Current students


Section: Systems and Control

Major Research topic:
Machine learning based data-driven control of nonlinear systems

One of the main factors preventing the application of advanced control techniques to real systems is the lack of an adequate model. When a physical model of the system is not available, e.g. when deriving it from first-principle equations is cumbersome, it is possible to resort to data-driven control strategies, i.e. to exploit the data collected from the field to infer information on the system and synthesize a controller. The goal of the research project is to investigate the application of machine-learning techniques in the context of data-driven control strategies applied to nonlinear systems.  The first part of the project is devoted to indirect data-driven control strategies, and in particular to the employment of Neural Networks as black-box models of nonlinear dynamical systems. In order to effectively adopt such models for control purposes, several concerns must be addressed, namely: ;
  • The choice of the structure of the Neural Network
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  • The definition of an appropriate training strategy
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  • The estimation of a bound on the prediction error
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  • The assessment of theoretical properties of the trained Neural Network, such as the Input-to-State Stability, which can be useful both during the controller design phase and to certify the representational capability of the network.
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;  In the latter part of the project, the possible applications of machine-learning techniques to direct data-driven control strategies will be investigated, comparing the performances and advantages of direct methods to those of indirect ones.