Systems Identification Algorithms and Software Tools for the Application of MPC in Process Control Systems

On March 10, we organized a scientific seminar on “Systems Identification Algorithms and Software Tools for the Application of MPC in Process Control Systems “. The scientific seminar was led by Gabriele Pannocchia, a professor at the University of Pisa. The scientific seminar was organized in the framework of the FrontSeat project, as part of the seminar series on “Research Seminar on Smart Cybernetics”.

Abstract:

The widespread adoption of multivariable optimization-based control systems, usually known as Model Predictive Control (MPC) technologies, have boosted the research and development activities in numerous related fields. In particular, systems identification methods gained tremendous importance, especially for large multivariable processes, which pose significant challenges to control engineers due to large settling times, process noise, correlations among (output and/or input) variables, and operational constraints. The scientific lecture is divided into three parts. The first part will present an overview of the basic steps of a multivariable system identification application project, covering the main theoretical foundations of (open-loop and closed-loop) data collection, data treatment, identification algorithms, and model validation. In the second part, a tutorial review of subspace identification algorithms will be given, covering both standard algorithms (e.g., N4SID, MOESP, etc.) and advanced parsimonious methods which can be consistently applied to closed- loop data. The last part will be devoted to presenting open-source software tools for systems identification and MPC design, including process control examples.

This project has received funding from the European Union’s Horizon under grant no. 101079342 (Fostering Opportunities Towards Slovak Excellence in Advanced Control for Smart Industries).