Taking advantage of their inherent dexterity,robotic arms are competent in completing many tasks efficiently.As a result of the modeling complexity and kinematic uncertainty of robotic arms,model-free control paradigm...Taking advantage of their inherent dexterity,robotic arms are competent in completing many tasks efficiently.As a result of the modeling complexity and kinematic uncertainty of robotic arms,model-free control paradigm has been proposed and investigated extensively.However,robust model-free control of robotic arms in the presence of noise interference remains a problem worth studying.In this paper,we first propose a new kind of zeroing neural network(ZNN),i.e.,integration-enhanced noise-tolerant ZNN(IENT-ZNN)with integration-enhanced noisetolerant capability.Then,a unified dual IENT-ZNN scheme based on the proposed IENT-ZNN is presented for the kinematic control problem of both rigid-link and continuum robotic arms,which improves the performance of robotic arms with the disturbance of noise,without knowing the structural parameters of the robotic arms.The finite-time convergence and robustness of the proposed control scheme are proven by theoretical analysis.Finally,simulation studies and experimental demonstrations verify that the proposed control scheme is feasible in the kinematic control of different robotic arms and can achieve better results in terms of accuracy and robustness.展开更多
How to establish a self‐equilibrium configuration is vital for further kinematics and dynamics analyses of tensegrity mechanism.In this study,for investigating tensegrity form‐finding problems,a concise and efficien...How to establish a self‐equilibrium configuration is vital for further kinematics and dynamics analyses of tensegrity mechanism.In this study,for investigating tensegrity form‐finding problems,a concise and efficient dynamic relaxation‐noise tolerant zeroing neural network(DR‐NTZNN)form‐finding algorithm is established through analysing the physical properties of tensegrity structures.In addition,the non‐linear constrained opti-misation problem which transformed from the form‐finding problem is solved by a sequential quadratic programming algorithm.Moreover,the noise may produce in the form‐finding process that includes the round‐off errors which are brought by the approximate matrix and restart point calculating course,disturbance caused by external force and manufacturing error when constructing a tensegrity structure.Hence,for the purpose of suppressing the noise,a noise tolerant zeroing neural network is presented to solve the search direction,which can endow the anti‐noise capability to the form‐finding model and enhance the calculation capability.Besides,the dynamic relaxation method is contributed to seek the nodal coordinates rapidly when the search direction is acquired.The numerical results show the form‐finding model has a huge capability for high‐dimensional free form cable‐strut mechanisms with complicated topology.Eventually,comparing with other existing form‐finding methods,the contrast simulations reveal the excellent anti‐noise performance and calculation capacity of DR‐NTZNN form‐finding algorithm.展开更多
This paper presents a distributed scheme with limited communications, aiming to achieve cooperative motion control for multiple omnidirectional mobile manipulators(MOMMs).The proposed scheme extends the existing singl...This paper presents a distributed scheme with limited communications, aiming to achieve cooperative motion control for multiple omnidirectional mobile manipulators(MOMMs).The proposed scheme extends the existing single-agent motion control to cater to scenarios involving the cooperative operation of MOMMs. Specifically, squeeze-free cooperative load transportation is achieved for the end-effectors of MOMMs by incorporating cooperative repetitive motion planning(CRMP), while guiding each individual to desired poses. Then, the distributed scheme is formulated as a time-varying quadratic programming(QP) and solved online utilizing a noise-tolerant zeroing neural network(NTZNN). Theoretical analysis shows that the NTZNN model converges globally to the optimal solution of QP in the presence of noise. Finally, the effectiveness of the control design is demonstrated by numerical simulations and physical platform experiments.展开更多
Dear Editor,Aiming at the consensus tracking problem of a class of unknown heterogeneous nonlinear multiagent systems(MASs)with input constraints,a novel data-driven iterative learning consensus control(ILCC)protocol ...Dear Editor,Aiming at the consensus tracking problem of a class of unknown heterogeneous nonlinear multiagent systems(MASs)with input constraints,a novel data-driven iterative learning consensus control(ILCC)protocol based on zeroing neural networks(ZNNs)is proposed.First,a dynamic linearization data model(DLDM)is acquired via dynamic linearization technology(DLT).展开更多
