H

This paper investigates the problem of H∞ filtering for class discrete-time Lipschitz nonlinear singular systems with measurement quantization. Assume that the system measurement output is quantized by a static, memoryless, and logarithmic quantizer before it is transmitted to the filter, while the quantizer errors can be treated as sector-bound uncertainties. The attention of this paper is focused on the design of a nonlinear quantized H∞ filter to mitigate quantization effects and ensure that the filtering error system is admissible (asymptotically stable, regular, and causal), while having a unique solution with a prescribed H∞ noise attenuation level. By introducing some slack variables and using the Lyapunov stability theory, some sufficient conditions for the existence of the nonlinear quantized H∞ filter are expressed in terms of linear matrix inequalities (LMIs). Finally, a numerical example is presented to demonstrate the effectiveness of the proposed quantized filter design method

On Hamilton-Jacobi Approaches to State Reconstruction for Dynamic Systems

We investigate the use of Hamilton-Jacobi approaches for the purpose of state reconstruction of dynamic systems. First, the classical formulation based on the minimization of an estimation functional is analyzed. Second, the structure of the resulting estimator is taken into account to study the global stability properties of the estimation error by relying on the notion of input-to-state stability. A condition based on the satisfaction of a Hamilton-Jacobi inequality is proposed to construct estimators with input-to-state stable dynamics of the estimation error, where the disturbances affecting such dynamics are regarded as input. Third, the so-developed general framework is applied to the special case of high-gain observers for a class of nonlinear systems

Projeto de filtros robustos para sistemas lineares e não lineares

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia Elétrica.Nesta tese estuda-se o projeto de filtros robustos para diversas classes de sistemas lineares e não lineares incertos usando funções de Lyapunov que são dependentes de parâmetros. O projeto do filtro é baseado na minimização dos critérios de desempenho H2 e Hinf do erro de estimação. Neste contexto, desigualdades matriciais lineares(LMIs) são utilizadas para descrever as condições do projeto do filtro. Assumindo que as incertezas presentes no sistema estão descritas na forma politópica, desenvolvem-se abordagens para sistemas a tempo contínuo e discreto no caso de sistemas lineares incertos e a tempo contínuo no caso de sistemas não lineares incertos com não linearidades de Lipschitz e racionais. Exemplos numéricos são apresentados para comprovar a eficiência dos métodos propostos

Robust airborne 3D visual simultaneous localisation and mapping

The aim of this thesis is to present robust solutions to technical problems of airborne three-dimensional (3D) Visual Simultaneous Localisation And Mapping (VSLAM). These solutions are developed based on a stereovision system available onboard Unmanned Aerial Vehicles (UAVs). The proposed airborne VSLAM enables unmanned aerial vehicles to construct a reliable map of an unknown environment and localise themselves within this map without any user intervention. Current research challenges related to Airborne VSLAM include the visual processing through invariant feature detectors/descriptors, efficient mapping of large environments and cooperative navigation and mapping of complex environments. Most of these challenges require scalable representations, robust data association algorithms, consistent estimation techniques, and fusion of different sensor modalities. To deal with these challenges, seven Chapters are presented in this thesis as follows: Chapter 1 introduces UAVs, definitions, current challenges and different applications. Next, in Chapter 2 we present the main sensors used by UAVs during navigation. Chapter 3 presents an important task for autonomous navigation which is UAV localisation. In this chapter, some robust and optimal approaches for data fusion are proposed with performance analysis. After that, UAV map building is presented in Chapter 4. This latter is divided into three parts. In the first part, a new imaging alternative technique is proposed to extract and match a suitable number of invariant features. The second part presents an image mosaicing algorithm followed by a super-resolution approach. In the third part, we propose a new feature detector and descriptor that is fast, robust and detect suitable number of features to solve the VSLAM problem. A complete Airborne Visual Simultaneous Localisation and Mapping (VSLAM) solution based on a stereovision system is presented in Chapter (5). Robust data association filters with consistency and observability analysis are presented in this chapter as well. The proposed algorithm is validated with loop closing detection and map management using experimental data. The airborne VSLAM is extended then to the multiple UAVs case in Chapter (6). This chapter presents two architectures of cooperation: a Centralised and a Decentralised. The former provides optimal precision in terms of UAV positions and constructed map while the latter is more suitable for real time and embedded system applications. Finally, conclusions and future works are presented in Chapter (7).EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Robust Nonlinear H∞ Filtering

This paper investigates the robust nonlinear H∞ filtering problem for nonlinear systems with uncertainties which are described by integral functional constraints. The objective is to design a dynamic filter such that the L2-gain from an exogenous input to an estimate error is minimized or guaranteed to be less or equal to a prescribed value for all admissable uncertainties. We establish the interconnection between the robust nonlinear H∞ filtering problem and the nonlinear H∞ filtering problem for known systems, i.e., systems without uncertainties. Using the existing nonlinear H∞ filtering results for known systems, we solve the robust nonlinear H∞ filtering problem in terms of Hamilton-Jacobi inequalities