Kinematics model identification and motion control of robot based on fast learning neural network

Sun, Xuehong

doi:10.1007/s12652-019-01459-z

Cited by 16 publications

(6 citation statements)

References 23 publications

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“…Wang et al [41] established the closed-form mapping from configuration perturbations to singular-value variations, proposed an efficient configuration search method, and performed self-calibration tests for redundant CDPRs. Sun [37] proposed a fast learning neural network for the identification of the motion model of modular robots to Fig. 1 3D model of the CDPR for 3D printing obtain the non-linear kinematics model of robots.…”

Section: Static Errors Self-calibrationmentioning

confidence: 99%

Calibration of Static Errors and Compensation of Dynamic Errors for Cable-driven Parallel 3D Printer

Qian,

Jiang,

Qian

et al. 2024

J Intell Robot Syst

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As rigid robots suffer from the higher inertia of their rigid links, cable-driven parallel robots (CDPRs) are more suitable for large-scale three-dimensional (3D) printing tasks due to their outstanding reconfigurability, high load-to-weight ratio, and extensive workspace. In this paper, a parallel 3D printing robot is proposed, comprising three pairs of driving cables to control the platform motion and three pairs of redundant cables to adjust the cable tension. To improve the motion accuracy of the moving platform, the static kinematic error model is established, and the error sensitivity coefficient is determined to reduce the dimensionality of the optimization function. Subsequently, the self-calibration positions are determined based on the maximum cable length error in the reachable workspace. A self-calibration method is proposed based on the genetic algorithm to solve the kinematic parameter deviations. Additionally, the dynamic errors are effectively reduced by compensating for the elastic deformation errors of the cable lengths. Furthermore, an experimental prototype is developed. The results of dynamic error compensation after the self-calibration indicate a 67.4% reduction in terms of the maximum error along the Z-axis direction. Finally, the developed prototype and proposed calibration and compensation methods are validated through the printing experiment.

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Section: Static Errors Self-calibrationmentioning

confidence: 99%

Calibration of Static Errors and Compensation of Dynamic Errors for Cable-driven Parallel 3D Printer

Qian,

Jiang,

Qian

et al. 2024

J Intell Robot Syst

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“…The neural network model, with input, hidden, and output layers, is valued for its self-learning, adaptability, and prediction abilities [25]. Consequently, this model has been widely employed across various disciplines [26,27].…”

Section: Prediction Model Based On Neural Networkmentioning

confidence: 99%

Prediction Model of Nitrogen, Phosphorus, and Potassium Fertilizer Application Rate for Greenhouse Tomatoes under Different Soil Fertility Conditions

Yu,

Luo,

Bai

et al. 2024

Agronomy

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To reach the target yield of crops, nutrient management is essential. Selecting the appropriate prediction model and adjusting the nutrient supply based on the actual situation can effectively improve the nutrient utilization efficiency, crop yield, and product quality. Therefore, a prediction model of the NPK fertilizer application rate for greenhouse tomatoes under the target yield was studied in this study. Under low, medium, and high soil fertility conditions, a neural network prediction model based on the sparrow search algorithm (SSA-NN), a neural network prediction model based on the improved sparrow search algorithm (ISSA-NN), and a neural network prediction model based on the hybrid algorithm (HA-NN) were used to predict the NPK fertilizer application rate for greenhouse tomatoes. The experimental results indicated that the evaluation indexes (i.e., the mean square error (MSE), explained variance score (EVS), and coefficient of determination (R2)) of the HA-NN prediction model proposed in this study were superior than the SSA-NN and ISSA-NN prediction models under three different soil fertility conditions. Under high soil fertility, compared with the SSA-NN prediction model, the MSE of the ISSA-NN and HA-NN prediction models decreased to 0.007 and 0.005, respectively; the EVS increased to 0.871 and 0.908, respectively; and the R2 increased to 0.862 and 0.899, respectively. This study showed that the HA–NN prediction model was superior in predicting the NPK fertilizer application rate for greenhouse tomatoes under three different soil fertility conditions. Due to the significance of NPK fertilizer application rate prediction for greenhouse tomatoes, this technique is expected to bring benefits to agricultural production management and decision support.

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“…The fast learning network (FLN) is a connection in a similar way to a single layer feed-forward neural network (SLFN), which consists of three layers: input, hidden, and output layer [32], [33]. The FLN, as depicted in Figure 1, is described in detail as follows.…”

Section: Overview Of Fast Learning Network (Fln)mentioning

confidence: 99%

Prediction of student satisfaction on mobile-learning by using fast learning network

Sultan

Abdulateef

Shtyat

2022

IJEECS

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The rapid advancement of mobile technologies over the past decade has had a significant impact on the appearance of M-learning applications. The research proposes the fast learning network model to investigate and identify the factors that affect student satisfaction in M-learning for the University of Tikrit students. The research model is conducted utilizing a questionnaire of 300 participating students based on variables. This research showed that the proposed model's perfor mance was superior to artificial neural network, <br /> k-nearest neighbors, and multilayer perceptron algorithms. The accuracy and specificity of predicting the student satisfaction coefficients in M-learning were 91.6% and 92.85%, respectively. The proposed findings demonstrate that diversity in the evaluation, teacher attitude and response, and quality of technology are key operators of student satisfaction.

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Kinematics model identification and motion control of robot based on fast learning neural network

Cited by 16 publications

References 23 publications

Calibration of Static Errors and Compensation of Dynamic Errors for Cable-driven Parallel 3D Printer

Calibration of Static Errors and Compensation of Dynamic Errors for Cable-driven Parallel 3D Printer

Prediction Model of Nitrogen, Phosphorus, and Potassium Fertilizer Application Rate for Greenhouse Tomatoes under Different Soil Fertility Conditions

Prediction of student satisfaction on mobile-learning by using fast learning network

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