Linchao Zhao

Bio: Linchao Zhao is an academic researcher from Hefei University of Technology. The author has contributed to research in topics: Eddy-current sensor & Rotation (mathematics). The author has an hindex of 2, co-authored 3 publications receiving 5 citations.

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

Abstract: 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.

Research on Measurement Method of Spherical Joint Rotation Angle Based on ELM Artificial Neural Network and Eddy Current Sensor

Abstract: This paper proposes a measurement method for the rotation angle of the spherical joint based on the extreme learning machine (ELM) artificial neural network and four eddy current sensors. Aiming at the problems of small range and low accuracy in the early three-eddy-current angle measurement prototype, the position matching scheme of four eddy current sensors is researched, a new prototype is developed through simulation analysis, and ELM neural network substitutes the previous generalized regression neural network (GRNN) for building a new measurement model. The modelling training and comparison test are completed in the self-developed high-precision angle calibration device. Experimental results show that the new prototype not only covers a ±20° measurement range but also promotes measurement accuracy, and the standard deviation of the single-axis measurement drops to $3^{\prime }$ within the range of 5°–15°. It provides a relatively high-precision measurement method for real-time, multi-axis active detection of spherical joint space rotation angle error.

Review on rotation angle measurement method for spherical joint

Abstract: Spherical joint is provided with three dimensional rotation motion, which has been widely applied in robots, parallel mechanisms, automobiles and medical devices on account of its smooth motion, compact structure, high load capacity. If the position of the spherical joint can be detected accurately in real time, it will be helpful to improve the accuracy of subsequent mechanical control. How to obtain the rotation angle of the spherical joint in real time has attracted lots of eyes at home and abroad. Currently, there are many kinds of measuring methods and instruments in this field. Focusing on the different measurement principles, as well as the measurement strategies, we systematically summaries the technology involved, which includes the recent work of our team. Their principles, advantage and disadvantages are analyzed, and the trend of development is also discussed in the paper.

Research on Measurement Method of Spherical Joint Rotation Angle Based on ELM Artificial Neural Network and Eddy Current Sensor

Abstract: This paper proposes a measurement method for the rotation angle of the spherical joint based on the extreme learning machine (ELM) artificial neural network and four eddy current sensors. Aiming at the problems of small range and low accuracy in the early three-eddy-current angle measurement prototype, the position matching scheme of four eddy current sensors is researched, a new prototype is developed through simulation analysis, and ELM neural network substitutes the previous generalized regression neural network (GRNN) for building a new measurement model. The modelling training and comparison test are completed in the self-developed high-precision angle calibration device. Experimental results show that the new prototype not only covers a ±20° measurement range but also promotes measurement accuracy, and the standard deviation of the single-axis measurement drops to $3^{\prime }$ within the range of 5°–15°. It provides a relatively high-precision measurement method for real-time, multi-axis active detection of spherical joint space rotation angle error.

Research on Measurement Modeling of Spherical Joint Rotation Angle Based on RBF–ELM Network

Abstract: The acquisition of rotation angle and pose information of precision spherical joint is of great importance for error motion prediction analysis and motion control. In the early stage, an analytical measurement model based on the equivalent magnetic charge method has been purposed to realize the rotation direction identification and rotation angle measurement. However, several shortcomings were observed such as complicated calculations and time-consuming, and the solution accuracy of the theoretical model was decreasing with the expansion of measurement range. To improve this situation, new modeling methods based on artificial neural network have been researched. This paper employs radial basis function (RBF), extreme learning machine (ELM), and RBF–ELM hybrid neural networks to construct measurement models, then analyzes and compares their effectiveness to obtain the optimal algorithm. Analysis results show that the RBF–ELM hybrid neural network has a better calculation accuracy than the others. Finally, the experimental data are used to train and test the network model, and the error between output angle of the model and the actual measured rotation angle is calculated. The comparison results show that the measurement model based on the RBF–ELM hybrid neural network has a higher calculation accuracy and generalization capability. Within the range of ±20°, the maximum error of rotation angle around the X and Y axes are $9'~48''$ and $6'~55''$ , respectively, and the root mean squared error is $1' 59''$ .

Grating Wedge Disk and its Application in the Measurement of Rotating Angle

Abstract: The grating wedge plate is formed by making flare grating on a wedge plate of glass.As a new component,this grating wedge plate is used in the measurement of rotating angle by a dual frequency laser interferometer,which not only simplifies the devices,decreases the size of the interferometer and also makes the assembly of the optical path convenient.The experimental results show that the resolution of the small angle measurement is better than 0.1 ″ and the repeatability of 360° angle measurement is better than 5 %.

Temperature Prediction Model for a Regenerative Aluminum Smelting Furnace by a Just-in-Time Learning-Based Triple-Weighted Regularized Extreme Learning Machine

Abstract: In a regenerative aluminum smelting furnace, real-time liquid aluminum temperature measurements are essential for process control. However, it is often very expensive to achieve accurate temperature measurements. To address this issue, a just-in-time learning-based triple-weighted regularized extreme learning machine (JITL-TWRELM) soft sensor modeling method is proposed for liquid aluminum temperature prediction. In this method, a weighted JITL method (WJITL) is adopted for updating the online local models to deal with the process time-varying problem. Moreover, a regularized extreme learning machine model considering both the sample similarities and the variable correlations was established as the local modeling method. The effectiveness of the proposed method is demonstrated in an industrial aluminum smelting process. The results show that the proposed method can meet the requirements of prediction accuracy of the regenerative aluminum smelting furnace.