A Temperature Compensation Method for aSix-Axis Force/Torque Sensor Utilizing Ensemble hWOA-LSSVM Based on Improved Trimmed Bagging

The performance of a six-axis force/torque sensor (F/T sensor) severely decreased when working in an extreme environment due to its sensitivity to ambient temperature. This paper puts forward an ensemble temperature compensation method based on the whale optimization algorithm (WOA) tuning the least-square support vector machine (LSSVM) and trimmed bagging. To be specific, the stimulated annealing algorithm (SA) was hybridized to the WOA to solve the local entrapment problem, and an adaptive trimming strategy is proposed to obtain the optimal trim portion for the trimmed bagging. In addition, inverse quote error (invQE) and cross-validation are employed to estimate the fitness better in training process. The maximum absolute measurement error caused by temperature decreased from 3.34% to 3.9×10−3% of full scale after being compensated by the proposed method. The analyses of experiments illustrate the ensemble hWOA-LSSVM based on improved trimmed bagging improves the precision and stability of F/T sensors and possesses the strengths of local search ability and better adaptability.

[1]  Yi-Kuen Lee,et al.  An Integrated Temperature Compensation Method for Thermal Expansion-based Angular Motion Sensors , 2021, 2021 IEEE Sensors.

[2]  Muhammad Shafie Abd Latiff,et al.  A hybrid whale optimization algorithm with differential evolution optimization for multi-objective virtual machine scheduling in cloud computing , 2021, Engineering Optimization.

[3]  Ping Wang,et al.  SOH estimation of lithium-ion batteries based on least squares support vector machine error compensation model , 2021, Journal of Power Electronics.

[4]  Junwu Wang,et al.  A Method for Prediction of Waterlogging Economic Losses in a Subway Station Project , 2021, Mathematics.

[5]  Yan Wang,et al.  Temperature Compensation for MEMS Mass Flow Sensors Based on Back Propagation Neural Network , 2021, 2021 IEEE 16th International Conference on Nano/Micro Engineered and Molecular Systems (NEMS).

[6]  Xin Wang,et al.  Thermal error prediction of machine tool spindle using segment fusion LSSVM , 2021, The International Journal of Advanced Manufacturing Technology.

[7]  Jun Deng,et al.  Correction model for CO detection in the coal combustion loss process in mines based on GWO-SVM , 2021, Journal of Loss Prevention in the Process Industries.

[8]  G. Fedder,et al.  Stress-and-Temperature-Induced Drift Compensation on a High Dynamic Range Accelerometer Array Using Deep Neural Networks , 2021, 2021 IEEE 34th International Conference on Micro Electro Mechanical Systems (MEMS).

[9]  Xisheng Feng,et al.  Fuzzy Adaptive Impedance Control for Deep-Sea Hydraulic Manipulator Grasping Under Uncertainties , 2020, Global Oceans 2020: Singapore – U.S. Gulf Coast.

[10]  W. Niu,et al.  A Novel Method for Energy Consumption Prediction of Underwater Gliders Using Optimal LSSVM with PSO Algorithm , 2020, Global Oceans 2020: Singapore – U.S. Gulf Coast.

[11]  Georgios N. Kouziokas.  SVM kernel based on particle swarm optimized vector and Bayesian optimized SVM in atmospheric particulate matter forecasting , 2020, Appl. Soft Comput..

[12]  Yuhanis Yusof,et al.  A HYBRID LEAST SQUARES SUPPORT VECTOR MACHINE WITH BAT AND CUCKOO SEARCH ALGORITHMS FOR TIME SERIES FORECASTING , 2020, Journal of Information and Communication Technology.

[13]  Shaoguang Shi,et al.  Temperature compensation technology of speckle structured light based on BP neural network , 2020 .

[14]  Wangyu Tong,et al.  A Hybrid Algorithm Framework with Learning and Complementary Fusion Features for Whale Optimization Algorithm , 2020, Sci. Program..

[15]  B. R. Baliga,et al.  Drift compensation in thermal anemometry , 2020, Measurement Science and Technology.

[16]  R. Zhu,et al.  Temperature and Strain Compensation for Flexible Sensors based on Thermosensation. , 2019, ACS applied materials & interfaces.

[17]  Ying Liu,et al.  Temperature Compensation of MEMS Gyroscope Based on Support Vector Machine Optimized by GA , 2019, 2019 IEEE Symposium Series on Computational Intelligence (SSCI).

[18]  Aiqun Zhang,et al.  Performance and experiment of deep-sea master-slave servo electric manipulator , 2019, OCEANS 2019 MTS/IEEE SEATTLE.

[19]  Seyed Mohammad Mirjalili,et al.  A hyper-heuristic for improving the initial population of whale optimization algorithm , 2019, Knowl. Based Syst..

[20]  Myeongjong Yu,et al.  Compensation Method of Gyroscope Bias Hysteresis Error with Temperature and Rate of Temperature using Deep Neural Networks , 2018, 2018 18th International Conference on Control, Automation and Systems (ICCAS).

[21]  Andrew Lewis,et al.  The Whale Optimization Algorithm , 2016, Adv. Eng. Softw..

[22]  L. Breiman.  Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.

[23]  Thomas G. Dietterich.  An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization , 2000, Machine Learning.

[24]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[25]  Regina Elsebach.  Evaluation of forecasts in AR models with outliers , 1994 .

[26]  Ricardo dos Santos Pereira,et al.  Thermal Compensation Method for Piezoresistive Pressure Transducer , 2021, IEEE Transactions on Instrumentation and Measurement.

[27]  Wentao Feng,et al.  Convergence Analysis of Whale Optimization Algorithm , 2021 .

[28]  Tao Bairui,et al.  Temperature Compensation Model Based on the Wavelet Neural Network with Genetic Algorithm , 2012 .

[29]  Jasbir S. Arora,et al.  5 – More on Optimum Design Concepts , 2004 .

[30]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[31]  L. Breiman.  Arcing Classifiers , 1998 .

[32]  Dirk Van,et al.  Ensemble Methods: Foundations and Algorithms , 2012 .