This paper presents a new data-driven model of length-pressure hysteresis of pneumatic artificial muscles(PAMs) based on Gaussian mixture models(GMMs). By ignoring the high-order dynamics, the hysteresis of PAMs is mo...This paper presents a new data-driven model of length-pressure hysteresis of pneumatic artificial muscles(PAMs) based on Gaussian mixture models(GMMs). By ignoring the high-order dynamics, the hysteresis of PAMs is modeled as a first-order nonlinear dynamical system based on GMMs, and inversion of the model is subsequently derived. Several verification experiments are conducted. Firstly, parameters of the model are identified under low-frequency triangle-wave pressure excitations.Then, pressure signals with different amplitudes, shapes and frequencies are applied to the PAM to test the prediction performance of the model. The proposed model shows advantages in identification efficiency and prediction precision compared with a generalized Prandtl–Ishlinskii(GPI) model and a modified generalized Prandtl–Ishlinskii(MGPI) model. Finally, the effectiveness of the inverse model is demonstrated by implementing the feedforward hysteresis compensation in trajectory tracking experiments.展开更多
This paper introduces techniques in Gaussian process regression model for spatiotemporal data collected from complex systems.This study focuses on extracting local structures and then constructing surrogate models bas...This paper introduces techniques in Gaussian process regression model for spatiotemporal data collected from complex systems.This study focuses on extracting local structures and then constructing surrogate models based on Gaussian process assumptions.The proposed Dynamic Gaussian Process Regression(DGPR)consists of a sequence of local surrogate models related to each other.In DGPR,the time-based spatial clustering is carried out to divide the systems into sub-spatio-temporal parts whose interior has similar variation patterns,where the temporal information is used as the prior information for training the spatial-surrogate model.The DGPR is robust and especially suitable for the loosely coupled model structure,also allowing for parallel computation.The numerical results of the test function show the effectiveness of DGPR.Furthermore,the shock tube problem is successfully approximated under different phenomenon complexity.展开更多
城市抗震防灾系统是一个复杂开放巨系统,系统中由于灾情的动态演化导致的建筑物震陷量形成机理也日趋复杂。根据高斯过程理论和贝叶斯规则,对训练样本进行的“归纳推理学习”,即综合先验信息,调整各随机变量的后验分布,进而提出基于高...城市抗震防灾系统是一个复杂开放巨系统,系统中由于灾情的动态演化导致的建筑物震陷量形成机理也日趋复杂。根据高斯过程理论和贝叶斯规则,对训练样本进行的“归纳推理学习”,即综合先验信息,调整各随机变量的后验分布,进而提出基于高斯回归过程的建筑物震陷量非线性预测模型。采用EP(expectation propagation)算法获得预测样本潜在函数的近似后验高斯分布,并对其超参数和协方差函数的选择进行了探讨,利用LSSVM(least square support vector machine)模型、PLS(partial least squares)模型和MLR(multiple linear regression)模型等统计模型对建筑物实测震陷样本进行预测训练,通过模型的交叉验证分析及建模参数详细对比分析,验证了预测模型的科学性和可靠性,可为城市抗震防灾决策提供借鉴。展开更多
Predicting the mechanical properties of additively manufactured parts is often a tedious process,requiring the integration of multiple stand-alone and expensive simulations.Furthermore,as properties are highly locatio...Predicting the mechanical properties of additively manufactured parts is often a tedious process,requiring the integration of multiple stand-alone and expensive simulations.Furthermore,as properties are highly location-dependent due to repeated heating and cooling cycles,the properties prediction models must be run for multiple locations before the part-level performance can be analyzed for certification,compounding the computational expense.This work has proposed a rapid prediction framework that replaces the physics-based mechanistic models with Gaussian process metamodels,a type of machine learning model for statistical inference with limited data.The metamodels can predict the varying properties within an entire part in a fraction of the time while providing uncertainty quantification.The framework was demonstrated with the prediction of the tensile yield strength of Ferrium?PH48S maraging stainless steel fabricated by additive manufacturing.Impressive agreement was found between the metamodels and the mechanistic models,and the computation was dramatically decreased from hours of physics-based simulations to less than a second with metamodels.This method can be extended to predict various materials properties in different alloy systems whose processstructure-property-performance interrelationships are linked by mechanistic models.It is powerful for rapidly identifying the spatial properties of a part with compositional and processing parameter variations,and can support part certification by providing a fast interface between materials models and part-level thermal and performance simulations.展开更多
基金supported by the National Natural Science Foundation of China (Grant No. 91648104)Shanghai Rising-Star Program (Grant No. 17QA1401900)
文摘This paper presents a new data-driven model of length-pressure hysteresis of pneumatic artificial muscles(PAMs) based on Gaussian mixture models(GMMs). By ignoring the high-order dynamics, the hysteresis of PAMs is modeled as a first-order nonlinear dynamical system based on GMMs, and inversion of the model is subsequently derived. Several verification experiments are conducted. Firstly, parameters of the model are identified under low-frequency triangle-wave pressure excitations.Then, pressure signals with different amplitudes, shapes and frequencies are applied to the PAM to test the prediction performance of the model. The proposed model shows advantages in identification efficiency and prediction precision compared with a generalized Prandtl–Ishlinskii(GPI) model and a modified generalized Prandtl–Ishlinskii(MGPI) model. Finally, the effectiveness of the inverse model is demonstrated by implementing the feedforward hysteresis compensation in trajectory tracking experiments.
