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Robust state estimation for uncertain linear systems with deterministic input signals

Control Theory and Technology 2014年第4期12卷 383-392页

作者： Huabo LIU Tong ZHOU Department of Automation Tsinghua University College of Automation Engineering Qingdao University Tsinghua National Laboratory for Information Science and Technology (TNList) Tsinghua University

In this paper, we investigate state estimations of a dynamical system in which not only process and measurement noise, but also parameter uncertainties and deterministic input signals are involved. The sensitivity penalization based robust state estimation is extended to uncertain linear systems with deterministic input signals and parametric uncertainties which may nonlinearly affect a state-space plant model. The form of the derived robust estimator is similar to that of the well-known Kalman filter with a comparable computational complexity. Under a few weak assumptions, it is proved that though the derived state estimator is biased, the bound of estimation errors is finite and the covariance matrix of estimation errors is bounded. Numerical simulations show that the obtained robust filter has relatively nice estimation performances.

关键词： Robust estimation Deterministic input regularized least-squares

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Alternating minimization for data-driven computational elasticity from experimental data: kernel method for learning constitutive manifold

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Theoretical & Applied Mechanics Letters 2021年第5期11卷 260-265页

作者： Yoshihiro Kanno Mathematics and Informatics Center The University of TokyoHongo 7-3-1Tokyo 113-8656Japan

Data-driven computing in elasticity attempts to directly use experimental data on material,without constructing an empirical model of the constitutive relation,to predict an equilibrium state of a structure subjected to a specified external *** that a data set comprising stress-strain pairs of material is available,a data-driven method using the kernel method and the regularized least-squares was developed to extract a manifold on which the points in the data set approximately lie(Kanno 2021,***.).From the perspective of physical experiments,stress field cannot be directly measured,while displacement and force fields are *** this study,we extend the previous kernel method to the situation that pairs of displacement and force,instead of pairs of stress and strain,are available as an input data set.A new regularized least-squares problem is formulated in this problem setting,and an alternating minimization algorithm is proposed to solve the problem.

关键词： Alternating minimization regularized least-squares Kernel method Manifold learning Data-driven computing

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