A time controlling neural network for time‐varying QP solving with application to kinematics of mobile manipulators

Kong, Ying; Jiang, Yunliang; Zhou, Junwen; Wu, Huifeng

doi:10.1002/int.22304

Cited by 4 publications

(5 citation statements)

References 32 publications

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“…The wall-climbing welding mobile robot studied in this paper is a complex redundant system composed of a moving platform and robot arm. The establishment of its kinematic mathematical model is the basis of its research and control [29,30].…”

Section: Controller Design Of the Robotmentioning

confidence: 99%

Trajectory Tracking Control of Mobile Manipulator Based on Improved Sliding Mode Control Algorithm

Cui,

Song,

Zheng

et al. 2024

Processes

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Research on trajectory tracking control for climbing welding robots holds significant importance in the field of automated welding. However, existing trajectory tracking methods suffer from issues such as jitter and slow speed. In this paper, an improved sliding mode control strategy is proposed based on the self-designed wall-climbing welding mobile manipulator. Firstly, a new adaptive sliding mode control strategy is proposed for the mobile platform based on the kinematic model. By introducing a new approach law, the controller is designed when the distance between the center of mass is unknown. Secondly, regarding the manipulator, we analyze simplified dynamic equations, extract uncertain components, and utilize a CNN for compensation. This compensation strategy is integrated into the sliding mode control law, achieving precise control over the manipulator and effectively resolving issues like slow tracking speeds, large errors, and chattering. The stability of the robot control system is proved by the Lyapunov function. Through simulation analysis and experimental validation, the proposed control method is confirmed to be feasible and superior.

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Section: Controller Design Of the Robotmentioning

confidence: 99%

Trajectory Tracking Control of Mobile Manipulator Based on Improved Sliding Mode Control Algorithm

Cui,

Song,

Zheng

et al. 2024

Processes

View full text Add to dashboard Cite

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“…gy gy gy gy (16) To satisfy universality, we can get two circumstances according to the magnitude of the error modulus: the first is…”

Section: Stability Analysismentioning

confidence: 99%

“…As a significant kind of neural network, 10–12 recurrent neural networks (RNNs) are widespreadly used in optimization, 13 image classification, 14 and robot control 15,16 . Based on RNNs, Yi et al proposed the gradient neural networks (GNNs) which used to deal with the time‐invariant problem 17 .…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, parallel processing methods such as neural networks which can quickly obtain accurate solutions and reduce computational complexity are more widely used.As a significant kind of neural network, 10-12 recurrent neural networks (RNNs) are widespreadly used in optimization, 13 image classification, 14 and robot control. 15,16 Based on RNNs, Yi et al proposed the gradient neural networks (GNNs) which used to deal with the time-invariant problem. 17 For the sake of overcoming the disadvantage of GNNs that the residual error can not be eliminated over time, with regard to the time-variant problems, Zhang et al 18 put forward zeroing neural networks (ZNNs).…”

mentioning

confidence: 99%

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

Dai

Luo

Xiao

et al. 2022

Int J of Intelligent Sys

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On account of the rapid progress of zeroing neural network (ZNN) and the extensive use of fuzzy logic system (FLS), this article proposes an intelligent fuzzy robustness ZNN (IFR‐ZNN) model and applies it to solving the time‐variant Stein matrix equation (TVSME) problem. Be different from ZNN models before, the IFR‐ZNN model uses a fuzzy parameter as the design parameter and adopts a first proposed improved nonlinear piecewise activation function. Particularly, the FLS that generates the fuzzy parameter utilizes an improved membership function of nonuniform distribution which can improve the adaptability and robustness of the IFR‐ZNN model. Based on the above two optimizations, the proposed IFR‐ZNN model possesses three significant advantages: (1) fixed‐time convergence independent of initial states; (2) superior robustness to tolerate two kinds of noises simultaneously; and (3) better adaptiveness based on computational error. Besides, the upper bounds of fixed‐time convergence of the IFR‐ZNN model under noisy or non‐noisy situations are calculated theoretically, and the stability as well as the excellent adaptability are analyzed in detail. Finally, simulation comparison results manifest the availability and meliority of the proposed IFR‐ZNN model in solving the TVSME problem.

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“…Due to their flexibility, ANNs can be used in different applications, such as universal function approximator, process control and robotics, 26,27 pattern classifier, [28][29][30] time series prediction, function optimization, computer vision, 31 large-scale global optimization, 32 image authenticity verification, 33 time-varying quadratic programming, 34 and image improvement and restoration. 35 Other alternative techniques for image improvement and reconstruction have also stood out in the scientific community, as can be seen in the works [36][37][38].…”

Section: Annsmentioning

confidence: 99%

A neural autonomous robotic manipulator with three degrees of freedom

Santos

Palácios

Mendonça

et al. 2021

Int J of Intelligent Sys

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This study focuses on the development of a robotic arm with three degrees of freedom designed for different applications, especially in industry, where the main contribution is the use of a low‐cost robotic arm manipulator, which with the proper adjustments, mainly of speed, aims to add the concept of collaborative robotics in its most advanced versions. This study employs some classical principles of robotics, such as acquisition, image processing, and object manipulation. In this context, artificial neural networks were used for two essential functionalities: camera calibration and inverse kinematics resolution. From the results obtained with the use of the artificial neural networks (ANNs), it was possible to control the trajectory of the servo motors with the aid of software support. Also, by employing ANNs and using computer vision techniques, the robotic manipulator achieves the objectives of manipulating objects in space with a relatively acceptable error rate, considering the low‐cost proposal applied. This manipulator, at first, has a didactic purpose, but the concepts and applications can be used in different areas of the industry.

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A time controlling neural network for time‐varying QP solving with application to kinematics of mobile manipulators

Cited by 4 publications

References 32 publications

Trajectory Tracking Control of Mobile Manipulator Based on Improved Sliding Mode Control Algorithm

Trajectory Tracking Control of Mobile Manipulator Based on Improved Sliding Mode Control Algorithm

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

A neural autonomous robotic manipulator with three degrees of freedom

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