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Tang Z, Mao M, Zhang Y, Tan N. Two Acceleration-Layer Configuration Amendment Schemes of Redundant Robot Arms Based on Zhang Neurodynamics Equivalency. Biomimetics (Basel) 2024; 9:435. [PMID: 39056876 PMCID: PMC11274851 DOI: 10.3390/biomimetics9070435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Revised: 07/10/2024] [Accepted: 07/11/2024] [Indexed: 07/28/2024] Open
Abstract
Two innovative acceleration-layer configuration amendment (CA) schemes are proposed to achieve the CA of constrained redundant robot arms. Specifically, by applying the Zhang neurodynamics equivalency (ZNE) method, an acceleration-layer CA performance indicator is derived theoretically. To obtain a unified-layer inequality constraint by transforming from angle-layer and velocity-layer constraints to acceleration-layer constraints, five theorems and three corollaries are theoretically derived and rigorously proved. Then, together with the unified acceleration-layer bound constraint, an enhanced acceleration-layer CA scheme specially considering three-layer time-variant physical limits is proposed, and a simplified acceleration-layer CA scheme considering three-layer time-invariant physical limits is also proposed. The proposed CA schemes are finally formulated in the form of standard quadratic programming and are solved by a projection neurodynamics solver. Moreover, comparative simulative experiments based on a four-link planar arm and a UR3 spatial arm are performed to verify the efficacy and superiority of the proposed CA schemes. At last, physical experiments are conducted on a real Kinova Jaco2 arm to substantiate the practicability of the proposed CA schemes.
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Affiliation(s)
- Zanyu Tang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China; (Z.T.); (Y.Z.); (N.T.)
- School of Computer Science and Engineering, Jishou University, Jishou 416000, China
| | - Mingzhi Mao
- School of Software Engineering, Sun Yat-sen University, Zhuhai 519082, China
| | - Yunong Zhang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China; (Z.T.); (Y.Z.); (N.T.)
| | - Ning Tan
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China; (Z.T.); (Y.Z.); (N.T.)
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2
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Liu M, Chen X, Shang M, Li H. A Pseudoinversion-Free Method for Weight Updating in Broad Learning System. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:2378-2389. [PMID: 35839197 DOI: 10.1109/tnnls.2022.3190043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Neural networks have evolved into one of the most critical tools in the field of artificial intelligence. As a kind of shallow feedforward neural network, the broad learning system (BLS) uses a training process based on random and pseudoinverse methods, and it does not need to go through a complete training cycle to obtain new parameters when adding nodes. Instead, it performs rapid update iterations on the basis of existing parameters through a series of dynamic update algorithms, which enables BLS to combine high efficiency and accuracy flexibly. The training strategy of BLS is completely different from the existing mainstream neural network training strategy based on the gradient descent algorithm, and the superiority of the former has been proven in many experiments. This article applies an ingenious method of pseudoinversion to the weight updating process in BLS and employs it as an alternative strategy for the dynamic update algorithms in the original BLS. Theoretical analyses and numerical experiments demonstrate the efficiency and effectiveness of BLS aided with this method. The research presented in this article can be regarded as an extended study of the BLS theory, providing an innovative idea and direction for future research on BLS.
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Qin C, Chen L, Cai Z, Liu M, Jin L. Long short-term memory with activation on gradient. Neural Netw 2023; 164:135-145. [PMID: 37149915 DOI: 10.1016/j.neunet.2023.04.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 01/03/2023] [Accepted: 04/18/2023] [Indexed: 05/09/2023]
Abstract
As the number of long short-term memory (LSTM) layers increases, vanishing/exploding gradient problems exacerbate and have a negative impact on the performance of the LSTM. In addition, the ill-conditioned problem occurs in the training process of LSTM and adversely affects its convergence. In this work, a simple and effective method of the gradient activation is applied to the LSTM, while empirical criteria for choosing gradient activation hyperparameters are found. Activating the gradient refers to modifying the gradient with a specific function named the gradient activation function. Moreover, different activation functions and different gradient operations are compared to prove that the gradient activation is effective on LSTM. Furthermore, comparative experiments are conducted, and their results show that the gradient activation alleviates the above problems and accelerates the convergence of the LSTM. The source code is publicly available at https://github.com/LongJin-lab/ACT-In-NLP.
