Modified ZNN for Time-Varying Quadratic Programming With Inherent Tolerance to Noises and Its Application to Kinematic Redundancy Resolution of Robot Manipulators

Jin, Long; Zhang, Yunong; Li, Shuai; Zhang, Yinyan

doi:10.1109/tie.2016.2590379

Cited by 222 publications

(64 citation statements)

References 21 publications

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“…Considering the similarity and space limitation, only the robustness analysis of PT-VP-RNN for solving QP problem (20) is discussed in this section, and the robustness analysis of QM problem (19) is omitted here.…”

Section: Theoretical Analysis Of Robustnessmentioning

confidence: 99%

“…To remedy this disadvantage, a zeroing neural network (ZNN) was proposed by Zhang et al [18], Jin et al [19], Xiao [34], and Li et al [35]. Since the derivatives of the error function is taken into consideration, ZNN contains a predictive ability, which can be applied to tracking theoretical solutions.…”

mentioning

confidence: 99%

“…That is to say, the residual errors of ZNN in the case of external disturbance would oscillate all the time. Even though Jin et al and Xiao proposed some modified ZNN models to improve the robustness performance [4], [19], [26], [34], the computational efficiency still should be further improved. One of the most popular modified ZNN models, which contains finite-time stability, is named finite-time recurrent neural network (FT-RNN) [36].…”

mentioning

confidence: 99%

See 2 more Smart Citations

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

Zhang

Kong

Zheng

et al. 2020

IEEE Trans. Syst. Man Cybern, Syst.

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Varying-parameter recurrent neural network, being a special kind of neural-dynamic methodology, has revealed powerful abilities to handle various time-varying problems, such as quadratic minimization (QM) and quadratic programming (QP) problems. In this paper, a novel power-type varying-parameter recurrent neural network (PT-VP-RNN) is proposed to solve the perturbed time-varying QM and QP problems. First, based on the generalization of time-varying QM and QP problems, the design process of the PT-VP-RNN is presented in detail. Second, the robustness performance of the proposed PT-VP-RNN is theoretically analyzed and proved. What is more, two numerical examples are simulated to illustrate the robustness convergence performance of PT-VP-RNN even in a large disturbance condition. Finally, two practical application examples (i.e., a robot tracking example and a venture investment example) further verify the effectiveness, accuracy, and widespread applicability of the proposed PT-VP-RNN.

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Section: Theoretical Analysis Of Robustnessmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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

Zhang

Kong

Zheng

et al. 2020

IEEE Trans. Syst. Man Cybern, Syst.

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“…For mobile robots, path planning consists of finding a feasible path to the target point in the workspace [17][18][19][20][21][22][23]. There are many related research results for this research direction [24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Dynamic Path Planning of Mobile Robots: A Novel Hybrid Heuristic Optimization Algorithm

Chen

Wang

et al. 2019

Sensors

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Mobile robots are becoming more and more widely used in industry and life, so the navigation of robots in dynamic environments has become an urgent problem to be solved. Dynamic path planning has, therefore, received more attention. This paper proposes a real-time dynamic path planning method for mobile robots that can avoid both static and dynamic obstacles. The proposed intelligent optimization method can not only get a better path but also has outstanding advantages in planning time. The algorithm used in the proposed method is a hybrid algorithm based on the beetle antennae search (BAS) algorithm and the artificial potential field (APF) algorithm, termed the BAS-APF method. By establishing a potential field, the convergence speed is accelerated, and the defect that the APF is easily trapped in the local minimum value is also avoided. At the same time, by setting a security scope to make the path closer to the available path in the real environment, the effectiveness and superiority of the proposed method are verified through simulative results.

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“…Modern optimization algorithms may be roughly classified into deterministic optimization algorithms [1][2][3][4][5] and stochastic ones. The former is theoretically sound for well-posed problems but not efficient for complicated problems.…”

Section: Introductionmentioning

confidence: 99%

Porcellio scaber algorithm (PSA) for solving constrained optimization problems

Zhang

Guo

2017

MATEC Web Conf.

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Abstract. In this paper, we extend a bio-inspired algorithm called the porcellio scaber algorithm (PSA) to solve constrained optimization problems, including a constrained mixed discrete-continuous nonlinear optimization problem. Our extensive experiment results based on benchmark optimization problems show that the PSA has a better performance than many existing methods or algorithms. The results indicate that the PSA is a promising algorithm for constrained optimization.

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Modified ZNN for Time-Varying Quadratic Programming With Inherent Tolerance to Noises and Its Application to Kinematic Redundancy Resolution of Robot Manipulators

Cited by 222 publications

References 21 publications

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

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

Real-Time Dynamic Path Planning of Mobile Robots: A Novel Hybrid Heuristic Optimization Algorithm

Porcellio scaber algorithm (PSA) for solving constrained optimization problems

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