Stability of Singular Equilibria in Quasilinear Implicit Differential Equations

AbstractThis paper addresses stability properties of singular equilibria arising in quasilinear implicit ODEs. Under certain assumptions, local dynamics near a singular point may be described through a continuous or directionally continuous vector field. This fact motivates a classification of geometric singularities into weak and strong ones. Stability in the weak case is analyzed through certain linear matrix equations, a singular version of the Lyapunov equation being especially relevant in the study. Weak stable singularities include singular zeros having a spherical domain of attraction which contains other singular points. Regarding strong equilibria, stability is proved via a Lyapunov–Schmidt approach under additional hypotheses. The results are shown to be relevant in singular root-finding problems

Discretization of implicit ODEs for singular root-finding problems

AbstractThis paper addresses the use of dynamical system theory to tackle singular root-finding problems. The use of continuous-time methods leads to implicit differential systems when applied to singular nonlinear equations. The analysis is based on a taxonomy of singularities and uses previous stability results proved in the context of quasilinear implicit ODEs. The proposed approach provides a framework for the systematic formulation of quadratically convergent iterations to singular roots. The scope of the work includes also the introduction of discrete-time analysis techniques for singular problems which are based on continuous-time stability and numerical stability. Some numerical experiments illustrate the applicability of the proposed techniques

Mean Field Behaviour of Collaborative Multi-Agent Foragers

Collaborative multi-agent robotic systems where agents coordinate by modifying a shared environment often result in undesired dynamical couplings that complicate the analysis and experiments when solving a specific problem or task. Simultaneously, biologically-inspired robotics rely on simplifying agents and increasing their number to obtain more efficient solutions to such problems, drawing similarities with natural processes. In this work we focus on the problem of a biologically-inspired multi-agent system solving collaborative foraging. We show how mean field techniques can be used to re-formulate such a stochastic multi-agent problem into a deterministic autonomous system. This de-couples agent dynamics, enabling the computation of limit behaviours and the analysis of optimality guarantees. Furthermore, we analyse how having finite number of agents affects the performance when compared to the mean field limit and we discuss the implications of such limit approximations in this multi-agent system, which have impact on more general collaborative stochastic problems

A mathematical framework for new fault detection schemes in nonlinear stochastic continuous-time dynamical systems

n this work, a mathematical unifying framework for designing new fault detection schemes in nonlinear stochastic continuous-time dynamical systems is developed. These schemes are based on a stochastic process, called the residual, which reflects the system behavior and whose changes are to be detected. A quickest detection scheme for the residual is proposed, which is based on the computed likelihood ratios for time-varying statistical changes in the Ornstein–Uhlenbeck process. Several expressions are provided, depending on a priori knowledge of the fault, which can be employed in a proposed CUSUM-type approximated scheme. This general setting gathers different existing fault detection schemes within a unifying framework, and allows for the definition of new ones. A comparative simulation example illustrates the behavior of the proposed schemes

A general formulation for fault detection in stochastic continuous-time dynamical systems

Abstract In this work, a general formulation for fault detection in stochastic continuoustime dynamical systems is presented. This formulation is based on the definition of a pre-Hilbert space so that orthogonal projection techniques, based on the statistics of the involved stochastic processes can be applied. The general setting gathers different existing schemes within a unifying framework

Adaptive Cellular Integration of Dynamical Systems Constructed for Locating Regular and Singular Invariants of a Given Flow

Some issues concerning cell mapping methods for the analysis of nonlinear dynamical systems are addressed in this paper. Convergence properties of simple and adjoining cell mapping are analyzed in the context of a general theory of convergence for cellular techniques. This theory takes account of the use of an underlying numerical method, unifying cellular and numerical aspects in a comprehensive framework. The behavior of these techniques concerning some singularity issues of a specific family of dynamical systems is also studied. Finally, the confluence of convergence and singularity aspects leads to the introduction of hybrid cell mapping as a new cellular tool. 1 Introduction Cell mapping technique is a method of global analysis for nonlinear dynamical systems [2], based on a discretization of the state space in cells and the numerical computation of a finite number of trajectories for constructing a cell-to-cell mapping, which can be considered as a discrete approximation of the ..