Gradient-based methods have been widely used for system design and optimization in
diverse application domains. Recently, there has been a renewed interest in studying …

Semidefinite programs are convex optimization problems involving a linear objective
function and a domain of positive-semidefinite matrices. Over the past two decades, they …

A comprehensive look at state-of-the-art ADP theory and real-world applications This book
fills a gap in the literature by providing a theoretical framework for integrating techniques …

Lyapunov functions are an essential tool in the stability analysis of dynamical systems, both
in theory and applications. They provide sufficient conditions for the stability of equilibria or …

Outlier-robust estimation is a fundamental problem and has been extensively investigated
by statisticians and practitioners. The last few years have seen a convergence across …

Historically, scalability has been a major challenge for the successful application of
semidefinite programming in fields such as machine learning, control, and robotics. In this …

This paper presents a novel method of global adaptive dynamic programming (ADP) for the
adaptive optimal control of nonlinear polynomial systems. The strategy consists of relaxing …

We study the problem of synthesizing a control strategy to enforce safety of affine-in-control
stochastic dynamical systems over finite time horizons. We use stochastic control barrier …

Die hier angesprochene Unterscheidung zwischen stetigen und diskreten Problemen ergibt
sich aus den sehr unterschiedlichen Lösungsansätzen. Grob gesprochen kann man bei …

This article deals with the extended-dissipativity-based adaptive event-triggered (AET)
control problem for stochastic polynomial fuzzy singular systems (SPFSSs). The purpose of …