A New Modeling Method of Angle Measurement for Intelligent Ball Joint Based on BP-RBF Algorithm

Hu, Penghao; Lu, Ze-Xun; Zhang, Yuanqi; Liu, Shanlin; Dang, Xueming

doi:10.3390/app9142850

Cited by 8 publications

(4 citation statements)

References 15 publications

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“…To verify the decoupling method in Section 2, we write the simulation results shown in Figure 3 as a matrix shown in Equation (9). According to the decoupling operation method shown in Equation ( 12), we obtained the simulated results and compared the simulation ones with the theoretical data to verify the decoupling method.…”

Section: Finite Element Simulation and Analysismentioning

confidence: 99%

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A Novel Method for Detecting the Two-Degrees-of-Freedom Angular Displacement of a Spherical Pair, Based on a Capacitive Sensor

Yang

et al. 2022

Sensors

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The spherical pair has an important role in the inner frame of the stabilization mechanism of the aviation optoelectronic pod. However, its two-degrees-of-freedom (2-DOF) angular displacement signal is difficult to detect, seriously restricting its application in aviation optoelectronic pods. Therefore, this study proposes a new method to measure a spherical pair’s 2-DOF angular displacement using a spherical capacitive sensor. The capacitive sensor presented by this method realizes the measurement of the 2-DOF angular displacement of the spherical pair by integrating the spherical electrode groups in the ball head and the ball socket of the spherical pair. First, based on the geometric structure of the spherical pair, the structure of the capacitive sensor is designed, and the mathematical model for the capacitive sensor is deduced. Then, the sensor’s output capacitance, in different directions, is simulated by Ansoft Maxwell software. Finally, an experiment device is built for the measurement experiments. The simulation analysis and experimental results show that the spherical capacitive sensor has an approximately linear output in different directions, and the measured output capacitance is as high as 89.7% of the theoretical value. Compared with the existing sensors that measure the 2-DOF angular displacement signal of the ball pair, the sensor proposed in this study has an integrated structure, which can be integrated into the spherical pair. That makes it possible to apply the spherical pair to the inner frame of the aviation optoelectronic pod.

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Section: Finite Element Simulation and Analysismentioning

confidence: 99%

“…It is unsuitable for precise angle measurement. Therefore, both Shaohui Foong's team and Penghao Hu's team used artificial intelligence algorithms to improve the magnetic effect method [6][7][8][9][10][11]. After the improvement, the measurement accuracy and resolution were significantly improved.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Detecting the Two-Degrees-of-Freedom Angular Displacement of a Spherical Pair, Based on a Capacitive Sensor

Yang

et al. 2022

Sensors

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“…Second, the measurement model was established using an ANN algorithm. The experimental results showed that the measurement accuracy of the prototype after using the ANN algorithm greatly improved; the range was within ±20°, the single axis measurement accuracy was 4’, and the resolution was 15” [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

A New Method for Measuring the Rotational Angles of a Precision Spherical Joint Using Eddy Current Sensors

Zhao

Tang

et al. 2020

Sensors

Self Cite

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Precision spherical joint is a spherical motion pair that can realize rotation with three degrees of freedom. This joint is widely used in robots, parallel mechanisms, and high-end medical equipment, as well as in aerospace and other fields. However, the rotation orientation and angle cannot be determined when the joint is in passive motion. The real-time determination of the rotation orientation and angle is crucial to the improvement of the motion control accuracy of the equipment where the joint is installed in. In this study, a new measurement method that utilizes eddy current sensors is proposed to identify the special features of the joint ball and realize angle measurements indirectly. The basic idea is to manufacture the specific shape features on the ball without affecting its movement accuracy and mechanical performance. An eddy current sensor array is distributed in the ball socket. When the ball head rotates, the features on the ball opposite to the sensor, as well as the output signal of every eddy current sensor, change. The measurement model that establishes the relationship between the output signal of the eddy current sensor array and the rotation direction and angle of the ball head is constructed by learning and training an artificial neural network. A prototype is developed using the proposed scheme, and the model simulation and feasibility experiment are subsequently performed. Results show that the root mean square angular error of a single axis within a range of ±14° is approximately 20 min, which suggests the feasibility of the proposed method.

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“…Examples of shallow ML models are the support vector machine (SVM) invented by Cortes and Vapnik [2] in 1995, and neural networks (NN). Numerous shallow ML models have been deployed in many research fields, including speech and natural language processing, computer vision, and public opinion mining [3][4][5][6][7]. The linear and nonlinear shallow models have gained impressive results in regression and prediction studies, including the prediction of airport passenger throughput with high accuracy [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

An Integrated Approach to Air Passenger Index Prediction: Mutual Information Principle and Support Vector Regression (MI-SVR) Blended Model

Xiong¹,

Fan²,

Antwi³

et al. 2020

Preprint

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Air passenger traffic prediction is crucial for the effective operation of civil aviation airports. Despite some progress in this field, the prediction accuracy and methods need further improvement. This paper proposes an integrated approach to the prediction of air passenger index as follows. Firstly, the air passenger index is defined and classified by the K-means clustering method. Based on the mutual information (MI) principle, the information entropy is used to analyze and select the key influencing factors of air passenger travel. By incorporating the MI principle into the support vector regression (SVR) framework, this paper presents an innovative MI-SVR machine learning model used to predict the air passenger index. Finally, the proposed model is validated by passenger throughput data of the Shanghai Pudong International Airport, China. The experimental results prove the model feasibility and effectiveness by comparing them with conventional methods, such as ARIMA, LSTM, and other machine learning models, outperformed by the MI-SVR model. Besides, it is shown that the prediction effect of each model could be improved by introducing influencing factors based on mutual information. The main findings are considered instrumental to the airport operation and air traffic optimization.

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A New Modeling Method of Angle Measurement for Intelligent Ball Joint Based on BP-RBF Algorithm

Cited by 8 publications

References 15 publications

A Novel Method for Detecting the Two-Degrees-of-Freedom Angular Displacement of a Spherical Pair, Based on a Capacitive Sensor

A Novel Method for Detecting the Two-Degrees-of-Freedom Angular Displacement of a Spherical Pair, Based on a Capacitive Sensor

A New Method for Measuring the Rotational Angles of a Precision Spherical Joint Using Eddy Current Sensors

An Integrated Approach to Air Passenger Index Prediction: Mutual Information Principle and Support Vector Regression (MI-SVR) Blended Model

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