Over the past decade, unmanned aerial vehicles (UAVs) have received a significant
attention due to their diverse capabilities for non-combatant and military applications. The …

Unknown correlations (UCs) generally exist in a wide spectrum of practical multi-source
information fusion problems, and thereby, their corresponding fusion problems have …

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 …

The growing demand for robots able to act autonomously in complex scenarios has widely
accelerated the introduction of Reinforcement Learning (RL) in robots control applications …

Data-driven discovery is revolutionizing how we model, predict, and control complex
systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and …

This paper reviews the current state of the art on reinforcement learning (RL)-based
feedback control solutions to optimal regulation and tracking of single and multiagent …

Closed-loop turbulence control is a critical enabler of aerodynamic drag reduction, lift
increase, mixing enhancement, and noise reduction. Current and future applications have …

This book is an introduction to machine learning control (MLC), a surprisingly simple model-
free methodology to tame complex nonlinear systems. These systems are assumed to be …

In this paper, the optimal control problem for dynamical systems represented by general
high-order fully actuated (HOFA) models is formulated. The problem aims to minimise an …

This paper deals with the design of an H∞ tracking controller for nonlinear continuous-time
systems with completely unknown dynamics. A general bounded L 2-gain tracking problem …