Topic
System identification
About: System identification is a research topic. Over the lifetime, 21291 publications have been published within this topic receiving 439142 citations.
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TL;DR: In this paper, the authors highlight the need for systematic model-based design and analysis in pharmaceutical product-process development and highlight the role, development and use of models of various types and the structure of the models for the product and the process.
103 citations
TL;DR: The model selection proved that hemodynamic models better explain the BOLD response than linear convolution, in particular because they are able to capture some features like poststimulus undershoot or nonlinear effects.
103 citations
TL;DR: This paper is to replace the unmeasurable variables in the information vector/matrix with the estimated residuals and the outputs of the auxiliary model.
Abstract: According to the hierarchical identification principle, a hierarchical gradient based iterative estimation algorithm is derived for multivariable output error moving average systems (i.e., multivariable OEMA-like models) which is different from multivariable CARMA-like models. As there exist unmeasurable noise-free outputs and unknown noise terms in the information vector/matrix of the corresponding identification model, this paper is, by means of the auxiliary model identification idea, to replace the unmeasurable variables in the information vector/matrix with the estimated residuals and the outputs of the auxiliary model. A numerical example is provided.
103 citations
01 Sep 2001
TL;DR: This article deals with modeling and identification of fractional systems in the time domain, and a new identification method is proposed, based on the generalization to fractional orders of classical methods based on State Variable Filters.
Abstract: This article deals with modeling and identification of fractional systems in the time domain. Fractional state-space representation is defined, and a stability condition for fractional systems given. A new identification method for fractional systems is then proposed. The method is based on the generalization to fractional orders of classical methods based on State Variable Filters (SVF). A particular case of fractional SVF, fractional Poisson filters, is studied. Parameter estimation is then performed, through the conventional least squares method, and then through the instrumental variable method which permits unbiased parameter estimation. Monte Carlo simulations are then performed, using various noise levels, to compare the identification performance of these two methods, and of a prediction error method based on a fractional ARX model.
103 citations
TL;DR: The adaptive control for discrete time linear dynamical systems preceded with hysteresis described by the Prandtl-Ishlinskii model is discussed, which ensures the global stability of the closed-loop system, and the output tracking error can be controlled to be as small as required by choosing the design parameters.
Abstract: Hysteresis hinders the effectiveness of smart materials in sensors and actuators. It is a challenging task to control the systems with hysteresis. This note discusses the adaptive control for discrete time linear dynamical systems preceded with hysteresis described by the Prandtl-Ishlinskii model. The time delay and the order of the linear dynamical system are assumed to be known. The contribution of the note is the fusion of the hysteresis model with adaptive control techniques without constructing the inverse hysteresis nonlinearity. Only the parameters (which are generated from the parameters of the linear system and the density function of the hysteresis) directly needed in the formulation of the controller are adaptively estimated online. The proposed control law ensures the global stability of the closed-loop system, and the output tracking error can be controlled to be as small as required by choosing the design parameters. Simulation results show the effectiveness of the proposed algorithm.
103 citations