Speaker: 刘兆波，深圳大学高等研究院

Title: Optimal adaptive output regulation for discrete-time nonlinear stochastic systems

Language: Chinese

Time & Venue: 2024.06.23 09:30-10:30 腾讯会议：544-899-869

Abstract: In this presentation, we consider the output regulation problem associated with a basic class of discrete-time nonlinear stochastic systems with unknown parameters. We allow the controlled plant to exhibit highly nonlinear growth, as well as nonlinearly parameterized structures in the exosystem. The main purpose is to design an adaptive regulator to achieve the optimal performance of the regulated output. The design of the regulator is an integration of the recursive least squares estimator and a modified nonlinear least squares (NLS) algorithm. Under certain mild conditions, it is shown that with stable exosystems, the square of the norm of the closed-loop regulated output is asymptotically optimal in average sense almost surely. Specifically, we propose a new formula for the strong convergence rate of NLS and derive an almost optimal strong convergence rate of the proposed modified NLS algorithm. Additionally, we employ novel closed-loop analysis techniques to overcome the Bayesian assumptions required in existing research on the stabilizability of discrete-time nonlinear stochastic systems.