Neural networks‐based sliding mode tracking control for the four wheel‐legged robot under uncertain interaction

Wu, Qingbin; Wang, Junzheng; Li, Jiehao

doi:10.1002/rnc.5473

Cited by 49 publications

(11 citation statements)

References 42 publications

(40 reference statements)

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“…The BIT-6NAZA robot system includes the environment perception section, sensor section, battery section, control system and motion section (Su et al, 2019;Li et al, 2021b). There are 36 GSM20-1202 electric cylinders with a 300 mm stroke and six-wheeled Elmo servo motors within the motion actuator system.…”

Section: System Developmentmentioning

confidence: 99%

Iterative learning control for a distributed cloud robot with payload delivery

Wang

et al. 2021

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Purpose When it comes to the high accuracy autonomous motion of the mobile robot, it is challenging to effectively control the robot to follow the desired trajectory and transport the payload simultaneously, especially for the cloud robot system. In this paper, a flexible trajectory tracking control scheme is developed via iterative learning control to manage a distributed cloud robot (BIT-6NAZA) under the payload delivery scenarios. Design/methodology/approach Considering the relationship of six-wheeled independent steering in the BIT-6NAZA robot, an iterative learning controller is implemented for reliable trajectory tracking with the payload transportation. Meanwhile, the stability analysis of the system ensures the effective convergence of the algorithm. Findings Finally, to evaluate the developed method, some demonstrations, including the different motion models and tracking control, are presented both in simulation and experiment. It can achieve flexible tracking performance of the designed composite algorithm. Originality/value This paper provides a feasible method for the trajectory tracking control in the cloud robot system and simultaneously promotes the robot application in practical engineering.

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Section: System Developmentmentioning

confidence: 99%

Iterative learning control for a distributed cloud robot with payload delivery

Wang

et al. 2021

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“…So how to minimize the number of hidden layer nodes while maintaining the effects becomes an important issue. To address this problem, some issues (Yang et al, 2012;Li et al, 2021bLi et al, , 2020a put forward dynamic adjustment. The number of hidden layer nodes is constantly adjusted in parameter learning until there is a balance between network performance and the number of hidden layer nodes.…”

Section: Introductionmentioning

confidence: 99%

Towards extreme learning machine framework for lane detection on unmanned mobile robot

Dai

Wang

et al. 2022

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Purpose This paper aims to focus on lane detection of unmanned mobile robots. For the mobile robot, it is undesirable to spend lots of time detecting the lane. So quickly detecting the lane in a complex environment such as poor illumination and shadows becomes a challenge. Design/methodology/approach A new learning framework based on an integration of extreme learning machine (ELM) and an inception structure named multiscale ELM is proposed, making full use of the advantages that ELM has faster convergence and convolutional neural network could extract local features in different scales. The proposed architecture is divided into two main components: self-taught feature extraction by ELM with the convolution layer and bottom-up information classification based on the feature constraint. To overcome the disadvantages of poor performance under complex conditions such as shadows and illumination, this paper mainly solves four problems: local features learning: replaced the fully connected layer, the convolutional layer is used to extract local features; feature extraction in different scales: the integration of ELM and inception structure improves the parameters learning speed, but it also achieves spatial interactivity in different scales; and the validity of the training database: a method how to find a training data set is proposed. Findings Experimental results on various data sets reveal that the proposed algorithm effectively improves performance under complex conditions. In the actual environment, experimental results tested by the robot platform named BIT-NAZA show that the proposed algorithm achieves better performance and reliability. Originality/value This research can provide a theoretical and engineering basis for lane detection on unmanned robots.

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“…By comparing different network models, the best solution is selected. Furthermore, it can also construct a deep CNN (Li et al , 2021e) suitable for its research system according to the advantages of the model. Table 2 summarizes the advantages and disadvantages of the standard model.…”

Section: Introduction To Convolutional Neural Networkmentioning

confidence: 99%

Eye control system based on convolutional neural network: a review

Xiong

Nie

2022

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Purpose This paper primarily aims to focus on a review of convolutional neural network (CNN)-based eye control systems. The performance of CNNs in big data has led to the development of eye control systems. Therefore, a review of eye control systems based on CNNs is helpful for future research. Design/methodology/approach In this paper, first, it covers the fundamentals of the eye control system as well as the fundamentals of CNNs. Second, the standard CNN model and the target detection model are summarized. The eye control system’s CNN gaze estimation approach and model are next described and summarized. Finally, the progress of the gaze estimation of the eye control system is discussed and anticipated. Findings The eye control system accomplishes the control effect using gaze estimation technology, which focuses on the features and information of the eyeball, eye movement and gaze, among other things. The traditional eye control system adopts pupil monitoring, pupil positioning, Hough algorithm and other methods. This study will focus on a CNN-based eye control system. First of all, the authors present the CNN model, which is effective in image identification, target detection and tracking. Furthermore, the CNN-based eye control system is separated into three categories: semantic information, monocular/binocular and full-face. Finally, three challenges linked to the development of an eye control system based on a CNN are discussed, along with possible solutions. Originality/value This research can provide theoretical and engineering basis for the eye control system platform. In addition, it also summarizes the ideas of predecessors to support the development of future research.

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Neural networks‐based sliding mode tracking control for the four wheel‐legged robot under uncertain interaction

Cited by 49 publications

References 42 publications

Iterative learning control for a distributed cloud robot with payload delivery

Iterative learning control for a distributed cloud robot with payload delivery

Towards extreme learning machine framework for lane detection on unmanned mobile robot

Eye control system based on convolutional neural network: a review

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