A Novel Adaptive Sliding Mode Control of Robot Manipulator Based on RBF Neural Network and Exponential Convergence Observer

Li, Xiaoling; Gao, Hongliang; Xiong, Lang; Zhang, Hongcong; Li, Bing

doi:10.1007/s11063-023-11237-w

Cited by 4 publications

(2 citation statements)

References 28 publications

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“…Deng and Zeng [21] designed an adaptive SMC strategy based on RBFNN optimization for hydraulic servo control. Li and Gao [22] proposed an RBFNN-optimized adaptive parameter control strategy for robotic arm control. Fu and Zhou [23] designed an adaptive SMC strategy based on RBFNN optimization for a six-degree-of-freedom electro-hydraulic servo platform, achieving multi-variable coupling control of electro-hydraulics.…”

Section: Introductionmentioning

confidence: 99%

Design and Control Performance Optimization of Variable Structure Hydrostatic Drive Systems for Wheel Loaders

Wang,

Wang

et al. 2024

Machines

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The traditional loader drive system is based on the hydraulic torque converter as the key component, and its gear is shifted through the coordination of the clutch and gearbox, which greatly increases power loss and operator fatigue. To address the above problem, a variable-structure hydrostatic drive system is proposed. This system adopts a closed-loop design and adjusts the displacement of the pump and dual motors to follow the throttle opening of the vehicle, achieving automatic gear shifting and smooth speed regulation. It can also automatically change the system structure according to the vehicle’s speed, meeting the vehicle’s demand for rapid switching output of high torque and high speed. At the same time, in the displacement matching control process, an adaptive sliding mode control scheme based on radial basis function neural network compensation is proposed. This scheme designs an adaptive hyperparameter update strategy according to the characteristics of the system to effectively compensate for changes in uncertain factors. Experimental results show that, compared to traditional drive systems, this system has the characteristics of simple operation, smooth speed regulation, and high fuel economy.

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Section: Introductionmentioning

confidence: 99%

Design and Control Performance Optimization of Variable Structure Hydrostatic Drive Systems for Wheel Loaders

Wang,

Wang

et al. 2024

Machines

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“…For example, articles [13] and [14] adopt ltering vibration method and saturation function respectively, which reduce the vibration of sliding mode controller obviously. When the unknown disturbances and uncertainties are large enough, they will lead too large shaking [15], [16] . As references in, some intelligent methods were proposed to estimate the unknown disturbances, not only reduce jitter of controller, but make the sliding mode controller to get good robustness and tracking performance also.…”

Section: Introductionmentioning

confidence: 99%

The optimal control of the forages-sorting mechanical arm

Luo,

Zhang,

2024

Preprint

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Removing impurities from forages is a key step in fine feed process, in the process of sorting weeds and foreign objects from the forages, affected by the working environment of the mechanical arm, dust will cause nonlinear friction in the mechanical arm joints, besides, the different sorting objects cause the problem of uncertain loads, these factors affect the normal operation of the mechanical arm badly. In order to track impurities accurately by end effector of mechanical arm, Kane modeling method was adopted and different control algorithms were analyzed in this paper. Firstly, we designed a conventional PID control algorithm, and analyzed its feasibility condition based on Routh criterion. Secondly, to weaken the effect caused by nonlinear friction in mechanical arm joints and uncertain loads in end effector, we combined Radial Basis Function Neural Network(RBFNN) and Expansion State Observer(ESO) with sliding mode control, proposed an adaptive sliding mode controller based on fuzzy PD compensation(FASMC), numerical simulation demonstrated that the controller can reject the unknown effects of the system and achieved a very good control performances.

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Improvement of the Precision of a Two-Degree-of-Freedom Manipulator Arm PID Controller

Zahira,

Hamza,

Mamou

et al. 2023

2023 IEEE International Conference on Artificial Intelligence &Amp; Green Energy (ICAIGE)

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A Novel Adaptive Sliding Mode Control of Robot Manipulator Based on RBF Neural Network and Exponential Convergence Observer

Cited by 4 publications

References 28 publications

Design and Control Performance Optimization of Variable Structure Hydrostatic Drive Systems for Wheel Loaders

Design and Control Performance Optimization of Variable Structure Hydrostatic Drive Systems for Wheel Loaders

The optimal control of the forages-sorting mechanical arm

Improvement of the Precision of a Two-Degree-of-Freedom Manipulator Arm PID Controller

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