Zeroing neural network for bound-constrained time-varying nonlinear equation solving and its application to mobile robot manipulators

Ma, Zhisheng; Song, Yu; Han, Yuxing; Guo, Dongsheng

doi:10.1007/s00521-021-06068-6

Cited by 16 publications

(2 citation statements)

References 30 publications

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“…As a powerful approach for solving time-varying problems, the conventional zeroing neural network (CZNN) proposed in Zhang et al ( 2002 ) has been thoroughly investigated in recent years (Miao et al, 2015 ; Xiao et al, 2017 ; Gerontitis et al, 2022 ; Sun et al, 2022 ; Zhang and Zheng, 2022 ). Ma et al ( 2021 ) proposed a new ZNN model to solve the bound-constrained time-varying nonlinear equation, which has been applied to the mobile robot manipulator. Chen et al proposed a multi-constrained ZNN.…”

Section: Introductionmentioning

confidence: 99%

Robust control for a tracked mobile robot based on a finite-time convergence zeroing neural network

Cao,

Liu,

2023

Front. Neurorobot.

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IntroductionSince tracked mobile robot is a typical non-linear system, it has been a challenge to achieve the trajectory tracking of tracked mobile robots. A zeroing neural network is employed to control a tracked mobile robot to track the desired trajectory.MethodsA new fractional exponential activation function is designed in this study, and the implicit derivative dynamic model of the tracked mobile robot is presented, termed finite-time convergence zeroing neural network. The proposed model is analyzed based on the Lyapunov stability theory, and the upper bound of the convergence time is given. In addition, the robustness of the finite-time convergence zeroing neural network model is investigated under different error disturbances.Results and discussionNumerical experiments of tracking an eight-shaped trajectory are conducted successfully, validating the proposed model for the trajectory tracking problem of tracked mobile robots. Comparative results validate the effectiveness and superiority of the proposed model for the kinematical resolution of tracked mobile robots even in a disturbance environment.

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Section: Introductionmentioning

confidence: 99%

Robust control for a tracked mobile robot based on a finite-time convergence zeroing neural network

Cao,

Liu,

2023

Front. Neurorobot.

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“…Chen et al proposed a novel supertwisting ZNN to address the tracking control of a robot manipulator [17] which combines SMC and ZNN successfully. Ma et al developed a ZNN for a bound-constrained omnidirectional mobile robot manipulator by introducing a time-varying non-negative vector [18].…”

Section: Introductionmentioning

confidence: 99%

A Novel Zeroing Neural Network Control Scheme for Tracked Mobile Robot Based on an Extended State Observer

Cao,

2023

Applied Sciences

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A novel zeroing neural network control scheme based on an extended state observer is proposed for the trajectory tracking of a tracked mobile robot which is subject to unknown external disturbances and uncertainties. To estimate unknown lumped disturbances and unmeasured velocities, a third-order fixed-time extended state observer is proposed, and the observation errors converge to zero in fixed time. Based on the estimated values, the zeroing neural network controller is designed for a tracked mobile robot to track an eight shape. The stability of the system is analyzed based on Lyapunov theory. Simulation results are illustrated to show the effectiveness of the proposed control scheme.

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Novel activation functions-based ZNN models for fixed-time solving dynamirc Sylvester equation

Jin

Zhu

Gong

et al. 2022

Neural Comput & Applic

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Zeroing neural network for bound-constrained time-varying nonlinear equation solving and its application to mobile robot manipulators

Cited by 16 publications

References 30 publications

Robust control for a tracked mobile robot based on a finite-time convergence zeroing neural network

Robust control for a tracked mobile robot based on a finite-time convergence zeroing neural network

A Novel Zeroing Neural Network Control Scheme for Tracked Mobile Robot Based on an Extended State Observer

Novel activation functions-based ZNN models for fixed-time solving dynamirc Sylvester equation

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