Identification of Nonparametric Nonlinear Systems

Presently, a modelling and identification of nonlinear systems is proposed. This study is developed based on spectral approach. The proposed nonlinear system is nonparametric and can be described by Hammerstein models. These systems consist of nonlinear element followed by a linear block. This latter (the linear subsystem) is not necessarily parametric and the nonlinear function can be nonparametric smooth nonlinearity. This identification problem of Hammerstein models is studied in the presence of possibly infinite-order linear dynamics. The determination of linear and nonlinear block can be done using a unique stage

Ant colony optimization algorithm and fuzzy logic for switched reluctance generator control

This article discusses two methods to control the output voltage of switched reluctance generators (SRGs) used in wind generator systems. To reduce the ripple of the SRG output voltage, a closed-loop voltage control technique has been designed. In the first method, a proportional-integral (PI) controller is used. The parameters of the PI controller are tuned based on the voltage variation. The SRG is generally characterized by strong nonlinearities. However, finding appropriate values for the PI controller is not an easy task. To overcome this problem and simplify the process of tuning the PI controller parameters, a solution based on the ant colony optimization algorithm (ACO) was developed. To settle the PI parameters, several cost functions are used in the implementation of the ACO algorithm. To control the SRG output voltage, a second method was developed based on the fuzzy logic controller. Unlike several previous works, the proposed methods, ACO and fuzzy logic control, are easy to implement and can solve numerous optimization problems. To check the best approach, a comparison between the two methods was performed. Finally, to show the effectiveness of this study, we present examples of simulations that entail the use of a three-phase SRG with a 12/8 structure and SIMULINK tools

Identification of hammerstein-wiener systems including backlash input nonlinearities

Postprint (published version

Identification of Nonparametric Nonlinear Systems

Presently, a modelling and identification of nonlinear systems is proposed. This study is developed based on spectral approach. The proposed nonlinear system is nonparametric and can be described by Hammerstein models. These systems consist of nonlinear element followed by a linear block. This latter (the linear subsystem) is not necessarily parametric and the nonlinear function can be nonparametric smooth nonlinearity. This identification problem of Hammerstein models is studied in the presence of possibly infinite-order linear dynamics. The determination of linear and nonlinear block can be done using a unique stage

Spectral Determination of Nonlinear System Parameters

In this paper we propose an identification method of nonlinear system. This later can be structured by Wiener models. The determination of nonlinear system parameters can be done using spectral analysis. The system nonlinearity is allowed to be noninvertible general shape nonlinearity but it must be approximated by a polynomial function. The polynomial degree n can vary from one interval to another. The linear dynamic element is not-necessarily parametric but BIBO stable. In this work, a spectral method is developed allowing the estimates of the complex frequency gain as well as the estimates of nonlinear block parameters the identification method is built using one stage

Frequency identification of Wiener systems with hard backlash nonlinearity

International audienceThis paper addresses the problem of Wiener system identification. The linear subsystem is stable but not necessarily parametric. The nonlinear subsystem is a backlash element with not necessarily symmetric borders. A common difficulty to all Wiener systems is that their models are not uniquely defined: they are unique up to an uncertain multiplicative factor. Then, it makes sense to start the frequency identification process estimating first the system phase as this is identical (modulo pi) for all models. To this end, the system frequency response, at a given frequency, is characterized by a family of Lissajous-like curves each one is characterized by a phase value. It is shown that the only admissible curve (i.e. one that presents lateral straight borders) is the one corresponding to the true phase value (modulo ). Interestingly, the admissible Lissajous-like curves, obtained with different frequencies, are all candidates to represent the system nonlinearity. But, the most convenient one is the less spread. The spread concept will also prove to be useful in the estimation of the frequency gain modulus

Parameter Identification of Switched Reluctance Motor SRM Using Exponential Swept-Sine Signal

Switched reluctance motors (SRMs) received major interest in several domains, e.g., in electric vehicles. This interest is due to the many advantages of SRMs, including operation at a wide range of speeds, high performances, low cost, robustness to run under degraded conditions, and controllability. One of the major aspects in the design and implementation of controllers for SRMs is the estimation of the motor parameters. An accurate estimate of these parameters is a challenge due to the highly nonlinear behavior of SRMs in addition to their magnetic saturated operating mode to maximize the energy transfer. This paper aims at estimating the parameters of SRM by developing a new SRM model using an analytical technique. The proposed technique is based on a parallel connection of several Hammerstein models that have polynomial nonlinearity. The model is driven by a swept-sine signal, and then finite element method analysis is performed to estimate the SRM parameters. The effectiveness of the proposed method is highlighted by numerical simulation. All these simulations were performed using MATLAB/SIMULINK