Gradient-Feedback Zhang Neural Network for Unconstrained Time-Variant Convex Optimization and Robot Manipulator Application

Fu, Zhuosong; Zhang, Yunong; Tan, Ning

doi:10.1109/tii.2023.3240737

Cited by 10 publications

(5 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With α = 2 and the initial state θ(0) = [0, 0, π 12 , π 3 , π 3 , π 3 , π 3 , π 3 ], experimental results are shown in Fig. 5, where the first row illustrates the results of the AAGND-1 model (22) and the second row illustrates results of the AAGND-2 model (24). 22) is about 10 −3 m, while the one of the AAGND-2 model ( 24) is about 4 −3 m. Consequently, the proposed AAGND models are efficient in the application on robot control.…”

Section: ) Application On Robot Controlmentioning

confidence: 99%

“…In the last decades, the zeroing neural dynamics (ZND) model originated from the Hopfield neural network has been widely investigated and applied [12]- [14]. It is endowed with predictive computing due to the utilization of time derivative of time-variant parameters, which can effectively Model in [8] Model in [17] Model in [10] Model in [12] Model in [21] Model in [22] This work Adaptive parameter…”

Section: Introductionmentioning

confidence: 99%

“…However, it also suffers from the inversion of the mass matrix because it contains the ZND model. Moreover, a gradient-feedback ZND model, which also is a variant of the CGND model, was proposed to solve time-variant optimization with superior performance [22].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Accelerated Adaptive Gradient Neural Dynamics Models for Solving Time-Variant Lyapunov Equation and Their Applications

Zhuang

Zou

et al. 2023

IEEE Access

View full text Add to dashboard Cite

The time-variant Lyapunov equation (TVLE) has played an important role in many fields due to its ubiquity and many neural dynamics models have been developed to obtain the online solution of the TVLE. In prevalent methods, the gradient neural dynamics (GND) models suffer from the large residual error due to the lack of predictive computing, while the zero neural dynamics (ZND) models have large computing complexity because of the inverse of the mass matrix in models. To mitigate these deficiencies, an adaptive parameter containing the time-derivative of time-variant parameters in the TVLE is added to the GND model to form the adaptive GND (AGND) model, which enables the AGND model predictive computing as ZND models and inherits the free of matrix inverse from the GND models. Moreover, two strategies are proposed to design the accelerated AGND (AAGND) models that enjoy a faster convergence rate. The accuracy and the convergence rate of AAGND models are theoretically analyzed, indicating that AAGND models achieve zero residual error and a faster convergence rate. In addition, numerical simulations and two applications are provided to verify the theoretical analyses and the efficiency of AAGND models. The experimental results demonstrate that the AAGND model can solve the TVLE with high accuracy and have great potentialities in applications.

show abstract

Section: ) Application On Robot Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Accelerated Adaptive Gradient Neural Dynamics Models for Solving Time-Variant Lyapunov Equation and Their Applications

Zhuang

Zou

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In recent years, with the rapid advancement of neural network research, many scholars have been committed to applying its formidable nonlinear modeling capability and efficient parallel computing ability to the domain of robotic arm motion control (Wang et al, 2021 ; Jin et al, 2024 ). This endeavor has given rise to a special kind of neural network known as the RNN (Xiao et al, 2021 ; Yan et al, 2022 ; Fu et al, 2023 ). For example, Xiao et al ( 2021 ) proposed a noise-enduring and finite-time convergent design formula is suggested to establish a novel RNN.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Xiao et al ( 2021 ) proposed a noise-enduring and finite-time convergent design formula is suggested to establish a novel RNN. Fu et al ( 2023 ) presented a gradient-feedback RNN to solve the unconstrained time-variant convex optimization problem.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang,

Han,

Qiu

2024

Front. Neurorobot.

View full text Add to dashboard Cite

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.

show abstract

Simplified Gradient-Zeroing Neuronet for Temporally-Variant Convex Objective Function Minimization

Yu,

Chen,

Mao

et al. 2024

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Gradient-Feedback Zhang Neural Network for Unconstrained Time-Variant Convex Optimization and Robot Manipulator Application

Cited by 10 publications

References 29 publications

Accelerated Adaptive Gradient Neural Dynamics Models for Solving Time-Variant Lyapunov Equation and Their Applications

Accelerated Adaptive Gradient Neural Dynamics Models for Solving Time-Variant Lyapunov Equation and Their Applications

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

Simplified Gradient-Zeroing Neuronet for Temporally-Variant Convex Objective Function Minimization

Contact Info

Product

Resources

About