Robustness Analysis of a Power-Type Varying-Parameter Recurrent Neural Network for Solving Time-Varying QM and QP Problems and Applications

Zhang, Zhijun; Kong, Lingdong; Zheng, Lunan; Zhang, Pengchao; Qu, Xiaobo; Liao, Bolin; Yu, Zhuliang

doi:10.1109/tsmc.2018.2866843

Cited by 89 publications

(12 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Lagrange multiplier method is employed in the controller to obtain the optimal solution. Zhang et al [47]- [49] proposed a power-type varying-parameter control method. To be brief, the solving procedure was divided into three steps: first, ∂L(u, λ)/∂u and ∂L(u, λ)/∂λ were rewritten as a linear matrix equation formatted by WY − G = 0.…”

Section: B Controller Designmentioning

confidence: 99%

Simultaneous Obstacle Avoidance and Target Tracking of Multiple Wheeled Mobile Robots With Certified Safety

Ли

et al. 2022

IEEE Trans. Cybern.

View full text Add to dashboard Cite

Collision avoidance plays a major part in the control of the wheeled mobile robot (WMR). Most existing collision-avoidance methods mainly focus on a single WMR and environmental obstacles. There are few products that cast light on the collision-avoidance between multiple WMRs (MWMRs). In this article, the problem of simultaneous collision-avoidance and target tracking is investigated for MWMRs working in the shared environment from the perspective of optimization. The collisionavoidance strategy is formulated as an inequality constraint, which has proven to be collision free between the MWMRs. The designed MWMRs control scheme integrates path following, collision-avoidance, and WMR velocity compliance, in which the path following task is chosen as the secondary task, and collisionavoidance is the primary task so that safety can be guaranteed in advance. A Lagrangian-based dynamic controller is constructed for the dominating behavior of the MWMRs. Combining theoretical analyses and experiments, the feasibility of the designed control scheme for the MWMRs is substantiated. Experimental results show that if obstacles do not threaten the safety of the WMR, the top priority in the control task is the target track task. All robots move along the desired trajectory. Once the collision criterion is satisfied, the collision-avoidance mechanism is activated and prominent in the controller. Under the proposed scheme, all robots achieve the target tracking on the premise of being collision free.

show abstract

Section: B Controller Designmentioning

confidence: 99%

Simultaneous Obstacle Avoidance and Target Tracking of Multiple Wheeled Mobile Robots With Certified Safety

Ли

et al. 2022

IEEE Trans. Cybern.

View full text Add to dashboard Cite

show abstract

“…The inverse kinematic problem is one of the challenging issues for robot redundant manipulators and the traditional ways to solve it (e.g., analytical solutions or pseudoinverse-based methods) could come with joint-angular-drift problems Klein and Kee (1989). We can exploit one of the recent varying-parameter neural networks proposed in Zhang et al (2018a,b,c) to cope with the joint-angular-drift problems.…”

Section: Impact In Roboticsmentioning

confidence: 99%

DeepDynamicHand: A Deep Neural Architecture for Labeling Hand Manipulation Strategies in Video Sources Exploiting Temporal Information

et al. 2018

View full text Add to dashboard Cite

Humans are capable of complex manipulation interactions with the environment, relying on the intrinsic adaptability and compliance of their hands. Recently, soft robotic manipulation has attempted to reproduce such an extraordinary behavior, through the design of deformable yet robust end-effectors. To this goal, the investigation of human behavior has become crucial to correctly inform technological developments of robotic hands that can successfully exploit environmental constraint as humans actually do. Among the different tools robotics can leverage on to achieve this objective, deep learning has emerged as a promising approach for the study and then the implementation of neuro-scientific observations on the artificial side. However, current approaches tend to neglect the dynamic nature of hand pose recognition problems, limiting the effectiveness of these techniques in identifying sequences of manipulation primitives underpinning action generation, e.g., during purposeful interaction with the environment. In this work, we propose a vision-based supervised Hand Pose Recognition method which, for the first time, takes into account temporal information to identify meaningful sequences of actions in grasping and manipulation tasks. More specifically, we apply Deep Neural Networks to automatically learn features from hand posture images that consist of frames extracted from grasping and manipulation task videos with objects and external environmental constraints. For training purposes, videos are divided into intervals, each associated to a specific action by a human supervisor. The proposed algorithm combines a Convolutional Neural Network to detect the hand within each video frame and a Recurrent Neural Network to predict the hand action in the current frame, while taking into consideration the history of actions performed in the previous frames. Experimental validation has been performed on two datasets of dynamic hand-centric strategies, where subjects regularly interact with objects and environment. Proposed architecture achieved a very good classification accuracy on both datasets, reaching performance up to 94%, and outperforming state of the art techniques. The outcomes of this study can be successfully applied to robotics, e.g., for planning and control of soft anthropomorphic manipulators.

show abstract

“…Furthermore, Zhang et al designed an adaptive multi-layer neural dynamics-based controllers of multi-rotor unmanned aerial vehicles for the sake of adapting to various complex transportation 36 . In addition, to solve time varying quadratic programming problem and robot tracking problem, a power-type varying-parameter recurrent neural network was proposed different from VP-CDNN 37,38 . To solve non-repetitive motion problem of redundant robot manipulators, Zhang et al proposed an adaptive fuzzy recurrent neural network, which can avoid the saturation of time varying design functions 39 .…”

Section: Introductionmentioning

confidence: 99%

A Bagging Dynamic Deep Learning Network for Diagnosing COVID-19

Zhang

Chen

Sun

2021

Preprint

Self Cite

View full text Add to dashboard Cite

COVID-19 is a serious epidemic all over the world. As an efficient way in intelligent medical services, using X-ray chest radiography image for automatically diagnosing COVID-19 provides huge assistances and conveniences for clinicians in practice. In this paper, a bagging dynamic deep learning network (B-DDLN) is proposed for diagnosing COVID-19 by intelligently recognizing X-ray chest radiography images. After a series of preprocessing steps for images, we pre-train convolution blocks as a feature extractor. For the extracted features, bagging dynamic learning network classifier is trained based on neural dynamic learning algorithm and bagging algorithm. B-DDLN connects feature extractor and bagging classifier in series. Experimental results verify that using the proposed B-DDLN can achieve 98.8889% testing accuracy, which illustrates the best diagnosis performances among existing methods on the open image set and provides evidences for further detection and treatment.

show abstract

Robustness Analysis of a Power-Type Varying-Parameter Recurrent Neural Network for Solving Time-Varying QM and QP Problems and Applications

Cited by 89 publications

References 45 publications

Simultaneous Obstacle Avoidance and Target Tracking of Multiple Wheeled Mobile Robots With Certified Safety

Simultaneous Obstacle Avoidance and Target Tracking of Multiple Wheeled Mobile Robots With Certified Safety

DeepDynamicHand: A Deep Neural Architecture for Labeling Hand Manipulation Strategies in Video Sources Exploiting Temporal Information

A Bagging Dynamic Deep Learning Network for Diagnosing COVID-19

Contact Info

Product

Resources

About