An intelligent fuzzy robustness ZNN model with fixed‐time convergence for time‐variant Stein matrix equation

Dai, Jianhua; Luo, Liu; Xiao, Lin; Jia, Lei; Li, Xiaopeng

doi:10.1002/int.23058

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…Another research stream is to use carefully selected parameters defined in fuzzy environments. Such research will be a continuation of research presented in [25,26,38,39].…”

mentioning

confidence: 77%

Recurrent Neural Network Models Based on Optimization Methods

et al. 2022

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Many researchers have addressed problems involving time-varying (TV) general linear matrix equations (GLMEs) because of their importance in science and engineering. This research discusses and solves the topic of solving TV GLME using the zeroing neural network (ZNN) design. Five new ZNN models based on novel error functions arising from gradient-descent and Newton optimization methods are presented and compared to each other and to the standard ZNN design. Pseudoinversion is involved in four proposed ZNN models, while three of them are related to Newton’s optimization method. Heterogeneous numerical examples show that all models successfully solve TV GLMEs, although their effectiveness varies and depends on the input matrix.

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“…Another research stream is to use carefully selected parameters defined in fuzzy environments. Such research will be a continuation of research presented in [25,26,38,39].…”

mentioning

confidence: 77%

Recurrent Neural Network Models Based on Optimization Methods

et al. 2022

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“…The design process for the ET-PTANN model with event triggering is described as follows. Building upon (17), an event-triggered mechanism is added to the evolution formula, leading to the following new evolution formula:…”

Section: Et-ptann Model With Event Triggermentioning

confidence: 99%

Efficient Predefined-Time Adaptive Neural Networks for Computing Time-Varying Tensor Moore–Penrose Inverse

Qi,

Ning,

Xiao

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

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This paper proposes predefined-time adaptive neural network (PTANN) and event-triggered PTANN (ET-PTANN) models to efficiently compute time-varying tensor Moore-Penrose inverse. The PTANN model incorporates a novel adaptive parameter and activation function, enabling it to achieve strongly predefined-time convergence. Unlike traditional time-varying parameters that increase over time, the adaptive parameter is proportional to the error norm, thereby better allocating computational resources and improving efficiency. To further enhance efficiency, the ET-PTANN model combines an event trigger with the evolution formula, resulting in the adjustment of step size and reduction of computation frequency compared to the PTANN model. By conducting mathematical derivations, the paper derives the upper bound of convergence time for the proposed neural network models and determines the minimum execution interval for the event trigger. A simulation example demonstrates that the PTANN and ET-PTANN models outperform other related neural network models in terms of computational efficiency and convergence rate. Finally, the practicality of the PTANN and ET-PTANN models is demonstrated through their application to mobile sound source localization.

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“…An error redefined neural network is proposed in [21] to control the mobile redundant manipulator to perform the tracking task with the redefined error monitoring function taken into account [20]. Zheng et al [22] and Dai et al [23] proposed a new controller design based on adaptive multilayer neurodynamics in the ZNN framework, implemented the time-varying trajectory tracking task with external interference and model uncertainty. Besides the predefined-time convergence ZNN [19], and intelligent fuzzy robustness ZNN [24], are also studied to solve the time-varying questions.…”

Section: Introductionmentioning

confidence: 99%

Force Tracking Control of Lower Extremity Exoskeleton Based on a New Recurrent Neural Network

Cao,

Chen,

Gao

et al. 2024

Applied Bionics and Biomechanics

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The lower extremity exoskeleton can enhance the ability of human limbs, which has been used in many fields. It is difficult to develop a precise force tracking control approach for the exoskeleton because of the dynamics model uncertainty, external disturbances, and unknown human–robot interactive force lied in the system. In this paper, a control method based on a novel recurrent neural network, namely zeroing neural network (ZNN), is proposed to obtain the accurate force tracking. In the framework of ZNN, an adaptive RBF neural network (ARBFNN) is employed to deal with the system uncertainty, and a fixed-time convergence disturbance observer is designed to estimate the external disturbance of the exoskeleton electrohydraulic system. The Lyapunov stability method is utilized to prove the convergence of all the closed-loop signals and the force tracking is guaranteed. The proposed control scheme’s (ARBFNN-FDO-ZNN) force tracking performances are presented and contrasted with the exponential reaching law-based sliding mode controller (ERL-SMC). The proposed scheme is superior to ERL-SMC with fast convergence speed and lower tracking error peak. Finally, experimental tests are conducted to verify the efficacy of the proposed controller for solving accurate force tracking control issues.

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An intelligent fuzzy robustness ZNN model with fixed‐time convergence for time‐variant Stein matrix equation

Cited by 5 publications

References 32 publications

Recurrent Neural Network Models Based on Optimization Methods

Recurrent Neural Network Models Based on Optimization Methods

Efficient Predefined-Time Adaptive Neural Networks for Computing Time-Varying Tensor Moore–Penrose Inverse

Force Tracking Control of Lower Extremity Exoskeleton Based on a New Recurrent Neural Network

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