In this article, we consider a robust state-space filtering problem in the case that the
transition probability density is unknown and possibly degenerate. The resulting robust filter …

This paper studies a cooperative modeling framework to reduce the complexity in deriving
the governing dynamical equations of complex systems composed of multiple bodies such …

The goal of this article is to study fundamental mechanisms behind so‐called indirect and
direct data‐driven control for unknown systems. Specifically, we consider policy iteration …

In this paper, we analyze the convergence of a family of robust Kalman filters. For each filter
of this family, the model uncertainty is tuned according to the so-called tolerance parameter …

The continuous time state-dependent Riccati equation (SDRE) technique is extended to
discrete-time under input and state constraints, yielding constrained (C) discrete-time (D) …

The purpose of this paper is to investigate the role that the so-called constrained
generalized Riccati equation plays within the context of continuous-time singular linear …

Many control problems require solving the algebraic Riccati equation (ARE). Previous
studies have focused more on solving the time-invariant ARE than on solving the time …

In this paper, we consider an arbitrary matrix-valued, rational spectral density Φ (z). We
show with a constructive proof that Φ (z) admits a factorization of the form Φ (z)= WT (z-1) W …

Abstract Model Predictive Control (MPC) is the principal control technique used in industrial
applications. Although it offers distinguishable qualities that make it ideal for industrial …

Uncertainty theory is a branch of mathematics which provides a new tool to deal with the
human uncertainty. Based on uncertainty theory, this paper proposes an optimistic value …