Séminaire Control@Lyon by Mattia Mattioni: Feedforwarding for nonlinear discrete-time and sampled-data systems

Title :Feedforwarding for nonlinear discrete-time and sampled-data systems
Abstract : Discrete-time systems at large are challenging for different reasons: the renew interest derived by the so-called information industry; their involvement in several scenarios (e.g., hybrid systems, triggered systems, data-driven systems, to cite a few). However, several obstacles are generally unavoidable, even for simple classes of systems. Among these, one can include: the primitive concept of passivity which is ambiguous in discrete time so directly impacting passivity-based design for stabilization of cascaded dynamics; the loss of a geometric structure underlying the evolutions; the nonlinear algebraic equations implicitly defining the control. Several works by Monaco and Normand-Cyrot have been aimed at bridging this gap. In this sense, a different way of representing discrete-time dynamics was introduced (Monaco and Normand-Cyrot (1997)) as an alternative to the usual one, employing difference equations, for providing a differential geometric flow interpretation to the input to state evolutions. It was then profitably exploited to define a notion of u-average passivity (Monaco & Normand-Cyrot, 2011). In such a framework, this presentation will provide recent results for stabilization of discrete-time cascade systems exhibiting an upper-triangular (or feedforward) mathematical model. Stabilization is achieved through an iterative procedure by ensuring at each step global asymptotic stability (GAS) of the feedforward interconnection of two dynamics via u-average passivity based feedback. Under suitable growth assumptions on the coupling nonlinearities, the design is extended to multiple cascades according to an iterative procedure which, at each step, makes use of the Lyapunov function and cross-term arguments proposed in Sepulchre et al. (1997). Finally, the stabilizing feedback is obtained through output-damping. Then, the case of sampled-data dynamics (i.e., continuous-time plant controlled via piecewise constant control signals) will be illustrated by showing that, taking advantage of the continuous-time original system, a less conservative forwarding strategy which stays in-between the continuous and discrete-time scenarios.
Bio : 
Mattia Mattioni received his Bachelor Degree (Laurea) and Master of Science (Laurea Magistrale) in Control Engineering from La Sapienza, Università di Roma (Italy), both Magna cum Laude. In 2015, he received the double degree for the Master de Recherche en Automatique, Traitement du Signal et des Images (M2R ATSI) through the bilateral agreement STIC&A with Université Paris-SudHe received the PhD in System and Control Theory in 2018 from La Sapienza, Università di Roma (Italy) and Université Paris-Sud (France) through a joint program with DIAG “A. Ruberti” (La Sapienza, Università di Roma) under the supervision of Salvatore Monaco and Dorothée Normand-Cyrot. In 2019, he was recipient of the Premio SIDRA “Miglior Tesi di Dottorato 2019” for the best Italian PhD Thesis in Automatica. In 2019, he was also finalist for the Prix des meilleures thèses du GDR MACS et de la Section Automatique du Club EEA for the best PhD Thesis developed in a French University. From October 2018 to August 2020, he was a post-doctoral fellow (assegnista di ricerca) at DIAG “A. Ruberti” (La Sapienza, Università di Roma). Since September 2020 he has been Assistant Professor at  DIAG “A. Ruberti” (La Sapienza, Università di Roma). His research is mainly concerned with hybrid systems, networked systems and nonlinear systems under sampling and time-delays.i
Link : 
Monday, Apr 26, 2021 2:00 pm | 2 hours | (UTC+02:00) Brussels, Copenhagen, Madrid, Paris
Meeting number: 121 193 9686
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Date(s) - 26 Avr 2021
14 h 00 min - 15 h 00 min


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