This work presents a nonlinear integral-ameliorated model for handling dynamic optimization problems with affine constraints.They pose a challenge as their optimal solutions evolve with time.Traditional iteration-base...This work presents a nonlinear integral-ameliorated model for handling dynamic optimization problems with affine constraints.They pose a challenge as their optimal solutions evolve with time.Traditional iteration-based methods that exactly solve the problem at each time instant,fail to precisely and realtime track the solution due to computational and communication bottlenecks.Our model,through rigorous theoretical analyses,is able to reduce the optimality gap(i.e.,the difference between the model state and optimal solution)to zero in a finite time,and thus,track the solution online.Besides,perturbance is taken into account.We prove that under certain conditions,our model can totally tolerate an important kind of noise that we call“errorrelated noise”.In numerical experiments,compared with six existing methods,our model exhibits superior robustness when contaminated by the error-related noise.The key techniques in the model design involve employing the zeroing neural network to leverage time-derivative information,and introducing an integral term as well as the class C_(L)^(0)functions to enhance convergence and noise resistance.Finally,we establish a model-free control framework for a surgical manipulator with the remote-center-of-motion constraint and compare the performances of the framework based on different models in simulations.The results indicate that our model achieves the best performance among various models employed within the framework.展开更多
Nonconvex Activated Fuzzy Zeroing Neural Network-based(NAFZNN)and Nonconvex Activated Fuzzy Noise-Tolerant Zeroing Neural Network-based(NAFNTZNN)models are devised and analyzed,drawing inspiration from the classical Z...Nonconvex Activated Fuzzy Zeroing Neural Network-based(NAFZNN)and Nonconvex Activated Fuzzy Noise-Tolerant Zeroing Neural Network-based(NAFNTZNN)models are devised and analyzed,drawing inspiration from the classical ZNN/NTZNN-based model for online addressing Time-Varying Quadratic Programming Problems(TVQPPs)with Equality and Inequality Constraints(EICs)in noisy circumstances,respectively.Furthermore,the proposed NAFZNN model and NAFNTZNN model are considered as general proportion-differentiation controller,along with general proportion-integration-differentiation controller.Besides,theoretical results demonstrate the global convergence of both the NAFZNN and NAFNTZNN models for TVQPPs with EIC under noisy conditions.Moreover,numerical results illustrate the efficiency,robustness,and ascendancy of the NAFZNN and NAFZNN models in addressing TVQPPs online,exhibiting inherent noise tolerance.Ultimately,an application example to plant leaf disease identification is conducted to support the feasibility and efficacy of the designed NAFNTZNN model,which shows its potential practical value in the field of image recognition.展开更多
基金supported by the National Natural Science Foundation of China(62173352,62103112)the Guangdong Basic and Applied Basic Research Foundation(2021A1515012314)+1 种基金the Open Project of Shenzhen Institute of Artificial Intelligence and Robotics for Society(AC01202005006)the Key-Area Research and Development Program of Guangzhou(202007030004)。
文摘Taking advantage of their inherent dexterity,robotic arms are competent in completing many tasks efficiently.As a result of the modeling complexity and kinematic uncertainty of robotic arms,model-free control paradigm has been proposed and investigated extensively.However,robust model-free control of robotic arms in the presence of noise interference remains a problem worth studying.In this paper,we first propose a new kind of zeroing neural network(ZNN),i.e.,integration-enhanced noise-tolerant ZNN(IENT-ZNN)with integration-enhanced noisetolerant capability.Then,a unified dual IENT-ZNN scheme based on the proposed IENT-ZNN is presented for the kinematic control problem of both rigid-link and continuum robotic arms,which improves the performance of robotic arms with the disturbance of noise,without knowing the structural parameters of the robotic arms.The finite-time convergence and robustness of the proposed control scheme are proven by theoretical analysis.Finally,simulation studies and experimental demonstrations verify that the proposed control scheme is feasible in the kinematic control of different robotic arms and can achieve better results in terms of accuracy and robustness.
基金supported in part by the National Natural Science Foundation of China under grants 61873304,62173048,62106023in part by the China Postdoctoral Science Foundation Funded Project under grants 2018M641784 and 2019T120240+1 种基金also in part by the Key Science and Technology Projects of Jilin Province,China,under grant 20210201106GXalso in part by the Changchun Science and Technology Project under grant 21ZY41.
文摘How to establish a self‐equilibrium configuration is vital for further kinematics and dynamics analyses of tensegrity mechanism.In this study,for investigating tensegrity form‐finding problems,a concise and efficient dynamic relaxation‐noise tolerant zeroing neural network(DR‐NTZNN)form‐finding algorithm is established through analysing the physical properties of tensegrity structures.In addition,the non‐linear constrained opti-misation problem which transformed from the form‐finding problem is solved by a sequential quadratic programming algorithm.Moreover,the noise may produce in the form‐finding process that includes the round‐off errors which are brought by the approximate matrix and restart point calculating course,disturbance caused by external force and manufacturing error when constructing a tensegrity structure.Hence,for the purpose of suppressing the noise,a noise tolerant zeroing neural network is presented to solve the search direction,which can endow the anti‐noise capability to the form‐finding model and enhance the calculation capability.Besides,the dynamic relaxation method is contributed to seek the nodal coordinates rapidly when the search direction is acquired.The numerical results show the form‐finding model has a huge capability for high‐dimensional free form cable‐strut mechanisms with complicated topology.Eventually,comparing with other existing form‐finding methods,the contrast simulations reveal the excellent anti‐noise performance and calculation capacity of DR‐NTZNN form‐finding algorithm.
