A structured overview of data-driven predictive control methods
Speaker: Prof. Mircea Lazar
DEIB - Seminar Room "N. Schiavoni" (Bld. 20)
February 27th, 2024
11.30 am
Contact: Prof. Simone Formentin
Research Line: Control systems
DEIB - Seminar Room "N. Schiavoni" (Bld. 20)
February 27th, 2024
11.30 am
Contact: Prof. Simone Formentin
Research Line: Control systems
Abstract
On February 27th, 2024 at 11.30 am the seminar "A structured overview of data-driven predictive control methods" will take place at Dipartimento di Elettronica, Informazione e Bioingegneria, Seminar Room "Nicola Schiavoni" (Building 20).
Data-driven predictive control (DPC) has gained an increased interest as an alternative to model predictive control in recent years, since it requires less system knowledge for implementation and reliable data is commonly available in smart engineering systems. Several data-driven predictive control algorithms have been developed recently, which largely follow similar approaches, but with specific formulations and tuning parameters.
This talk will provide a structured and accessible overview of data-driven predictive control methods, such as subspace predictive control (SPC), and data-enabled predictive control (DeePC). Newer hybrid approaches to DPC, such as 𝛾–data-driven predictive control and generalized data-driven predictive control will also be discussed. A brief analysis of underlying theory, implementation details and design guidelines, including an overview of methods to guarantee closed-loop stability and promising extensions towards handling nonlinear systems will be provided. The performance of the reviewed DPC approaches will be compared via simulations on several examples from the literature and real-time control of a 4th order mass-spring-mass-damper system.
Data-driven predictive control (DPC) has gained an increased interest as an alternative to model predictive control in recent years, since it requires less system knowledge for implementation and reliable data is commonly available in smart engineering systems. Several data-driven predictive control algorithms have been developed recently, which largely follow similar approaches, but with specific formulations and tuning parameters.
This talk will provide a structured and accessible overview of data-driven predictive control methods, such as subspace predictive control (SPC), and data-enabled predictive control (DeePC). Newer hybrid approaches to DPC, such as 𝛾–data-driven predictive control and generalized data-driven predictive control will also be discussed. A brief analysis of underlying theory, implementation details and design guidelines, including an overview of methods to guarantee closed-loop stability and promising extensions towards handling nonlinear systems will be provided. The performance of the reviewed DPC approaches will be compared via simulations on several examples from the literature and real-time control of a 4th order mass-spring-mass-damper system.
Short Bio
Dr. Mircea Lazar is an Associate Professor in Constrained control of complex systems at the Electrical Engineering Department, Eindhoven University of Technology, The Netherlands.
He received the European Embedded Control Institute Ph.D. Award in 2007 for his PhD dissertation and a Veni personal grant from the Dutch Research Council (NWO) in 2008.
He supervised 10 PhD researchers (2 received the Cum laude distinction) that received the PhD title. Lazar chaired the 4th IFAC Conference on Nonlinear Model Predictive Control, Noordwijkerhout, The Netherlands, in 2012 and he will chair the IFAC MICNON 2027 Conference. His research interests cover physics-based neural networks, nonlinear and data-driven predictive control, non-monotone Lyapunov functions, compositional stability certificates and distributed control. His research is driven by control problems in high-precision mechatronics, power electronics, power networks, water networks, automotive and biological systems. He is an Active Member of the IFAC Technical Committees 1.3 Discrete Event and Hybrid Systems, 2.3 Nonlinear Control Systems and an Associate Editor of IEEE Transactions on Automatic Control.
He received the European Embedded Control Institute Ph.D. Award in 2007 for his PhD dissertation and a Veni personal grant from the Dutch Research Council (NWO) in 2008.
He supervised 10 PhD researchers (2 received the Cum laude distinction) that received the PhD title. Lazar chaired the 4th IFAC Conference on Nonlinear Model Predictive Control, Noordwijkerhout, The Netherlands, in 2012 and he will chair the IFAC MICNON 2027 Conference. His research interests cover physics-based neural networks, nonlinear and data-driven predictive control, non-monotone Lyapunov functions, compositional stability certificates and distributed control. His research is driven by control problems in high-precision mechatronics, power electronics, power networks, water networks, automotive and biological systems. He is an Active Member of the IFAC Technical Committees 1.3 Discrete Event and Hybrid Systems, 2.3 Nonlinear Control Systems and an Associate Editor of IEEE Transactions on Automatic Control.