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Affiliation(s)
- Chuan Qin
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China; The State Key Laboratory of Tibetan Intelligent Information Processing and Application, Qinghai Normal University, Xining 810008, China
| | - Liangming Chen
- Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China; Chongqing School, University of Chinese Academy of Sciences, Chongqing 400714, China
| | - Zangtai Cai
- The State Key Laboratory of Tibetan Intelligent Information Processing and Application, Qinghai Normal University, Xining 810008, China
| | - Mei Liu
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China; The State Key Laboratory of Tibetan Intelligent Information Processing and Application, Qinghai Normal University, Xining 810008, China.
| | - Long Jin
- Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China.
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Shi Y, Sheng W, Li S, Li B, Sun X, Gerontitis DK. A direct discretization recurrent neurodynamics method for time-variant nonlinear optimization with redundant robot manipulators. Neural Netw 2023; 164:428-438. [PMID: 37182345 DOI: 10.1016/j.neunet.2023.04.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 03/31/2023] [Accepted: 04/21/2023] [Indexed: 05/16/2023]
Abstract
Discrete time-variant nonlinear optimization (DTVNO) problems are commonly encountered in various scientific researches and engineering application fields. Nowadays, many discrete-time recurrent neurodynamics (DTRN) methods have been proposed for solving the DTVNO problems. However, these traditional DTRN methods currently employ an indirect technical route in which the discrete-time derivation process requires to interconvert with continuous-time derivation process. In order to break through this traditional research method, we develop a novel DTRN method based on the inspiring direct discrete technique for solving the DTVNO problem more concisely and efficiently. To be specific, firstly, considering that the DTVNO problem emerging in the discrete-time tracing control of robot manipulator, we further abstract and summarize the mathematical definition of DTVNO problem, and then we define the corresponding error function. Secondly, based on the second-order Taylor expansion, we can directly obtain the DTRN method for solving the DTVNO problem, which no longer requires the derivation process in the continuous-time environment. Whereafter, such a DTRN method is theoretically analyzed and its convergence is demonstrated. Furthermore, numerical experiments confirm the effectiveness and superiority of the DTRN method. In addition, the application experiments of the robot manipulators are presented to further demonstrate the superior performance of the DTRN method.
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Affiliation(s)
- Yang Shi
- School of Information Engineering, Yangzhou University, Yangzhou 225127, China; Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou 225127, China.
| | - Wangrong Sheng
- School of Information Engineering, Yangzhou University, Yangzhou 225127, China; Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou 225127, China
| | - Shuai Li
- College of Engineering, Swansea University, Fabian Way, Swansea, UK
| | - Bin Li
- School of Information Engineering, Yangzhou University, Yangzhou 225127, China; Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou 225127, China
| | - Xiaobing Sun
- School of Information Engineering, Yangzhou University, Yangzhou 225127, China; Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service, Yangzhou University, Yangzhou 225127, China
| | - Dimitrios K Gerontitis
- Department of Information and Electronic Engineering International Hellenic University, Thessaloniki, Greece
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Wang T, Chen Y. Event-triggered control of flexible manipulator constraint system modeled by PDE. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:10043-10062. [PMID: 37322923 DOI: 10.3934/mbe.2023441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The vibration suppression control of a flexible manipulator system modeled by partial differential equation (PDE) with state constraints is studied in this paper. On the basis of the backstepping recursive design framework, the problem of the constraint of joint angle and boundary vibration deflection is solved by using the Barrier Lyapunov function (BLF). Moreover, based on the relative threshold strategy, an event-triggered mechanism is proposed to save the communication workload between controller and actuator, which not only deals with the state constraints of the partial differential flexible manipulator system, but also effectively improves the system work efficiency. Good damping effect on vibration and the elevated system performance can be seen under the proposed control strategy. At the same time, the state can meet the constraints given in advance, and all system signals are bounded. The proposed scheme is effective, which is proven by simulation results.
