Internal model control （IMC） yields very good performance for set point tracking, but gives sluggish response for disturbance rejection problem. A two-degree-of-freedom IMC （2DOF-IMC） has been developed to overcom...Internal model control （IMC） yields very good performance for set point tracking, but gives sluggish response for disturbance rejection problem. A two-degree-of-freedom IMC （2DOF-IMC） has been developed to overcome the weakness. However, the setting of parameter becomes a complicated matter if there is an uncertainty model. The present study proposes a new tuning method for the controller. The proposed tuning method consists of three steps. Firstly, the worst case of the model uncertainty is determined. Secondly, the parameter of set point con- troller using maximum peak （Mp） criteria is specified, and finally, the parameter of the disturbance rejection con- troller using gain margin （GM） criteria is obtained. The proposed method is denoted as Mp-GM tuning method. The effectiveness of Mp-GM tuning method has evaluated and compared with IMC-controller tuning program （IMCTUNE） as bench mark. The evaluation and comparison have been done through the simulation on a number of first order plus dead time （FOPDT） and higher order processes. The FOPDT process tested includes processes with controllability ratio in the range 0.7 to 2.5. The higher processes include second order with underdarnped and third order with nonminimum phase processes. Although the two of higher order processes are considered as difficult processes, the proposed Mp-GM tuning method are able to obtain the good controller parameter even under process uncertainties.展开更多
A data-driven model reduction strategy is presented for the representation of random polycrystal microstructures.Given a set of microstructure snapshots that satisfy certain statistical constraints such as given low-o...A data-driven model reduction strategy is presented for the representation of random polycrystal microstructures.Given a set of microstructure snapshots that satisfy certain statistical constraints such as given low-order moments of the grain size distribution,using a non-linear manifold learning approach,we identify the intrinsic low-dimensionality of the microstructure manifold.In addition to grain size,a linear dimensionality reduction technique(Karhunun-Lo´eve Expansion)is used to reduce the texture representation.The space of viable microstructures is mapped to a low-dimensional region thus facilitating the analysis and design of polycrystal microstructures.This methodology allows us to sample microstructure features in the reduced-order space thus making it a highly efficient,low-dimensional surrogate for representing microstructures(grain size and texture).We demonstrate the model reduction approach by computing the variability of homogenized thermal properties using sparse grid collocation in the reduced-order space that describes the grain size and orientation variability.展开更多
We develop an efficient,adaptive locally weighted projection regression(ALWPR)framework for uncertainty quantification(UQ)of systems governed by ordinary and partial differential equations.The algorithm adaptively sel...We develop an efficient,adaptive locally weighted projection regression(ALWPR)framework for uncertainty quantification(UQ)of systems governed by ordinary and partial differential equations.The algorithm adaptively selects the new input points with the largest predictive variance and decides when and where to add new localmodels.It effectively learns the local features and accurately quantifies the uncertainty in the prediction of the statistics.The developed methodology provides predictions and confidence intervals at any query input and can dealwithmulti-output cases.Numerical examples are presented to show the accuracy and efficiency of the ALWPR framework including problems with non-smooth local features such as discontinuities in the stochastic space.展开更多
基金Supported by Postgraduate Fellowship of UMP,Fundamental Research Grant Scheme of Malaysia(GRS070120)Joint Research Grant between Universiti Malaysia Pahang (UMP) and Institut Teknologi Sepuluh Nopember (ITS) Surabaya
文摘Internal model control （IMC） yields very good performance for set point tracking, but gives sluggish response for disturbance rejection problem. A two-degree-of-freedom IMC （2DOF-IMC） has been developed to overcome the weakness. However, the setting of parameter becomes a complicated matter if there is an uncertainty model. The present study proposes a new tuning method for the controller. The proposed tuning method consists of three steps. Firstly, the worst case of the model uncertainty is determined. Secondly, the parameter of set point con- troller using maximum peak （Mp） criteria is specified, and finally, the parameter of the disturbance rejection con- troller using gain margin （GM） criteria is obtained. The proposed method is denoted as Mp-GM tuning method. The effectiveness of Mp-GM tuning method has evaluated and compared with IMC-controller tuning program （IMCTUNE） as bench mark. The evaluation and comparison have been done through the simulation on a number of first order plus dead time （FOPDT） and higher order processes. The FOPDT process tested includes processes with controllability ratio in the range 0.7 to 2.5. The higher processes include second order with underdarnped and third order with nonminimum phase processes. Although the two of higher order processes are considered as difficult processes, the proposed Mp-GM tuning method are able to obtain the good controller parameter even under process uncertainties.
基金support from the Computational Mathematics program of AFOSR(grant F49620-00-1-0373),the DOE Office of Science ASCR(award DE-SC0004910)the Materials Design and Surface Engineering program of the NSF(award CMMI-0757824)+1 种基金the Mechanical Behavior of Materials program Army Research Office(proposal to Cornell University No.W911NF0710519)an OSD/AFOSR MURI09 award to Cornell University on uncertainty quantification.
文摘A data-driven model reduction strategy is presented for the representation of random polycrystal microstructures.Given a set of microstructure snapshots that satisfy certain statistical constraints such as given low-order moments of the grain size distribution,using a non-linear manifold learning approach,we identify the intrinsic low-dimensionality of the microstructure manifold.In addition to grain size,a linear dimensionality reduction technique(Karhunun-Lo´eve Expansion)is used to reduce the texture representation.The space of viable microstructures is mapped to a low-dimensional region thus facilitating the analysis and design of polycrystal microstructures.This methodology allows us to sample microstructure features in the reduced-order space thus making it a highly efficient,low-dimensional surrogate for representing microstructures(grain size and texture).We demonstrate the model reduction approach by computing the variability of homogenized thermal properties using sparse grid collocation in the reduced-order space that describes the grain size and orientation variability.
文摘We develop an efficient,adaptive locally weighted projection regression(ALWPR)framework for uncertainty quantification(UQ)of systems governed by ordinary and partial differential equations.The algorithm adaptively selects the new input points with the largest predictive variance and decides when and where to add new localmodels.It effectively learns the local features and accurately quantifies the uncertainty in the prediction of the statistics.The developed methodology provides predictions and confidence intervals at any query input and can dealwithmulti-output cases.Numerical examples are presented to show the accuracy and efficiency of the ALWPR framework including problems with non-smooth local features such as discontinuities in the stochastic space.
