Identification of block-oriented nonlinear systems starting from linear approximations: A survey
Block-oriented nonlinear models are popular in nonlinear system identification because of
their advantages of being simple to understand and easy to use. Many different identification …
their advantages of being simple to understand and easy to use. Many different identification …
Iterative model identification of nonlinear systems of unknown structure: Systematic data-based modeling utilizing design of experiments
P Schrangl, P Tkachenko… - IEEE Control Systems …, 2020 - ieeexplore.ieee.org
High-quality models are essential to the performance of many control-related tasks [1]-[3]. If
the structure of the system is known, first principle models can be created (which constitutes …
the structure of the system is known, first principle models can be created (which constitutes …
Data-driven design of two degree-of-freedom nonlinear controllers: The D2-IBC approach
In this paper, we introduce and discuss the Data-Driven Inversion-Based Control (D 2-IBC)
method for nonlinear control system design. The method relies on a two degree-of-freedom …
method for nonlinear control system design. The method relies on a two degree-of-freedom …
Identification of systems with localised nonlinearity: From state-space to block-structured models
This paper presents a method that generates initial estimates for a rather general block-
structured model, starting from the (more general) polynomial nonlinear state-space model …
structured model, starting from the (more general) polynomial nonlinear state-space model …
A nonlinear blind identification approach to modeling of diabetic patients
In the last decades, mathematical models have become of great importance in the context of
diabetes treatment planning. Several modeling approaches based on first principles or input …
diabetes treatment planning. Several modeling approaches based on first principles or input …
Sparse identification of nonlinear functions and parametric set membership optimality analysis
C Novara - IEEE Transactions on automatic control, 2012 - ieeexplore.ieee.org
Sparse identification can be relevant in the automatic control field to solve several problems
for nonlinear systems such as identification, control, filtering, fault detection. However …
for nonlinear systems such as identification, control, filtering, fault detection. However …
Sufficient conditions for parameter convergence over embedded manifolds using kernel techniques
The persistence of excitation (PE) condition is sufficient to ensure parameter convergence in
adaptive estimation problems. Recent results on adaptive estimation in reproducing kernel …
adaptive estimation problems. Recent results on adaptive estimation in reproducing kernel …
On the initialization of nonlinear LFR model identification with the best linear approximation
Balancing the model complexity and the representation capability towards the process to be
captured remains one of the main challenges in nonlinear system identification. One …
captured remains one of the main challenges in nonlinear system identification. One …
Identification of Hammerstein-Wiener model with discontinuous input nonlinearity
This paper deals with the identification of Hammerstein-Wiener models with an irregular
function in the input block. These models comprise a set of linear segments. The linear time …
function in the input block. These models comprise a set of linear segments. The linear time …
Learning a nonlinear controller from data: theory, computation, and experimental results
The problem of learning a nonlinear controller directly from experimental data is considered.
It is assumed that an existing, unknown controller, able to stabilize the plant, is available …
It is assumed that an existing, unknown controller, able to stabilize the plant, is available …