System identification and a model-based control strategy of motor driven system with high order flexible manipulator

Ding, Youshuang; Xiao, Xi; Huang, Xuanrui; Sun, Jiexiang

doi:10.1108/ir-01-2019-0012

Cited by 4 publications

(4 citation statements)

References 25 publications

(25 reference statements)

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“…The data from their study was utilized in current investigation (details of the fluid -Appendix B). The uncertainty in the data and its reliability for scientific analysis is crucial for algorithmic realization of a physical process, and numerous methods were proposed recently [60][61][62] which deal with analyzing the unreliability of the data. In the presented study water test were conducted to evaluate and verify the reliability of instrumentation and data acquisition system, followed by Yield Power Law (YPL) fluids after establishing strong confidence on measurement.…”

Section: Description Of Experimental Datamentioning

confidence: 99%

Machine Learning Methods for Herschel–Bulkley Fluids in Annulus: Pressure Drop Predictions and Algorithm Performance Evaluation

et al. 2020

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Accurate measurement of pressure drop in energy sectors especially oil and gas exploration is a challenging and crucial parameter for optimization of the extraction process. Many empirical and analytical solutions have been developed to anticipate pressure loss for non-Newtonian fluids in concentric and eccentric pipes. Numerous attempts have been made to extend these models to forecast pressure loss in the annulus. However, there remains a void in the experimental and theoretical studies to establish a model capable of estimating it with higher accuracy and lower computation. Rheology of fluid and geometry of system cumulatively dominate the pressure gradient in an annulus. In the present research, the prediction for Herschel-Bulkley fluids is analyzed by Bayesian Neural Network (BNN), random forest (RF), artificial neural network (ANN), and support vector machines (SVM) for pressure loss in the concentric and eccentric annulus. This study emphasizes on the performance evaluation of given algorithms and their pitfalls in predicting accurate pressure drop. The predictions of BNN and RF exhibit the least mean absolute error of 3.2% and 2.57%, respectively, and both can generalize the pressure loss calculation. The impact of each input parameter affecting the pressure drop is quantified using the RF algorithm.

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Section: Description Of Experimental Datamentioning

confidence: 99%

Machine Learning Methods for Herschel–Bulkley Fluids in Annulus: Pressure Drop Predictions and Algorithm Performance Evaluation

et al. 2020

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“…A coupling dynamic model of a manipulator system with the harmonic drive is identified as experimental (Wang and Lou, 2019). A novel method based on a fuzzy-Kalman filter is proposed to estimate unknown parameters of a high-order manipulator (Ding et al, 2019). A new identification approach is implemented to a rotating single-link flexible manipulator and two mechanical structures (Tarvirdizadeh et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Measurement and modeling of a flexible manipulator for vibration control using five-segment S-curve motion

Malgaca

Lök

2021

Transactions of the Institute of Measurement and Control

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User designed manipulators are widely used in industry as a part of automation. The design of lighter robotic arms is required for less energy consumption. Joints, structural features, and payload affect the dynamic behavior of manipulators. Even if the arms have sufficient structural rigidity, joints, or payloads further increase their flexibility. These factors should be considered at the design stage. Flexibility causes vibrations, and these vibrations negatively affect robot repeatability and processing speed. Reducing the vibration levels of flexible manipulators is an attractive issue for engineers and researchers. Accurate estimation of the mathematical model of flexible manipulators increases the success of vibration control. In this paper, the modeling and experiments for vibration control of a single-axis flexible curved manipulator with payload are considered. The experimental system is introduced to collect vibration responses synchronously at the tip of the curved manipulator for angular velocity input. The mathematical model of the manipulator is estimated using the continuous-time system identification (CTSI) method with a black-box model based on the experimental input/output (I/O) signals. A five-segment S-curve motion input based on the modal parameters is designed to suppress residual vibrations. Vibration control is successfully performed for different deceleration times of the designed S-curve motion input. The results showed that the residual vibrations from experiments and predicted models matched well for different cases depending on payload, angular position, and motion time.

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“…It was determined that the decision tree algorithm was correct and produced significantly different results with Bayes net and JRip rules being two types of rules. The CRISP methodology was exploited to test student performance in a c++ course [7], and the researcher in this study compared the performance of two decision trees viz. ID3 and C4.5 with naive bayes algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…The researchers Zhu et al [5], also explored analog classifiers including ID3 and C4.5 on datasets relating to MCA students, with the primary objective of predicting and improving student performance [8]. Kotsiantis et al [7], used the Naive Bayes ensemble method, neural network and the WINDOWS algorithm to predict student performance [9].…”

Section: Introductionmentioning

confidence: 99%

An Ensemble and Filtering-Based System for Predicting Educational Data Mining

Hananto¹

2021

J. Appl. Data Sci.

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When developing a prediction paradigm, an ensemble technique such as boosting is used. It is built on a heuristic framework. Generally speaking, engineering ensemble learning is more accurate than individual classifiers when it comes to making predictions. Consequently, numerous ensemble strategies have been presented in this work, particularly to provide a more complete understanding of the essential methods in general. Researchers have experimented with boosting methods to forecast student performance as part of a variety of ensemble techniques. The researchers employed improvement approaches to construct an accurate predictive educational model, which was based on a key phenomena seen in categorization and prediction operations. In light of the uniqueness and originality of the suggested strategy in educational data mining, the researchers used augmentation strategies in order to construct an accurate predictive pedagogical model. Tenfold cross-validation was performed to evaluate the effectiveness of the basic classifiers, which included the random tree, the j48, the knn, and the Naive Bayes. The random tree was found to be the most effective classifier. Several additional screening techniques, including oversampling (SMOTE) and undersampling (Spread subsampling), were utilized to analyze any statistically significant variations in results between the meta and base classifiers that had been identified between the meta and base classifiers. The use of ensemble and screening strategies, as compared to the use of standard classifiers, has demonstrated considerable gains in predicting student performance, as has the use of either strategy alone. Furthermore, after the completion of a performance research on each approach, two new prediction models have been established on the basis of the improved results gained thus far.

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System identification and a model-based control strategy of motor driven system with high order flexible manipulator

Cited by 4 publications

References 25 publications

Machine Learning Methods for Herschel–Bulkley Fluids in Annulus: Pressure Drop Predictions and Algorithm Performance Evaluation

Machine Learning Methods for Herschel–Bulkley Fluids in Annulus: Pressure Drop Predictions and Algorithm Performance Evaluation

Measurement and modeling of a flexible manipulator for vibration control using five-segment S-curve motion

An Ensemble and Filtering-Based System for Predicting Educational Data Mining

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