A Novel Discretized ZNN Model for Velocity Layer Weighted Multicriteria Optimization of Robotic Manipulators With Multiple Constraints

Qiu, Binbin; Yang, Song; Yu, Peng; Tan, Ning

doi:10.1109/tii.2022.3197270

Cited by 11 publications

(5 citation statements)

References 40 publications

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“…previous studies (Zhang and Zhang, 2013 ; Zhang et al, 2018 ; Li, 2020 ; Li et al, 2023 ; Qiu et al, 2023 ) supply a large number of JLCSs.…”

Section: Scheme Formulation and Algorithm Designmentioning

confidence: 99%

“…previous studies (Zhang and Zhang, 2013;Zhang et al, 2018;Li, 2020;Li et al, 2023;Qiu et al, 2023) supply a large number of JLCSs. Nevertheless, the JLCS in Zhang and Zhang ( 2013) is unable to guarantee 0 − (t) or 0 + (t) to be differentiable anywhere.…”

Section: Rudimentary Knowledgementioning

confidence: 99%

“…Meanwhile, as regard to the JLCS in Li ( 2020 ), ℧ − ( t ) and ℧ + ( t ) are designed as piecewise functions, respectively, and complex compound functions are embedded in them. In addition, the JLCS in Li et al ( 2023 ); Qiu et al ( 2023 ) adopt numerous design parameters and construct pretty complex expressions.…”

Section: Scheme Formulation and Algorithm Designmentioning

confidence: 99%

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An adaptive discretized RNN algorithm for posture collaboration motion control of constrained dual-arm robots

Zhang,

Han,

Qiu

2024

Front. Neurorobot.

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Although there are many studies on repetitive motion control of robots, few schemes and algorithms involve posture collaboration motion control of constrained dual-arm robots in three-dimensional scenes, which can meet more complex work requirements. Therefore, this study establishes the minimum displacement repetitive motion control scheme for the left and right robotic arms separately. On the basis of this, the design mentality of the proposed dual-arm posture collaboration motion control (DAPCMC) scheme, which is combined with a new joint-limit conversion strategy, is described, and the scheme is transformed into a time-variant equation system (TVES) problem form subsequently. To address the TVES problem, a novel adaptive Taylor-type discretized recurrent neural network (ATT-DRNN) algorithm is devised, which fundamentally solves the problem of calculation accuracy which cannot be balanced well with the fast convergence speed. Then, stringent theoretical analysis confirms the dependability of the ATT-DRNN algorithm in terms of calculation precision and convergence rate. Finally, the effectiveness of the DAPCMC scheme and the excellent convergence competence of the ATT-DRNN algorithm is verified by a numerical simulation analysis and two control cases of dual-arm robots.

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“…previous studies (Zhang and Zhang, 2013 ; Zhang et al, 2018 ; Li, 2020 ; Li et al, 2023 ; Qiu et al, 2023 ) supply a large number of JLCSs.…”

Section: Scheme Formulation and Algorithm Designmentioning

confidence: 99%

Section: Rudimentary Knowledgementioning

confidence: 99%

Section: Scheme Formulation and Algorithm Designmentioning

confidence: 99%

See 1 more Smart Citation

An adaptive discretized RNN algorithm for posture collaboration motion control of constrained dual-arm robots

Zhang,

Han,

Qiu

2024

Front. Neurorobot.

Self Cite

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“…In 2022, Qiu et al proposed a groundbreaking Velocity Layer Weighted Multicriteria Optimization (VLWMCO) scheme, showcasing its superiority over conventional methods for robotic manipulators dealing with multiple constraints [78]. Subsequently, the VLWMCO scheme was reformulated as a dynamic quadratic programming problem and then solved using a novel DZNN model and the precision-based Six-Step Extrapolated-Backward Discretization (6SEBD) rule.…”

Section: Application In Robot Manipulator Motion Planningmentioning

confidence: 99%

“…And the novel 6SEBD-based DZNN model with a truncation error of o(τ 7 ) is obtained as: In the literature [78], the performance of using the conventional INVM scheme and the resultant DZNN model, compared with using the VLWMCO scheme and the 6SEBDbased DZNN model, for end-effector tracking of the eightpetal flower path was experimentally compared. The results showed that using the conventional Infinity-norm velocity minimization scheme resulted in the actual trajectory of the end effector gradually deviating from the expected path during task execution, and undesirable joint angle drift occurred.…”

Section: Application In Robot Manipulator Motion Planningmentioning

confidence: 99%

Applications of Zeroing Neural Networks: A Survey

Wang,

Zhang,

Huang

et al. 2024

IEEE Access

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Time-varying problems are prevalent in engineering, presenting a significant challenge due to the fluctuations in parameters and goals at different time points. The Zeroing Neural Network (ZNN), a specialized form of Recurrent Neural Network (RNN) developed by Zhang et al., has gained attention for its rapid convergence speed and robustness making it a valuable tool for real-time solving of diverse timevarying problems. This review article explores the practical applications of ZNN across various domains in the past two decades, specifically focusing on robot manipulator path tracking, motion planning, and chaotic systems. The comprehensive scope of this review is essential for researchers and beginners looking to grasp the efficacy of ZNN in addressing practical challenges in diverse fields. INDEX TERMS time-varying problems, Zeroing neural network (ZNN),robustness,robot manipulator I. INTRODUCTION

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