Pseudo-inverse Jacobian control with grey relational analysis for robot manipulators mounted on oscillatory bases

Lin, Jia-Chun; Lin, C.C.; Lo, Hsin-Hsin

doi:10.1016/j.jsv.2009.05.027

Cited by 29 publications

(10 citation statements)

References 15 publications

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“…By expanding the ZNN design formula, the following evolution equation of a ZNN model is obtained. (3) in which the exponential convergence was verified by Liao et al in [18]. Note that Eq.…”

Section: Znn Solutionmentioning

confidence: 66%

“…A variety of science and engineering fields, such as image restoration [1], signal processing [2], robotics [3], and associative memories [4], often need pseudoinverse matrices to be calculated. Owing to their important role within these fields, a significant amount of effort has been directed towards the fast and accurate calculation of pseudoinverse matrices.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robustness Analyses and Optimal Sampling Gap of Recurrent Neural Network for Dynamic Matrix Pseudoinversion

Liao¹,

Xiang²

2017

JACIII

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This study analyses the robustness and convergence characteristics of a neural network. First, a special class of recurrent neural network (RNN), termed a continuous-time Zhang neural network (CTZNN) model, is presented and investigated for dynamic matrix pseudoinversion. Theoretical analysis of the CTZNN model demonstrates that it has good robustness against various types of noise. In addition, considering the requirements of digital implementation and online computation, the optimal sampling gap for a discrete-time Zhang neural network (DTZNN) model under noisy environments is proposed. Finally, experimental results are presented, which further substantiate the theoretical analyses and demonstrate the effectiveness of the proposed ZNN models for computing a dynamic matrix pseudoinverse under noisy environments.

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Section: Znn Solutionmentioning

confidence: 66%

Section: Introductionmentioning

confidence: 99%

Robustness Analyses and Optimal Sampling Gap of Recurrent Neural Network for Dynamic Matrix Pseudoinversion

Liao¹,

Xiang²

2017

JACIII

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“…The central issues of such problems are first to decouple the manipulator and base dynamics and second to decay the base oscillation. To satisfy those requirements, several approaches have been proposed such as task space feedback [36,87], filtering command [65], path-planning [70,79], acceleration feedback [57], active damping [58], H ∞ control [96,97], Fuzzy control [61], pseudo-inverse jacobian [62]-based approaches.…”

Section: What Is An Oscillatory-base Manipulator?mentioning

confidence: 99%

Robust Motion Control of Oscillatory-Base Manipulators

Toda¹

2016

Lecture Notes in Control and Information Sciences

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About this SeriesThis series aims to report new developments in the fields of control and information sciences-quickly, informally and at a high level. The type of material considered for publication includes:1. Preliminary drafts of monographs and advanced textbooks 2. Lectures on a new field, or presenting a new angle on a classical field 3. Research reports 4. Reports of meetings, provided they are (a) of exceptional interest and (b) devoted to a specific topic. The timeliness of subject material is very important. More information about this series atAdditional material to this book can be downloaded from http://extras.springer.com.

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“…As an essential foundation of solution, pseudoinverse appears frequently in various mathematical and engineering fields. Its application also can be found in optimization [1], robotics [2], [3], image restoration [4], [5]. In view of its importance, many researchers put forward a lot of algorithms [6]- [11].…”

Section: Introductionmentioning

confidence: 99%

A Noise-Acceptable ZNN for Computing Complex-Valued Time-Dependent Matrix Pseudoinverse

2019

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The issue of complex-valued time-dependent pseudoinverse often exists in science and engineering fields. In the existing studies, many models were presented for solving complex-valued timedependent pseudoinverse in the noiseless environments. However, the appearance of noise is unavoidable in practice. In this paper, a novel noise-acceptable zeroing neural network (NAZNN) model is first proposed for computing complex-valued time-dependent matrix pseudoinverse with different noise situations. For comparison, the traditional zeroing neural network and the gradient neural network are adopted to complete the same task. Theoretical analyses prove that the proposed NAZNN model obtains the global exponential convergence performance. Moreover, the proposed NAZNN is also proven to obtain strong resistance to various sorts of noise. Finally, the results of numerical experiments further substantiate the theoretical analysis and indicate the effectiveness and superiority of the proposed NAZNN model for computing complex-valued time-dependent matrix pseudoinverse in various kinds of noise. INDEX TERMS Zeroing neural network, time-dependent, complex-valued, noise-acceptable, matrix pseudoinverse.

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Pseudo-inverse Jacobian control with grey relational analysis for robot manipulators mounted on oscillatory bases

Cited by 29 publications

References 15 publications

Robustness Analyses and Optimal Sampling Gap of Recurrent Neural Network for Dynamic Matrix Pseudoinversion

Robustness Analyses and Optimal Sampling Gap of Recurrent Neural Network for Dynamic Matrix Pseudoinversion

Robust Motion Control of Oscillatory-Base Manipulators

A Noise-Acceptable ZNN for Computing Complex-Valued Time-Dependent Matrix Pseudoinverse

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