On-line identification and control of chaos in a real Chua's circuit
On-line parameter estimation and two-level control of large-scale discrete time systems
On-line wavelet estimation of Hammerstein system nonlinearity
A new algorithm for nonparametric wavelet estimation of Hammerstein system nonlinearity is proposed. The algorithm works in the on-line regime (viz., past measurements are not available) and offers a convenient uniform routine for nonlinearity estimation at an arbitrary point and at any moment of the identification process. The pointwise convergence of the estimate to locally bounded nonlinearities and the rate of this convergence are both established.
Optimal reconstruction of state vector in 2-D systems
Optimization and identification of nonlinear uncertain systems
In this paper we consider the optimal control of both operators and parameters for uncertain systems. For the optimal control and identification problem, we show existence of an optimal solution and present necessary conditions of optimality.
Parameter identification and synchronization of dynamical system by introducing an auxiliary subsystem.
Parametric Identification of Sorensen model for glucose-insulin-carbohydrates dynamics using evolutive algorithms
Diabetes mellitus (DM) is a disease affecting millions of people worldwide, and its medical care brings an economic wear to patients and public health systems. Many efforts have been made to deal with DM, one of them is the full-automation of insulin delivery. This idea consists in design a closed-loop control system to maintain blood glucose levels (BGL) within normal ranges. Dynamic models of glucose-insulin-carbohydrates play an important role in synthesis of control algorithms, but also in other...
Planning identification experiments for cell signaling pathways: An NFκB case study
Mathematical modeling of cell signaling pathways has become a very important and challenging problem in recent years. The importance comes from possible applications of obtained models. It may help us to understand phenomena appearing in single cells and cell populations on a molecular level. Furthermore, it may help us with the discovery of new drug therapies. Mathematical models of cell signaling pathways take different forms. The most popular way of mathematical modeling is to use a set of nonlinear...
Programming means for simulation of logical networks. II
Programming means for simulation of logical networks. III
Range identification for a perspective dynamic system with a single homogeneous observation
Perspective problems arise in machine vision when using a camera to observe the scene. Essential problems include the identification of unknown states and/or unknown parameters from perspective observations. Range identification is used to estimate the states/positions of a moving object with known motion parameters. Range estimation has been discussed in the literature using nonlinear observers with full homogeneous observations derived from the image plane. In this paper, the same range identification...
Reconstruction of the parameters of a system of connected beams from dynamic boundary measurements.
Reduced-order Unscented Kalman Filtering with application to parameter identification in large-dimensional systems
We propose a general reduced-order filtering strategy adapted to Unscented Kalman Filtering for any choice of sampling points distribution. This provides tractable filtering algorithms which can be used with large-dimensional systems when the uncertainty space is of reduced size, and these algorithms only invoke the original dynamical and observation operators, namely, they do not require tangent operator computations, which of course is of considerable benefit when nonlinear operators are considered....
Reduced-order Unscented Kalman Filtering with application to parameter identification in large-dimensional systems
We propose a general reduced-order filtering strategy adapted to Unscented Kalman Filtering for any choice of sampling points distribution. This provides tractable filtering algorithms which can be used with large-dimensional systems when the uncertainty space is of reduced size, and these algorithms only invoke the original dynamical and observation operators, namely, they do not require tangent operator computations, which of course is of considerable benefit when nonlinear operators are considered....
Regional detection and reconstruction of unknown internal or boundary sources
The purpose of this paper is to study the problem of regional detection, to characterize internal or boundary regionally detectable sources and regionally spy sensors, and to establish a relationship between these sensors and regionally strategic sensors. It is shown how to reconstruct a regionally detectable internal or a boundary source from a given output, with an extension to the case when the output is affected by an observation error. Numerical results are given in the case of a diffusion...
Remarks on Parameter Identification. I.
Robust identification of parasitic feedback disturbances for linear lumped parameter systems
We study the problem of identification of an input to a linear finite-dimensional system. We assume that the input has a feedback form, which is related to a problem often encountered in fault detection. The method we use is to embed the identification problem in a class of inverse problems of dynamics for controlled systems. Two algorithms for identification of a feedback matrix based on the method of feedback control with a model are constructed. These algorithms are stable with respect to noise-corrupted...
Robustly stable multiestimation scheme for adaptive control and identification with model reduction issues.
RTC-method for the control of nuclear reactor power
In this paper, a new concept of the Reactivity Trace Curve (RTC) for reactor power control is presented. The concept is demonstrated for a reactor model with one group of delayed neutrons, where the reactivity trace curve is simply a closed form exponential solution of the RTC-differential equation identifier. An extended reactor model of multigroup (six groups) of delayed neutrons is discussed for power control using the RTC-method which is based on numerical solution of the governing equation...
Self-tuning controllers based on orthonormal functions
Problems of the system identification using orthonormal functions are discussed and algorithms of computing parameters of the discrete time state- space model of the plant based on the generalized orthonormal functions and the Laguerre functions are derived. The adaptive LQ regulator and the predictive controller based on the Laguerre function model are also presented. The stability and the robustness of the closed loop using the predictive controller are investigated.