A Data-Driven Cyclic-Motion Generation Scheme for Kinematic Control of Redundant Manipulators

Xie, Zhengtai; Jin, Long; Luo, Xin; Li, Shuai; Xiao, Xiuchun

doi:10.1109/tcst.2019.2963017

Cited by 74 publications

(24 citation statements)

References 32 publications

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“…The above projection equation has the same solution of (20), which indicates that the output of RNN model (18) converges to the optimal solution of MSA scheme (10). The proof is complete.…”

Section: Solutionmentioning

confidence: 66%

“…First, based on MSA scheme (10), a Lagrangian function is defined as F (ω) = ω T ω/2 + T ω + γ T Jω, with γ ∈ R k denoting the Lagrange-multiplier vector. As known from the Karush-Kuhn-Tucker condition [37], the optimal solution to MSA scheme (10) satisfies…”

Section: Solutionmentioning

confidence: 99%

“…Note that RNN model (19) is different from RNN (18) although they are similar except for the individual parameter. Besides, the RNN model (18) is applied to solve MSA scheme (10), and the other is used for solving SA scheme (2). The following theorem illustrates the convergence of RNN model (18) in solving MSA scheme (10).…”

Section: Solutionmentioning

confidence: 99%

“…Therefore, in recent years, redundant manipulators have received considerable attention, and studies on the motion control and applications of redundant manipulators have emerged in an endless stream [7]- [11]. For example, Xie et al exploit data-driven technology to fulfill the flexible performance of redundant manipulators and present a highperformance repetitive motion scheme [10]. Besides, the control approach is extended to the acceleration level with the quantitative relationship between the joint space error and Cartesian space error explored [11].…”

Section: Introductionmentioning

confidence: 99%

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Recurrent Neural Network for State Adjustment of Redundant Manipulators

Jin

Huang

et al. 2020

IEEE Access

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To make joints of a redundant manipulator moving automatically to a target state, a stateadjustment (SA) scheme is studied and modified in this paper. Specifically, owing to the problem of non-zero initial joint velocity in the SA scheme leading to a potential hazard to redundant manipulators, a modified state-adjustment (MSA) scheme is obtained on the basis of the SA scheme. The MSA scheme achieves the state adjustment by minimizing the differences between the joint angles and the target values. For solving the MSA scheme, a recurrent neural network (RNN) model is derived, of which the critical component is to iterate over the joint angles and joint velocities. The MSA scheme solved by the RNN model enables the redundant manipulator to adjust to the target state automatically while ensuring that the initial joint velocity is zero. Beyond that, several comparative simulations demonstrate the availability and accuracy of the MSA scheme solved by the RNN model in controlling the state adjustment of redundant manipulators. INDEX TERMS State-adjustment scheme, recurrent neural network, redundant manipulators.

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“…The above projection equation has the same solution of (20), which indicates that the output of RNN model (18) converges to the optimal solution of MSA scheme (10). The proof is complete.…”

Section: Solutionmentioning

confidence: 66%

Section: Solutionmentioning

confidence: 99%

Section: Solutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Recurrent Neural Network for State Adjustment of Redundant Manipulators

Jin

Huang

et al. 2020

IEEE Access

Self Cite

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show abstract

“…In recent decades, an increasing number of hospitals have adopted artificial intelligence methods to assist medical diagnosis as computer technology thrives [1]- [7], which promotes the reform and development of intelligent medical care at the same time [8]- [13]. Human higher neural activities such as memory, intelligence and consciousness are controlled by the central nervous system of the brain which is the most complicated structure [14].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network Based on Complex Networks for Brain Tumor Image Classification With a Modified Activation Function

Huang

Chen

et al. 2020

IEEE Access

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The diagnosis of brain tumor types generally depends on the clinical experience of doctors, and computer-assisted diagnosis improves the accuracy of diagnosing tumor types. Therefore, a convolutional neural network based on complex networks (CNNBCN) with a modified activation function for the magnetic resonance imaging classification of brain tumors is presented. The network structure is not manually designed and optimized, but is generated by randomly generated graph algorithms. These randomly generated graphs are mapped into a computable neural network by a network generator. The accuracy of the modified CNNBCN model for brain tumor classification reaches 95.49%, which is higher than several models presented by other works. In addition, the test loss of brain tumor classification of the modified CNNBCN model is lower than those of the ResNet, DenseNet and MobileNet models in the experiments. The modified CNNBCN model not only achieves satisfactory results in brain tumor image classification, but also enriches the methodology of neural network design. INDEX TERMS Convolutional neural network, complex networks, randomly generated graph, network generator, brain tumors.

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Discrete-Time Recurrent Neural Network for Solving Multi-linear $$\mathcal {M}$$-tensor Equation

Wang

et al. 2021

Advances in Intelligent Systems and Computing

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Multi-linear M-tensor equation is a complex system of linear equations, whose coefficient is a higher-order tensor. Recurrent neural network (RNN) has great advantages in the processing of sequence data, especially in the processing of nonlinear systems, showing a very potent ability. In this paper, solving the multi-linear M-tensor equation is transformed into an error function from the perspective of control science. By controlling the error to zero, the real solution to the error function is approached to the theoretical solution. The model is discretized for the convenience of implementation in computers. Moreover, the relevant theoretical analyses are provided, and the effectiveness and advantages of the proposed method are proved by comparing it with the Newton iteration (NI) neural algorithm.

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A Data-Driven Cyclic-Motion Generation Scheme for Kinematic Control of Redundant Manipulators

Cited by 74 publications

References 32 publications

Recurrent Neural Network for State Adjustment of Redundant Manipulators

Recurrent Neural Network for State Adjustment of Redundant Manipulators

Convolutional Neural Network Based on Complex Networks for Brain Tumor Image Classification With a Modified Activation Function

Discrete-Time Recurrent Neural Network for Solving Multi-linear $$\mathcal {M}$$-tensor Equation

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