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Affiliation(s)
- Tongyu Wang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
| | - Yadong Chen
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
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Chen X, Luo X, Jin L, Li S, Liu M. Growing Echo State Network With an Inverse-Free Weight Update Strategy. IEEE TRANSACTIONS ON CYBERNETICS 2023; 53:753-764. [PMID: 35316203 DOI: 10.1109/tcyb.2022.3155901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
An echo state network (ESN) draws widespread attention and is applied in many scenarios. As the most typical approach for solving the ESN, the matrix inverse operation of high computational complexity is involved. However, in the modern big data era, addressing the heavy computational burden problem is necessary. In order to reduce the computational load, an inverse-free ESN (IFESN) is proposed for the first time in this article. Besides, an incremental IFESN is constructed to attain the network topology with theoretical proof on the training error's monotone decline property. Simulations and experiments are conducted on several numerical and real-world time-series benchmarks, and corresponding results indicate that the proposed model is superior to some existing models and possesses excellent practical application potential. The source code is publicly available at https://github.com/LongJin-lab/the-supplementary-file-for-CYB-E-2021-04-0944.
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Xiao X, Jiang C, Mei Q, Zhang Y. Noise‐tolerate and adaptive coefficient zeroing neural network for solving dynamic matrix square root. CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2023. [DOI: 10.1049/cit2.12183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Affiliation(s)
- Xiuchun Xiao
- School of Electronics and Information Engineering Guangdong Ocean University Zhanjiang China
| | - Chengze Jiang
- School of Cyber Science and Engineering Southeast University Nanjing China
| | - Qixiang Mei
- School of Electronics and Information Engineering Guangdong Ocean University Zhanjiang China
| | - Yudong Zhang
- School of Computing and Mathematical Sciences University of Leicester Leicester UK
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Wang G, Liu Y, Sun Y, Yu J, Sun Z. Generalized zeroing neural dynamics model for online solving time-varying cube roots problem with various external disturbances in different domains. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.12.058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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9
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Zhang B, Lan X, Wang G, Pang Z, Zhang X, Sun Z. A noise-suppressing neural network approach for upper limb human-machine interactive control based on sEMG signals. Front Neurorobot 2022; 16:1047325. [DOI: 10.3389/fnbot.2022.1047325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Accepted: 10/11/2022] [Indexed: 11/06/2022] Open
Abstract
The use of upper limb rehabilitation robots to assist the affected limbs for active rehabilitation training is an inevitable trend in the field of rehabilitation medicine. In particular, the active motion intention-based control of the upper limb rehabilitation robots to assist subjects in rehabilitation training is a hot research topic in human-computer interaction control. Therefore, improving the accuracy of active motion intention recognition is the premise of the human-machine interaction controller design. Furthermore, there are external disturbances (bounded/unbounded disturbances) during rehabilitation training, which seriously threaten the safety of subjects. Thereby, eliminating external disturbances (especially unbounded disturbances) is the difficulty and key to the human-machine interaction control of the upper limb rehabilitation robots. In response to these problems, based on the surface electromyogram signal of the human upper limb, this paper proposes a fuzzy neural network active motion intention recognition method to explore the internal connection between the surface electromyogram signal of the human upper limb and active motion intention, and improve the real-time and accuracy of recognition. Based on this, two types of human-machine interaction controllers, which can be called as zeroing neural network controller and noise-suppressing zeroing neural network controller are designed to establish a safe and comfortable training environment to avoid secondary damage to the affected limb. Numerical experiments verify the feasibility and effectiveness of the proposed theories and methods.