基金supported in part by the National Natural Science Foundation of China (62373065,61873304,62173048,62106023)the Innovation and Entrepreneurship Talent funding Project of Jilin Province(2022QN04)+1 种基金the Changchun Science and Technology Project (21ZY41)the Open Research Fund of National Mobile Communications Research Laboratory,Southeast University (2024D09)。
文摘This paper presents a distributed scheme with limited communications, aiming to achieve cooperative motion control for multiple omnidirectional mobile manipulators(MOMMs).The proposed scheme extends the existing single-agent motion control to cater to scenarios involving the cooperative operation of MOMMs. Specifically, squeeze-free cooperative load transportation is achieved for the end-effectors of MOMMs by incorporating cooperative repetitive motion planning(CRMP), while guiding each individual to desired poses. Then, the distributed scheme is formulated as a time-varying quadratic programming(QP) and solved online utilizing a noise-tolerant zeroing neural network(NTZNN). Theoretical analysis shows that the NTZNN model converges globally to the optimal solution of QP in the presence of noise. Finally, the effectiveness of the control design is demonstrated by numerical simulations and physical platform experiments.
基金supported by the National Nature Science Foundation of China(U21A20166)the Science and Technology Development Foundation of Jilin Province(20230508095RC)+2 种基金the Major Science and Technology Projects of Jilin Province and Changchun City(20220301033GX)the Development and Reform Commission Foundation of Jilin Province(2023C034-3)the Interdisciplinary Integration and Innovation Project of JLU(JLUXKJC2020202).
文摘Dear Editor,Aiming at the consensus tracking problem of a class of unknown heterogeneous nonlinear multiagent systems(MASs)with input constraints,a novel data-driven iterative learning consensus control(ILCC)protocol based on zeroing neural networks(ZNNs)is proposed.First,a dynamic linearization data model(DLDM)is acquired via dynamic linearization technology(DLT).
基金supported by the National Natural Science Foundation of China(62376290).
文摘This work presents a nonlinear integral-ameliorated model for handling dynamic optimization problems with affine constraints.They pose a challenge as their optimal solutions evolve with time.Traditional iteration-based methods that exactly solve the problem at each time instant,fail to precisely and realtime track the solution due to computational and communication bottlenecks.Our model,through rigorous theoretical analyses,is able to reduce the optimality gap(i.e.,the difference between the model state and optimal solution)to zero in a finite time,and thus,track the solution online.Besides,perturbance is taken into account.We prove that under certain conditions,our model can totally tolerate an important kind of noise that we call“errorrelated noise”.In numerical experiments,compared with six existing methods,our model exhibits superior robustness when contaminated by the error-related noise.The key techniques in the model design involve employing the zeroing neural network to leverage time-derivative information,and introducing an integral term as well as the class C_(L)^(0)functions to enhance convergence and noise resistance.Finally,we establish a model-free control framework for a surgical manipulator with the remote-center-of-motion constraint and compare the performances of the framework based on different models in simulations.The results indicate that our model achieves the best performance among various models employed within the framework.
基金supported in part by the Key Science and Technology Project of Jilin Province,China(No.20240304028SF)the Funding of Jilin Province Science and Technology(No.JJKH20240422KJ).
文摘Nonconvex Activated Fuzzy Zeroing Neural Network-based(NAFZNN)and Nonconvex Activated Fuzzy Noise-Tolerant Zeroing Neural Network-based(NAFNTZNN)models are devised and analyzed,drawing inspiration from the classical ZNN/NTZNN-based model for online addressing Time-Varying Quadratic Programming Problems(TVQPPs)with Equality and Inequality Constraints(EICs)in noisy circumstances,respectively.Furthermore,the proposed NAFZNN model and NAFNTZNN model are considered as general proportion-differentiation controller,along with general proportion-integration-differentiation controller.Besides,theoretical results demonstrate the global convergence of both the NAFZNN and NAFNTZNN models for TVQPPs with EIC under noisy conditions.Moreover,numerical results illustrate the efficiency,robustness,and ascendancy of the NAFZNN and NAFZNN models in addressing TVQPPs online,exhibiting inherent noise tolerance.Ultimately,an application example to plant leaf disease identification is conducted to support the feasibility and efficacy of the designed NAFNTZNN model,which shows its potential practical value in the field of image recognition.
