Reinforcement learning (RL) offers powerful algorithms to search for optimal controllers of
systems with nonlinear, possibly stochastic dynamics that are unknown or highly uncertain …

The purpose of this paper is twofold. In the first part, we give a review on the current state of
nonlinear model predictive control (NMPC). After a brief presentation of the basic principle of …

This article presents a secure communication scheme based on the quantized
synchronization of master-slave neural networks under an event-triggered strategy. First, a …

(I. la) m= f (x)+[gi (x) ui i= I (1. lb) y= h (x) and we want to examine to what extent the behavior
of such a system could be made" linear" under the effect of an appropriate feedback control …

A method is proposed for designing controllers with arbitrarily small tracking error for
uncertain, mismatched nonlinear systems in the strict feedback form. This method is another" …

In this document, we present the main ideas and results concerning high‐gain observers
and some of their applications in control. The introduction gives a brief history of the topic …

There are many communication imperfections in networked control systems (NCS) such as
varying transmission delays, varying sampling/transmission intervals, packet loss …

Constructive Nonlinear Control presents a broad repertoire of constructive nonlinear
designs not available in other works by widening the class of systems and design tools …

We introduce a concept of input-to-output practical stability (IOpS) which is a natural
generalization of input-to-state stability proposed by Sontag. It allows us to establish two …

This paper studies the robust optimal control design for a class of uncertain nonlinear
systems from a perspective of robust adaptive dynamic programming (RADP). The objective …