How can I extract a model from my experiments? How can I estimate a parameter value from my data? What is the impact of disturbances on the quality of my results? What is the “best” model that I can obtain from a given data set? Should I use a nonlinear model, or will a linear model do?
All these questions deal with the extraction of information from data? Data Driven Modeling offers a generic framework to address these questions. System Identification is the sub-field that is focused on retrieving mathematical models for dynamical systems starting from experimental data.
The goal of this website is twofold. Firstly, nonlinear system identification is introduced to a wide audience, guiding practical engineers and newcomers in the field to a sound solution of their data-driven modeling problems for nonlinear dynamic systems. In addition, the website also provides a broad perspective on the topic for researchers who are already familiar with linear system identification theory, showing the similarities and differences between linear and nonlinear problems.
The focus is on the basic philosophy, giving an intuitive understanding of the problems and the solutions, providing a guided tour of the wide range of user choices in (non)linear system identification. To reach these goals, we will make use of slides, supported by video presentations and short texts. Links are provided for the readers who want to learn more or to refresh their background knowledge. The existing literature will be referred too for detailed mathematical explanations and formal proofs.
The information is structured along two main lines: the development a Data Driven Modeling framework that is focused on the theoretical aspects, and a series of Exercises that provide hands-on experience.
Data Driven Modeling introduces the basic concepts of System Identification. Next, these tools are further used to present a framework to the Identification of Linear Systems and the Identification of Nonlinear Systems. Nonlinear system identification is much more involved than linear identification. For that reason, the intermediate solution Linear Modeling in the Presence of Nonlinear Distortions may be an acceptable solution to keep the modeling effort low.
The Exercises highlight many of the important steps in the identification process and give the user the possibility to provide hands-on experience. This helps to make the abstract concepts of the theory more accessible. The development of this website is a long-term project. Starting from the current basis, we intend to expand/update gradually the information in the coming years. We decided to make the website publicly accessible in this period, even if it is far from being finished. It is our strong believe that also the partial information can be very useful for many of our users. Moreover, the feedback that we get from these early experiences provides very valuable inputs for the further development of our project.