749 research outputs found
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Iterative procedures for identification of nonlinear interconnected systems
This work addresses the identification problem of a discrete-time nonlinear system composed by linear and nonlinear subsystems. Systems in this class will be represented by Linear Fractional
Transformations. Iterative identification procedures are examined, that alternate between the estimation of the linear and the nonlinear components. The burden of identification falls naturally on the nonlinear subsystem, as techniques for identification of linear systems have long been established. Two approaches are
examined. A point-wise identification of the nonlinearity, recently proposed in the literature, is applied and its advantages and
drawbacks are outlined. An alternative procedure that employs piecewise affine approximation techniques is proposed. Numerical examples demonstrate the efficiency of the proposed algorithm
A new kernel-based approach for overparameterized Hammerstein system identification
In this paper we propose a new identification scheme for Hammerstein systems,
which are dynamic systems consisting of a static nonlinearity and a linear
time-invariant dynamic system in cascade. We assume that the nonlinear function
can be described as a linear combination of basis functions. We reconstruct
the coefficients of the nonlinearity together with the first samples of
the impulse response of the linear system by estimating an -dimensional
overparameterized vector, which contains all the combinations of the unknown
variables. To avoid high variance in these estimates, we adopt a regularized
kernel-based approach and, in particular, we introduce a new kernel tailored
for Hammerstein system identification. We show that the resulting scheme
provides an estimate of the overparameterized vector that can be uniquely
decomposed as the combination of an impulse response and coefficients of
the static nonlinearity. We also show, through several numerical experiments,
that the proposed method compares very favorably with two standard methods for
Hammerstein system identification.Comment: 17 pages, submitted to IEEE Conference on Decision and Control 201
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B-spline neural networks based PID controller for Hammerstein systems
A new PID tuning and controller approach is introduced for Hammerstein systems based on input/output data. A B-spline neural network is used to model the nonlinear static function in the Hammerstein system. The control signal is composed of a PID controller together with a correction term. In order to update the control signal, the multi-step ahead predictions of the Hammerstein system based on the B-spline neural networks and the associated Jacobians matrix are calculated using the De Boor algorithms including both the functional and derivative recursions. A numerical example is utilized to demonstrate the efficacy of the proposed approaches
Initializing Wiener-Hammerstein Models Based on Partitioning of the Best Linear Approximation
This paper describes a new algorithm for initializing and estimating Wiener-
Hammerstein models. The algorithm makes use of the best linear model of the system which
is split in all possible ways into two linear sub-models. For all possible splits, a Wiener-
Hammerstein model is initialized which means that a nonlinearity is introduced in between
the two sub-models. The linear parameters of this nonlinearity can be estimated using leastsquares.
All initialized models can then be ranked with respect to their fit. Typically, one is only
interested in the best one, for which all parameters are fitted using prediction error minimization.
The paper explains the algorithm and the consistency of the initialization is stated. Computational
aspects are investigated, showing that in most realistic cases, the number of splits of
the initial linear model remains low enough to make the algorithm useful. The algorithm is
illustrated on an example where it is shown that the initialization is a tool to avoid many local
minima
A Hammerstein-bilinear approach with application to heating ventilation and air conditioning systems
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