基金co-supported by the National Natural Science Foundation of China(No.12101608)the NSAF(No.U2230208)the Hunan Provincial Innovation Foundation for Postgraduate,China(No.CX20220034).
文摘This paper introduces techniques in Gaussian process regression model for spatiotemporal data collected from complex systems.This study focuses on extracting local structures and then constructing surrogate models based on Gaussian process assumptions.The proposed Dynamic Gaussian Process Regression(DGPR)consists of a sequence of local surrogate models related to each other.In DGPR,the time-based spatial clustering is carried out to divide the systems into sub-spatio-temporal parts whose interior has similar variation patterns,where the temporal information is used as the prior information for training the spatial-surrogate model.The DGPR is robust and especially suitable for the loosely coupled model structure,also allowing for parallel computation.The numerical results of the test function show the effectiveness of DGPR.Furthermore,the shock tube problem is successfully approximated under different phenomenon complexity.
文摘城市抗震防灾系统是一个复杂开放巨系统,系统中由于灾情的动态演化导致的建筑物震陷量形成机理也日趋复杂。根据高斯过程理论和贝叶斯规则,对训练样本进行的“归纳推理学习”,即综合先验信息,调整各随机变量的后验分布,进而提出基于高斯回归过程的建筑物震陷量非线性预测模型。采用EP(expectation propagation)算法获得预测样本潜在函数的近似后验高斯分布,并对其超参数和协方差函数的选择进行了探讨,利用LSSVM(least square support vector machine)模型、PLS(partial least squares)模型和MLR(multiple linear regression)模型等统计模型对建筑物实测震陷样本进行预测训练,通过模型的交叉验证分析及建模参数详细对比分析,验证了预测模型的科学性和可靠性,可为城市抗震防灾决策提供借鉴。
基金This work was supported by the Digital Manufacturing and Design Innovation Institute(DMDII)through award number 15-07-07.This material is also based upon the work of Ms.Yu-Chin Chan supported by the National Science Foundation Graduate Research Fellowship Program under Grant No.DGE-1842165.Any opinions,findings,and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
文摘Predicting the mechanical properties of additively manufactured parts is often a tedious process,requiring the integration of multiple stand-alone and expensive simulations.Furthermore,as properties are highly location-dependent due to repeated heating and cooling cycles,the properties prediction models must be run for multiple locations before the part-level performance can be analyzed for certification,compounding the computational expense.This work has proposed a rapid prediction framework that replaces the physics-based mechanistic models with Gaussian process metamodels,a type of machine learning model for statistical inference with limited data.The metamodels can predict the varying properties within an entire part in a fraction of the time while providing uncertainty quantification.The framework was demonstrated with the prediction of the tensile yield strength of Ferrium?PH48S maraging stainless steel fabricated by additive manufacturing.Impressive agreement was found between the metamodels and the mechanistic models,and the computation was dramatically decreased from hours of physics-based simulations to less than a second with metamodels.This method can be extended to predict various materials properties in different alloy systems whose processstructure-property-performance interrelationships are linked by mechanistic models.It is powerful for rapidly identifying the spatial properties of a part with compositional and processing parameter variations,and can support part certification by providing a fast interface between materials models and part-level thermal and performance simulations.
