1,294 research outputs found
Adaptive Input Reconstruction with Application to Model Refinement, State Estimation, and Adaptive Control.
Input reconstruction is the process of using the output of a system to estimate its input. In some cases, input reconstruction can be accomplished by determining the output of the inverse of a model of the system whose input is the output of the original system. Inversion, however, requires an exact and fully known analytical model, and is limited by instabilities arising from nonminimum-phase zeros.
The main contribution of this work is a novel technique for input reconstruction that does not require model inversion. This technique is based on a retrospective cost, which requires a limited number of Markov parameters. Retrospective cost input reconstruction (RCIR) does not require knowledge of nonminimum-phase zero locations or an analytical model of the system.
RCIR provides a technique that can be used for model refinement, state estimation, and adaptive control. In the model refinement application, data are used to refine or improve a model of a system. It is assumed that the difference between the model output and the data is due to an unmodeled subsystem whose interconnection with the modeled system is inaccessible, that is, the interconnection signals cannot be measured and thus standard system identification techniques cannot be used. Using input reconstruction, these inaccessible signals can be estimated, and the inaccessible subsystem can be fitted. We demonstrate input reconstruction in a model refinement framework by identifying unknown physics in a space weather model and by estimating an unknown film growth in a lithium ion battery. The same technique can be used to obtain estimates of states that cannot be directly measured.
Adaptive control can be formulated as a model-refinement problem, where the unknown subsystem is the idealized controller that minimizes a measured performance variable. Minimal modeling input reconstruction for adaptive control is useful for applications where modeling information may be difficult to obtain. We demonstrate adaptive control of a seeker-guided missile with unknown aerodynamics.Ph.D.Aerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91520/1/amdamato_1.pd
Retrieving highly structured models starting from black-box nonlinear state-space models using polynomial decoupling
Nonlinear state-space modelling is a very powerful black-box modelling
approach. However powerful, the resulting models tend to be complex, described
by a large number of parameters. In many cases interpretability is preferred
over complexity, making too complex models unfit or undesired. In this work,
the complexity of such models is reduced by retrieving a more structured,
parsimonious model from the data, without exploiting physical knowledge.
Essential to the method is a translation of all multivariate nonlinear
functions, typically found in nonlinear state-space models, into sets of
univariate nonlinear functions. The latter is computed from a tensor
decomposition. It is shown that typically an excess of degrees of freedom are
used in the description of the nonlinear system whereas reduced representations
can be found. The method yields highly structured state-space models where the
nonlinearity is contained in as little as a single univariate function, with
limited loss of performance. Results are illustrated on simulations and
experiments for: the forced Duffing oscillator, the forced Van der Pol
oscillator, a Bouc-Wen hysteretic system, and a Li-Ion battery model.Comment: submitted to Mechanical Systems and Signal Processin
State-of-Health Identification of Lithium-Ion Batteries Based on Nonlinear Frequency Response Analysis: First Steps with Machine Learning
In this study, we show an effective data-driven identification of the State-of-Health of
Lithium-ion batteries by Nonlinear Frequency Response Analysis. A degradation model based on
support vector regression is derived from highly informative Nonlinear Frequency Response Analysis
data sets. First, an ageing test of a Lithium-ion battery at 25 C is presented and the impact of relevant
ageing mechanisms on the nonlinear dynamics of the cells is analysed. A correlation measure is used
to identify the most sensitive frequency range for ageing tests. Here, the mid-frequency range from
1 Hz to 100 Hz shows the strongest correlation to Lithium-ion battery degradation. The focus on the
mid-frequency range leads to a dramatic reduction in measurement time of up to 92% compared to
standard measurement protocols. Next, informative features are extracted and used to parametrise
the support vector regression model for the State of Health degradation. The performance of the
degradation model is validated with additional cells and validation data sets, respectively. We show
that the degradation model accurately predicts the State of Health values. Validation data demonstrate
the usefulness of the Nonlinear Frequency Response Analysis as an effective and fast State of Health
identification method and as a versatile tool in the diagnosis of ageing of Lithium-ion batteries
in general
State-of-Health Identification of Lithium-Ion Batteries Based on Nonlinear Frequency Response Analysis: First Steps with Machine Learning
In this study, we show an effective data-driven identification of the State-of-Health of
Lithium-ion batteries by Nonlinear Frequency Response Analysis. A degradation model based on
support vector regression is derived from highly informative Nonlinear Frequency Response Analysis
data sets. First, an ageing test of a Lithium-ion battery at 25 C is presented and the impact of relevant
ageing mechanisms on the nonlinear dynamics of the cells is analysed. A correlation measure is used
to identify the most sensitive frequency range for ageing tests. Here, the mid-frequency range from
1 Hz to 100 Hz shows the strongest correlation to Lithium-ion battery degradation. The focus on the
mid-frequency range leads to a dramatic reduction in measurement time of up to 92% compared to
standard measurement protocols. Next, informative features are extracted and used to parametrise
the support vector regression model for the State of Health degradation. The performance of the
degradation model is validated with additional cells and validation data sets, respectively. We show
that the degradation model accurately predicts the State of Health values. Validation data demonstrate
the usefulness of the Nonlinear Frequency Response Analysis as an effective and fast State of Health
identification method and as a versatile tool in the diagnosis of ageing of Lithium-ion batteries
in general
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