptic device for neuromorphic computing until now. Here, a vertical three-terminal n-type organic artificial synapse (TNOAS) using a lithium ion-based organic dielectric and the n-type donor-acceptor (D-A) conjugated polymer-naphthalene-1,4,5,8-tetracarboxylic-diimide-thiophene-vinyl-thiophene (NDI-gTVT) as the channel is proposed. The TNOAS achieves nonvolatile conductance modulation with high current density operation (≈10 KA cm −2 ) at low voltage and mimics the basic functions of biological synapses, such as long-term synaptic plasticity and paired-pulse facilitation. The minimum energy consumption of a response event triggered by a single action potential is 6.16 pJ, which can be comparable with p-type counterparts. Moreover, simulation using handwritten digital datasets exhibit a high recognition accuracy of 94%.

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“…However, most problems in real life cannot be determined with input and output conditions, and these problems require strong real‐time processing capabilities, which are very difficult for traditional machine vision systems, such as image recognition and logical deduction. [ 5–10 ]…”

Section: Introductionmentioning

confidence: 99%

Advanced Optoelectronic Devices for Neuromorphic Analog Based on Low‐Dimensional Semiconductors

Wang

Zong²,

Liu

et al. 2023

Adv Funct Materials

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Neuromorphic systems can parallelize the perception and computation of information, making it possible to break through the von Neumann bottleneck. Neuromorphic engineering has been developed over a long period of time based on Hebbian learning rules. The optoelectronic neuromorphic analog device combines the advantages of electricity and optics, and can simulate the biological visual system, which has a very strong development potential. Low‐dimensional materials play a very important role in the field of optoelectronic neuromorphic devices due to their flexible bandgap tuning mechanism and strong light‐matter coupling efficiency. This review introduces the basic synaptic plasticity of neuromorphic devices. According to the different number of terminals, two‐terminal neuromorphic memristors, three‐terminal neuromorphic transistors and artificial visual system are introduced from the aspects of the action mechanism and device structure. Finally, the development prospect of optoelectronic neuromorphic analog devices based on low‐dimensional materials is prospected.

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Improved Online Adjustment of Step Timing and Location for Legged Locomotion

Cited by 15 publications

References 41 publications

Retracted: Research on intelligent elevation control method of shearer drum based on deep learning

Retracted: Research on intelligent elevation control method of shearer drum based on deep learning

All‐Solid‐State Vertical Three‐Terminal N‐Type Organic Synaptic Devices for Neuromorphic Computing

Advanced Optoelectronic Devices for Neuromorphic Analog Based on Low‐Dimensional Semiconductors

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