In this paper, a distributed output regulation problem is formulated for a class of uncertain nonlinear multi-agent systems subject to local disturbances. The formulation is given to study a leader-following problem when the leader contains unknown inputs and its dynamics is different from those of the followers. Based on the conventional output regulation assumptions and graph theory, distribu...

The problem of passivity analysis finds important applications in many signal processing systems such as digital quantizers, decision feedback equalizers and digital and analog filters. This paper considers the passivity analysis problem for a large class of systems which involve uncertain parameters, time delays, quantization errors, and unmodeled high order dynamics. By characterizing these a...

In this paper, we consider the robust H1 filtering problem for a general class of uncertain linear systems described by the so-called integral quadratic constraints (IQC’s). This problem is important in many signal processing applications where noises, nonlinearity, quantization errors, time delays, and unmodeled dynamics can be naturally described by IQC’s. The main contribution of this paper ...

The paper addresses the problem of consensus robustness against small heterogeneous input delays in the agents. The agents are assumed to have first-order dynamics and may be nonlinearly coupled; the couplings maps may be uncertain, assumed only to satisfy a sector (slope) restriction and a symmetry condition. The topology of the network may be switching and uncertain, however, it is supposed t...

In this paper, exponential anti-synchronization in mean square of an uncertain memristor-based neural network is studied. The uncertain terms include non-modeled dynamics with boundary and stochastic perturbations. Based on the differential inclusions theory, linear matrix inequalities, Gronwall’s inequality and adaptive control technique, an adaptive controller with update laws is developed to...

This paper proposes a technique for reducing the number of uncertain parameters in order to simplify robust and adaptive controller design. The system is assumed to have a known structure with parametric uncertainties that represent plant dynamics variation. An original set of parameters is identified by nonlinear least-squares (NLS) optimization using noisy frequency response functions. Based ...

Integrated stochastic optimal control and system learning to simultaneously reduce parametric and model structure uncertainty can create new avenues for achieving high-performance operation of uncertain systems using model predictive control. This paper presents a generic framework for stochastic optimal control with integrated (control-oriented) model structure adaptation, and discusses genera...

This paper addresses the problem of adaptively controlling a plant with unknown parameters using communication-limited feedback. Assuming known dynamics, expressions have recently been obtained for the minimum average feedback data rate required for asymptotic stabilisability. The main purpose of this work is to demonstrate that this minimum rate does not increase if the plant parameters are un...

Abstract: The robust stabilization of uncertain linear time-varying continuous-time systems with a mode-switch dynamics is considered. Each mode is characterized by a dynamical matrix containing elements whose time behavior over bounded time intervals is sufficiently smooth to be well described by interval polynomials of arbitrary degree. The stability conditions of the switching closed-loop sy...

A novel theory of stages in cognitive development is presented, loosely corresponding to Piagetan theory but specifically oriented toward AI systems centered on uncertain inference components. Four stages are articulated (infantile, concrete, formal and reflexive), and are characterized both in terms of external cognitive achievements (a la Piaget) and in terms of internal inference control dyn...