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Soufi Enayati AM, Zhang Z, Najjaran H. A methodical interpretation of adaptive robotics: Study and reformulation. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.09.114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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11
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Direct derivation scheme of DT-RNN algorithm for discrete time-variant matrix pseudo-inversion with application to robotic manipulator. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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12
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Different discrete-time noise-suppression Z-type models for online solving time-varying and time-invariant cube roots in real and complex domains: Application to fractals. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.05.089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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13
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A GNN for repetitive motion generation of four-wheel omnidirectional mobile manipulator with nonconvex bound constraints. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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14
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Distributed adaptive fixed-time neural networks control for nonaffine nonlinear multiagent systems. Sci Rep 2022; 12:8459. [PMID: 35590095 PMCID: PMC9120193 DOI: 10.1038/s41598-022-12634-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 05/12/2022] [Indexed: 11/11/2022] Open
Abstract
This paper, with the adaptive backstepping technique, presents a novel fixed-time neural networks leader–follower consensus tracking control scheme for a class of nonaffine nonlinear multiagent systems. The expression of the error system is derived, based on homeomorphism mapping theory, to formulate a set of distributed adaptive backstepping neural networks controllers. The weights of the neural networks controllers are trained, by an adaptive law based on fixed-time theory, to determine the adaptive control input. The control algorithm can guarantee that the output of the follower agents of the system effectively follow the output of the leader of the system in a fixed time, while the upper bound of the settling time can be calculated without initial parameters. Finally, a simulation example is presented to demonstrate the effectiveness of the proposed consensus tracking control approach. A step-by-step procedure for engineers and researchers interested in applications is proposed.
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Li Z, Li S. Kinematic Control of Manipulator with Remote Center of Motion Constraints Synthesised by a Simplified Recurrent Neural Network. Neural Process Lett 2021. [DOI: 10.1007/s11063-021-10678-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
AbstractRedundancy manipulators need favorable redundancy resolution to obtain suitable control actions to guarantee accurate kinematic control. Among numerous kinematic control applications, some specific tasks such as minimally invasive manipulation/surgery require the distal link of a manipulator to translate along such fixed point. Such a point is known as remote center of motion (RCM) to constrain motion planning and kinematic control of manipulators. Recurrent neural network (RNN) which possesses parallel processing ability, is a powerful alternative and has achieved success in conventional redundancy resolution and kinematic control with physical constraints of joint limits. However, up to now, there still is few related works on the RNNs for redundancy resolution and kinematic control of manipulators with RCM constraints considered yet. In this paper, for the first time, an RNN-based approach with a simplified neural network architecture is proposed to solve the redundancy resolution issue with RCM constraints, with a new and general dynamic optimization formulation containing the RCM constraints investigated. Theoretical results analyze and convergence properties of the proposed simplified RNN for redundancy resolution of manipulators with RCM constraints. Simulation results further demonstrate the efficiency of the proposed method in end-effector path tracking control under RCM constraints based on a redundant manipulator.
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Liu M, Ma D, Li S. Neural dynamics for adaptive attitude tracking control of a flapping wing micro aerial vehicle. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.05.088] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Liu M, Peng B, Shang M. Lower limb movement intention recognition for rehabilitation robot aided with projected recurrent neural network. COMPLEX INTELL SYST 2021. [DOI: 10.1007/s40747-021-00341-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
AbstractFor the lower limb rehabilitation robot, how to better realize intention recognition is the key issue in the practical application. Recognition of the patient’s movement intention is a challenging research work, which needs to be studied from the shallow to the deep. Specifically, it is necessary to ensure that the movement intention of the normal person can be accurately recognized, and then improve the model to realize the recognition of the movement intention of the patients. Therefore, before studying the patient’s movement intention, it is essential to consider the normal person first, which is also for safety considerations. In recent years, a new Hill-based muscle model has been demonstrated to be capable of directly estimating the joint angle intention in an open-loop form. On this basis, by introducing a recurrent neural network (RNN), the whole prediction process can achieve more accuracy in a closed-loop form. However, for the traditional RNN algorithms, the activation function must be convex, which brings some limitations to the solution of practical problems. Especially, when the convergence speed of the traditional RNN model is limited in the practical applications, as the error continues to decrease, the convergence performance of the traditional RNN model will be greatly affected. To this end, a projected recurrent neural network (PRNN) model is proposed, which relaxes the condition of the convex function and can be used in the saturation constraint case. In addition, the corresponding theoretical proof is given, and the PRNN method with saturation constraint has been successfully applied in the experiment of intention recognition of lower limb movement compared with the traditional RNN model.
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