Identification Of Block-oriented Systems Research Articles

Block-oriented system identification for nonlinear modeling of all-solid-state Li-ion battery technology

The high energy density characteristic of the all-solid-state battery technology makes it one of the promising competitors of current liquid electrolyte-based lithium-ion batteries. However, due to the low ionic conductivity of solid materials and existing issues in active material-solid electrolyte interfaces, all-solid-state batteries show strong nonlinear behavior when the amplitude of input current is relatively high. In this paper, we have developed a methodology based on block-oriented nonlinear systems that can detect, quantize, and model the battery behavior, accurately. For this purpose, two solid-state coin cells, which have different ionic conductivity at the cathode interface are manufactured and excited with multisine input current. The Best Linear Approximation (BLA) method has been used to separate linear frequency response function (FRF) from nonlinear distortion. Then, a Hammerstein-Wiener system has been developed and parametrized to model the nonlinear distortions caused by ionic conductivity variation of the solid electrolyte at the cathode layer. Furthermore, the developed model has been utilized to detect possible faults in the electrode-electrolyte interface based on the nonlinearity level of the measured voltage.

Interval variable step-size spline adaptive filter for the identification of nonlinear block-oriented system

In order to improve the convergence speed of the nonlinear spline adaptive filter (SAF) in the identification of block-oriented systems, an interval variable step-size algorithm is proposed. Traditional SAF algorithm uses constant step size during iteration, leading to a contradiction between convergence speed and steady-state accuracy. In this paper, a new kind of variable step-size algorithm is proposed, fully considering the particularity of spline interpolation in the nonlinear part of the block-oriented model. The step size of each interpolation interval is independent from that of other intervals, and it is dominated by the correlated squared error which is evaluated by an exponential-weighted averaging (EWA) process. In this paper, the independent step size in each interpolation interval is also updated through an EWA process of the correlated error. The effects of the parameters on the convergence performance of the proposed strategy have been theoretically analyzed and verified by simulations. Finally, some numerical simulations have confirmed that the proposed interval variable step-size approach can significantly improve the convergence speed as well as reduce the steady-state error compared with the traditional SAF and the existing variable step-size SAF algorithms.

Identification of Block-oriented Systems with Rate Saturation Nonlinearity

The identification of systems consisting of a series cascade of a linear subsystem and a dynamic nonlinearity is considered. A method based on the concept of best linear approximation is proposed for estimating the parameters of the linear dynamics as well as the rate saturation limit. In particular, the feasibility of separating the dynamics of the subsystems using excitation with two different amplitude levels is investigated. The technique provides a characterisation of the system across a wide range of frequencies.

Iterative Method in the Identification of Block-Oriented Systems Based on Biconvex Optimization

In this paper, we investigate the identification of the class of block-oriented nonlinear systems presented by Li et al. [2011] by using an iterative method. Firstly a common model is proposed to represent such block-oriented systems. Then identifying the common model is formulated as a biconvex optimization problem. Based on this, a normalized alterative convex search (NACS) algorithm is proposed under a given arbitrary nonzero initial condition. It is shown that we only need to find the unique partial optimum point of a biconvex cost function in the formulated optimization problem in order to obtain its global minimum point. Thus, the convergence property of the proposed algorithm is established under arbitrary nonzero initial conditions. The approach presented in this paper provides a unified framework for the identification of block-oriented systems.

Mixed parametric-nonparametric identification of Hammerstein and Wiener systems - a survey

The paper surveys the ideas of cooperation between parametric and nonparametric (kernel-based) algorithms of nonlinear block-oriented system identification. Various strategies are proposed, dependently on the system structure, number of data and the prior knowledge. The estimates are consistent and their rates of convergence are presented. The aim of the paper is to show some recent results in the field in a systematic, ordered way.

Identification of block-oriented systems in the presence of nonparametric input nonlinearities of switch and backlash types

The problem of identifying Hammerstein-like systems containing dynamic nonlinearities, of the switch or backlash types, is considered. Interestingly, the nonlinearity borders are nonparametric borders (i.e. of unknown structure) and so are allowed to be noninvertible and cross each other. A semi-parametric identification approach is developed to estimate the linear subsystem parameters and m points on both nonlinearity borders. It relies on two main experiments designed so that during each one, the focus is on one lateral border exciting m specific points. Doing so, the initial nonparametric identification problem is decomposed into two simpler problems involving static parametric nonlinearities. The new problems are dealt with independently using least squares type estimators. It is formally shown that the experiments generate persistently exciting signals ensuring the consistency of all involved parameter estimators.

Semiparametric Identification of Block-Oriented Systems

In this paper, we give an overview of the problem of identification of a class of block-oriented systems from the view point of the theory of semiparametric estimation. The semiparametric methodology restricts the system parametrization to a finite-dimensional parameter and nonlinear characteristics that run typically through a nonparametric class of univariate functions. A general methodology for identifying semiparametric block-oriented systems is examined. This is explained by a semiparametric version of least squares applied to the multivariate Hammerstein system. The statistical accuracy of the resulting identification algorithms is discussed.

Nonlinear System Identification of Block-oriented Systems using Local Affine Models

The nonlinear system identification of block-oriented systems like Hammerstein and Wiener systems is considered. The static nonlinearity is modeled by local affine models whose input space decomposition can be fixed a-priori or can be learnt during optimization. The optimization is done in output error configuration and is solved via the Levenberg-Marquardt algorithm using finite differences. The local affine model used is LOLIMOT which is flexible and robust and allows to model all kinds of smooth and unique nonlinear functions. Furthermore, a regularization operator especially for local affine models is applied which preserves the local interpretability of the submodels during the global optimization which optimizes the parameters of all submodels and the linear dynamic subsystem simultaneously.

Identification of nonlinear block-oriented systems by the recursive kernel estimate

The identification of block-oriented systems by the kernel recursive estimate is discussed. Three types of system are considered: cascade of nonlinear memoryless system, Hammerstein system and Wiener system. The optimal model of memoryless cascade system is derived and estimated by the recursive kernel regression estimate. Nonlinear dynamical systems of Hammerstein and Wiener type with arbitrary nonlinearities are identified by means of nonparametric techniques. Convergence of identification procedures is investigated.