|Title:||Efficient data-driven modelling of constrained nonlinear processes||Language:||English||Authors:||Deregnaucourt, Maxime-Vianney||Qualification level:||Doctoral||Advisor:||Jakubek, Stefan||Issue Date:||2015||Number of Pages:||82||Qualification level:||Doctoral||Abstract:||
This dissertation summarizes my research activities as a PhD student at the Vienna University of Technology since 2011. My research activities originated with the collaboration between the Institute of Mechanics and Mechatronics (Division of Control and Process Automation) and AVL List GmbH and are part of the research activities of the Christian Doppler Laboratory for Model Based Calibration Methodologies. My research activities concern the efficient modelling of constrained nonlinear processes using data-based models with applications for the calibration of nonlinear systems. The calibration consists in optimizing systems by finding proper controller parameters and is confronted to the increasing complexity of the modern systems. Modern systems are constrained by legal norms or physical limits to operating ranges where the system processes must be operated optimally. For optimizing the system behaviour, modelling complex constrained nonlinear processes with high quality is therefore an important issue. For that purpose, an efficient modelling methodology relying on nonlinear system identification and design of experiments is proposed. For the nonlinear system identification, nonlinear data-driven modelling is used which consists in producing a data-based model relying on nonlinear model architecture. The main objective of nonlinear data-driven modelling is the production of a model structure and model parameters corresponding to the process using training data. The training data are generated from the system by carrying out experiments. In this context, the inputs of a system process are varied and the process output is measured. A prerequisite to data-driven modelling is that the training data describe the process behaviour characteristics. For generating training data describing the process behaviour, model-based design of experiments is used. Model-based design of experiments consists in using the model of a process for exciting the very same process so that the nonlinear process behaviour is revealed and assessed efficiently. The main challenge for model-based design of experiments is the integration of information contained in the model so that high-quality models are parameterised with the training data and process input and output constraints are complied with. In Chapter 1, the problematic, the objectives, and the mains concepts for modelling constrained nonlinear processes with data-based models are described, as well as the contributions to the selected journal publications. In Chapter 2, the selected journal publications and my own contribution are presented.
|Keywords:||System Identification; Design of Experiments; Nonlinear System||URI:||https://doi.org/10.34726/hss.2015.28033
|DOI:||10.34726/hss.2015.28033||Library ID:||AC12189363||Organisation:||E325 - Institut für Mechanik und Mechatronik||Publication Type:||Thesis
|Appears in Collections:||Thesis|
Show full item record
Files in this item:
|Deregnaucourt Maxime-Vianney - 2015 - Efficient data-driven modelling of...pdf||1.5 MB||Adobe PDF|
checked on May 5, 2021
checked on May 5, 2021
Items in reposiTUm are protected by copyright, with all rights reserved, unless otherwise indicated.