Reinforcement learning (RL) has become a highly successful framework for learning in
Markov decision processes (MDP). Due to the adoption of RL in realistic and complex …

This paper presents a survey of the most significant results on robust control theory. In
particular, we study the modeling of uncertain systems, robust stability analysis for systems …

Disturbance observer (DOB) has been one of the most widely used robust control tools since
it was proposed by Ohnishi in 1983. This paper introduces the origins of DOB and presents …

This paper provides a survey of results in linear parameter-varying (LPV) control that have
been validated by experiments and/or high-fidelity simulations. The LPV controller synthesis …

Nature discovered feedback long ago. It created feedback mechanisms and exploited them
at all levels, that are central to homeostasis and life. As a technology, control dates back at …

The presence of uncertainty in a system description has always been a critical issue in
control. The main objective of Randomized Algorithms for Analysis and Control of Uncertain …

This paper proposes a robust feedback controller using Linear Matrix Inequalities (LMIs)
formulation for the stabilization of an underactuated mechanical system, namely the Inertia …

In this paper, we apply active disturbance rejection control, an emerging control technology,
to achieve practical output tracking for a class of nonlinear systems in the presence of vast …

This paper presents a data-based robust adaptive control methodology for a class of
nonlinear constrained-input systems with completely unknown dynamics. By introducing a …

This paper presents the design and experimental validation of grid-based and Linear
Fractional Transformation (LFT) approaches for the lateral control of autonomous vehicles …