1,408 research outputs found
Switching Control for Parameter Identifiability of Uncertain Systems
This paper considers the problem of identifying the parameters of an
uncertain linear system by means of feedback control. The problem is approached
by considering time-varying controllers. It is shown that even when the
uncertainty set is not finite, parameter identifiability can be generically
ensured by switching among a finite number of linear time-invariant
controllers. The results are shown to have several implications, ranging from
fault detection and isolation to adaptive and supervisory control. Practical
aspects of the problem are also discussed in details
Global Sensitivity Methods for Design of Experiments in Lithium-ion Battery Context
Battery management systems may rely on mathematical models to provide higher
performance than standard charging protocols. Electrochemical models allow us
to capture the phenomena occurring inside a lithium-ion cell and therefore,
could be the best model choice. However, to be of practical value, they require
reliable model parameters. Uncertainty quantification and optimal experimental
design concepts are essential tools for identifying systems and estimating
parameters precisely. Approximation errors in uncertainty quantification result
in sub-optimal experimental designs and consequently, less-informative data,
and higher parameter unreliability. In this work, we propose a highly efficient
design of experiment method based on global parameter sensitivities. This novel
concept is applied to the single-particle model with electrolyte and thermal
dynamics (SPMeT), a well-known electrochemical model for lithium-ion cells. The
proposed method avoids the simplifying assumption of output-parameter
linearization (i.e., local parameter sensitivities) used in conventional Fisher
information matrix-based experimental design strategies. Thus, the optimized
current input profile results in experimental data of higher information
content and in turn, in more precise parameter estimates.Comment: Accepted for 21st IFAC World Congres
What can systems and control theory do for agricultural science?
Abstract: While many professionals with a background in agricultural and bio-resource sciences work with models, only few have been exposed to systems and control theory. The purpose of this paper is to elucidate a selection of methods from systems theory that can be beneficial to quantitative agricultural science. The state space representation of a dynamical system is the corner stone in the mainstream of systems theory. It is not well known in agro-modelling that linearization followed by evaluation of eigenvalues and eigenvectors of the system matrix is useful to obtain dominant time constants and dominant directions in state space, and offers opportunities for science-based model reduction. The continuous state space description is also useful in deriving truly equivalent discrete time models, and clearly shows that parameters obtained with discrete models must be interpreted with care when transferred to another model code environment. Sensitivity analysis of dynamic models reveals that sensitivity is time and input dependent. Identifiability and sensitivity are essential notions in the design of informative experiments, and the idea of persistent excitation, leading to dynamic experiments rather than the usual static experiments can be very beneficial. A special branch of systems theory is control theory. Obviously, control plays an important part in agricultural and bio-systems engineering, but it is argued that also agronomists can profit from notions from the world of control, even if practical control options are restricted to alleviating growth limiting conditions, rather than true crop control. The most important is the idea of reducing uncertainty via feed-back. On the other hand, the systems and control community is challenged to do more to address the problems of real life, such as spatial variability, measurement delays, lacking data, environmental stochasticity, parameter variability, unavoidable model uncertainty, discrete phenomena, variable system structures, the interaction of technical ad living systems, and, indeed, the study of the functioning of life itself
Delay identification in time-delay systems using variable structure observers
In this paper we discuss delay estimation in time-delay systems. In the introduction section a short overview is given of some existing estimation techniques as well as identifiability studies. In the following sections we propose several algorithms for the delay identification based on variable structure observers
A discontinuous extended Kalman filter for non-smooth dynamic problems
Problems that result into locally non-differentiable and hence non-smooth state-space equations are often encountered in engineering. Examples include problems involving material laws pertaining to plasticity, impact and highly non-linear phenomena. Estimating the parameters of such systems poses a challenge, particularly since the majority of system identification algorithms are formulated on the basis of smooth systems under the assumption of observability, identifiability and time invariance. For a smooth system, an observable state remains observable throughout the system evolution with the exception of few selected realizations of the state vector. However, for a non-smooth system the observable set of states and parameters may vary during the evolution of the system throughout a dynamic analysis. This may cause standard identification (ID) methods, such as the Extended Kalman Filter, to temporarily diverge and ultimately fail in accurately identifying the parameters of the system. In this work, the influence of observability of non-smooth systems to the performance of the Extended and Unscented Kalman Filters is discussed and a novel algorithm particularly suited for this purpose, termed the Discontinuous Extended Kalman Filter (DEKF), is proposed
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