【時(shí)間】2023 年12月28日（周四）下午 14：30 開始

【地點(diǎn)】線下講座，9A103會(huì)議室

【主題】

When Are Nonlinear Systems Semiglobally Asymptotically Stabilizable by Sampled-Data Feedback?

【主講人】

林威教授，凱斯西儲(chǔ)大學(xué)

【內(nèi)容簡(jiǎn)介】

Most of the existing results in the literature over the past two decades were devoted to semiglobal practical stabilization of nonlinear systems by sampled-data feedback and few results are available on semiglobal asymptotic stabilization, due to the nature of ``sample and hold'' and the resulting hybrid closed-loop system. In this talk, we present recent advances in semiglobal asymptotic control of general nonlinear systems by sampled-data feedback. For MIMO nonlinear systems, we prove that while global asymptotic stabilizability (GAS) may not ensure either global or semiglobal asymptotic stabilizability (SGAS) by sampled-data state feedback, global asymptotic local exponential stabilizability (GALES) does imply the existence of a SGAS sampled-data state feedback controller. Based on this state feedback result, we further present the main contribution of this talk: GALES and uniform observability imply semi-global asymptotic stabilizability by sampled-data output feedback, which is indeed a sampled-data version of the “separation principle” for nonlinear systems. The development of the “digital separation principle” is made possible by employing a subtle Lyapunov argument that makes the estimation of domains of attraction and semiglobal asymptotic analysis intuitive and simple, without involving intricate Lyapunov functions and the corresponding level sets. Examples and results on SGAS by sampled-data feedback for representative classes of nonlinear systems are also given as illustrations.

誠(chéng)摯歡迎廣大師生參加。