Temperature Compensation for a Six-Axis Force/Torque Sensor Based on the Particle Swarm Optimization Least Square Support Vector Machine for Space Manipulator

Sun, Yongjun; Liu, Yiwei; Liu, Hong

doi:10.1109/jsen.2015.2485258

Cited by 35 publications

(17 citation statements)

References 21 publications

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“…In order to validate the effectiveness of the proposed algorithm, several methods include SVM with RBF kernel [28], particle swarm optimization optimized RBF kernel SVM (PSO-RBF-SVM) [29], particle swarm optimization optimized RBF kernel LSSVM (PSO-RBF-LSSVM) [30], particle swarm optimization optimized hybrid kernel LSSVM (PSO-Hybrid-LSSVM), and ions motion algorithm optimized hybrid kernel LSSVM (IMA-Hybrid-LSSVM) are investigated. Because optimization techniques discussed in this article are all population-based algorithms, the hyper-parameters set (

C, t, p, σ, λ

) is defined as an individual in any population.…”

Section: Data Calibration Experiments and Results Analysismentioning

confidence: 99%

A Temperature Compensation Method for Piezo-Resistive Pressure Sensor Utilizing Chaotic Ions Motion Algorithm Optimized Hybrid Kernel LSSVM

Zhou³

et al. 2016

Sensors

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A piezo-resistive pressure sensor is made of silicon, the nature of which is considerably influenced by ambient temperature. The effect of temperature should be eliminated during the working period in expectation of linear output. To deal with this issue, an approach consists of a hybrid kernel Least Squares Support Vector Machine (LSSVM) optimized by a chaotic ions motion algorithm presented. To achieve the learning and generalization for excellent performance, a hybrid kernel function, constructed by a local kernel as Radial Basis Function (RBF) kernel, and a global kernel as polynomial kernel is incorporated into the Least Squares Support Vector Machine. The chaotic ions motion algorithm is introduced to find the best hyper-parameters of the Least Squares Support Vector Machine. The temperature data from a calibration experiment is conducted to validate the proposed method. With attention on algorithm robustness and engineering applications, the compensation result shows the proposed scheme outperforms other compared methods on several performance measures as maximum absolute relative error, minimum absolute relative error mean and variance of the averaged value on fifty runs. Furthermore, the proposed temperature compensation approach lays a foundation for more extensive research.

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C, t, p, σ, λ

) is defined as an individual in any population.…”

Section: Data Calibration Experiments and Results Analysismentioning

confidence: 99%

A Temperature Compensation Method for Piezo-Resistive Pressure Sensor Utilizing Chaotic Ions Motion Algorithm Optimized Hybrid Kernel LSSVM

Zhou³

et al. 2016

Sensors

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“…The parameter selection of LSSVM will determine its learning performance and generalization ability. At present, the most commonly used parameter selection algorithms are GA and PSO [17,18], but the selection, crossover and mutation of the former are too complex, and the latter is prone to be premature, and the convergence speed of two algorithms is still not ideal.…”

Section: The Lssvm Based On the Cedamentioning

confidence: 99%

SAW Torque Sensor Gyroscopic Effect Compensation by Least Squares Support Vector Machine Algorithm Based on Chaos Estimation of Distributed Algorithm

Han

Cao

et al. 2019

Sensors

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As this study examined the issue of surface acoustic wave (SAW) torque sensor which interfered in high rotational speed, the gyroscopic effect generated by rotation was analyzed. Firstly, the SAW coupled equations which contained torque and rotation loads were deduced, and the torque calculation error caused by rotation was solved. Following this, the hardware of the SAW gyroscopic effect testing platform and the turntable experiment were designed to verify the correctness of the theoretical calculation. Finally, according to the experimental data, the gyroscopic effect was compensated by multivariate polynomial fitting (MPF), Gaussian processes regression (GPR), and least squares support vector machine algorithms (LSSVM). The comparison results showed that the LSSVM has the obvious advantage. For improving the function of LSSVM model, chaos estimation of distributed algorithm (CEDA) was proposed to optimize the super parameters of the LSSVM, and numerical simulation results showed that: (1) CEDA is superior to traditional estimation of distributed algorithms in convergence speed and anti-premature ability; (2) the performance of CEDA-LSSVM is better than genetic algorithms (GA)-LSSVM and particle swarm optimization (PSO)-LSSVM. After compensating by CEDA-LSSVM, the magnitude of the torque calculation relative error was 10−4 in any direction. This method has a significant effect on reducing gyroscopic interference, and it lays a foundation for the engineering application of SAW torque sensor.

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“…Traditional software compensation approaches include the least squares method (LSM) and artificial neural network (ANN) [12]- [18]. While the LSM is commonly applied to error compensation, its accuracy is limited as it often identifies a local optimum rather than the global optimum when fitting periodic errors [16], [19]. With regard to ANN, studies have found that an ANN can overcome periodic nonlinear shortages and achieve a good fit [13], however, their disadvantages are a slow convergence rate, a tendency to over-fit, and a susceptibility to falling into a local extremum.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Error Compensation of Capacitive Angular Encoders Based on Improved Particle Swarm Optimization Support Vector Machines

Hou

Zhou

et al. 2020

IEEE Access

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Rotary encoders are widely applied in a variety of industrial fields. However, as the exist of the installation, processing and demodulation circuits errors, the test result of the encoder is superimposed with periodic nonlinear errors and the encoder needs compensation to achieve high measurement accuracy. Traditional methods including the least square method (LSM) and back propagation artificial neural network (BP-ANN), are not capable of addressing nonlinear errors. Thus, a novel method based on improved particle swarm optimization (IPSO) and support vector machines (SVM) is proposed to provide better compensation. The proposed method incorporates the SVM method into the design of the compensation model, and the IPSO algorithm is applied to tune the SVM parameters. To validate the algorithm, four sets of data were obtained from encoders with different numbers of segments. The experimental results show that the IPSO-SVM algorithm has a better prediction precision and the nonlinear standard deviation of 180 petal-shaped numbers has dropped from 0.08 • to 0.0005 • after compensation over 0 • to 360 • measurement range. Based on the results, the proposed IPSO-SVM model provided more accurate compensation on the nonlinear errors to the capacitive angular encoders than other method.

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Temperature Compensation for a Six-Axis Force/Torque Sensor Based on the Particle Swarm Optimization Least Square Support Vector Machine for Space Manipulator

Cited by 35 publications

References 21 publications

A Temperature Compensation Method for Piezo-Resistive Pressure Sensor Utilizing Chaotic Ions Motion Algorithm Optimized Hybrid Kernel LSSVM

A Temperature Compensation Method for Piezo-Resistive Pressure Sensor Utilizing Chaotic Ions Motion Algorithm Optimized Hybrid Kernel LSSVM

SAW Torque Sensor Gyroscopic Effect Compensation by Least Squares Support Vector Machine Algorithm Based on Chaos Estimation of Distributed Algorithm

Nonlinear Error Compensation of Capacitive Angular Encoders Based on Improved Particle Swarm Optimization Support Vector Machines

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