Special Issue "Industrial Applications of System Identification"
Deadline for manuscript submissions: 1 January 2022.
Determining the model of a dynamical system is an important problem in engineering. Such a model allows, among other things, a better understanding of the system studied, an analysis of the interactions and causal relationships between different variables and quantities relating to the process, and the observation and prediction of some of these variables.
Generically, two processes are distinguished for the development of a model. The first process consists in decomposing the system into elementary subsystems, then, via the addition of elementary laws of physics, finance, life, etc., it is possible to build a dynamical model of the whole system. This type of modeling is called white box modeling, and it has two main drawbacks. First of all, it requires a thorough knowledge of these elementary laws and of the internal behavior of the system. Then, it often induces the determination of a complex model, based on partial derivatives or unknown parameters, for instance, and consequently difficult to exploit.
The second process for developing a dynamical model consists in carrying out one or more experiments on the system and then extracting a coherent dynamical model from these experiments. This second process requires acting specifically on the system (the variables through which it is possible to act on the system are called the inputs) and carrying out descriptive measurements of the behavior of the system (the variables via which it is possible to observe the behavior of the system are called the outputs). This type of modeling is called black box modeling or identification. It is this type of modeling that interests us here.
We are interested here in the industrial applications of parametric identification methods. While the literature reports numerous techniques for the implementation of such a process, this Special Issue is dedicated more particularly to the identification of dynamical systems in the form of transfer function or in the form of state representation, discrete time or continuous time. Experiments can be in open loop or in closed loop, while systems can be linear or nonlinear.
Please note that contribution must describe in details (to help understanding) the industrial system and the identification process (description of the identification algorithm used, the design of the experiment, the validation step).
Prof. Dr. Mathieu Pouliquen
Manuscript Submission Information
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- dynamical system
- transfer function model
- state space model
- open loop or closed loop
- discrete-time or continuous time model
- linear or nonlinear system
- industrial application
- system Identification
- data based modelling