Recent advances on recursive filtering and sliding mode design for networked nonlinear stochastic systems: A survey

Copyright © 2013 Jun Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Some recent advances on the recursive filtering and sliding mode design problems for nonlinear stochastic systems with network-induced phenomena are surveyed. The network-induced phenomena under consideration mainly include missing measurements, fading measurements, signal quantization, probabilistic sensor delays, sensor saturations, randomly occurring nonlinearities, and randomly occurring uncertainties. With respect to these network-induced phenomena, the developments on filtering and sliding mode design problems are systematically reviewed. In particular, concerning the network-induced phenomena, some recent results on the recursive filtering for time-varying nonlinear stochastic systems and sliding mode design for time-invariant nonlinear stochastic systems are given, respectively. Finally, conclusions are proposed and some potential future research works are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grant nos. 61134009, 61329301, 61333012, 61374127 and 11301118, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant no. GR/S27658/01, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

Recent advances on filtering and control for nonlinear stochastic complex systems with incomplete information: A survey

This Article is provided by the Brunel Open Access Publishing Fund - Copyright @ 2012 Hindawi PublishingSome recent advances on the filtering and control problems for nonlinear stochastic complex systems with incomplete information are surveyed. The incomplete information under consideration mainly includes missing measurements, randomly varying sensor delays, signal quantization, sensor saturations, and signal sampling. With such incomplete information, the developments on various filtering and control issues are reviewed in great detail. In particular, the addressed nonlinear stochastic complex systems are so comprehensive that they include conventional nonlinear stochastic systems, different kinds of complex networks, and a large class of sensor networks. The corresponding filtering and control technologies for such nonlinear stochastic complex systems are then discussed. Subsequently, some latest results on the filtering and control problems for the complex systems with incomplete information are given. Finally, conclusions are drawn and several possible future research directions are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grant nos. 61134009, 61104125, 61028008, 61174136, 60974030, and 61074129, the Qing Lan Project of Jiangsu Province of China, the Project sponsored by SRF for ROCS of SEM of China, the Engineering and Physical Sciences Research Council EPSRC of the UK under Grant GR/S27658/01, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

Discrete Time Systems

Discrete-Time Systems comprehend an important and broad research field. The consolidation of digital-based computational means in the present, pushes a technological tool into the field with a tremendous impact in areas like Control, Signal Processing, Communications, System Modelling and related Applications. This book attempts to give a scope in the wide area of Discrete-Time Systems. Their contents are grouped conveniently in sections according to significant areas, namely Filtering, Fixed and Adaptive Control Systems, Stability Problems and Miscellaneous Applications. We think that the contribution of the book enlarges the field of the Discrete-Time Systems with signification in the present state-of-the-art. Despite the vertiginous advance in the field, we also believe that the topics described here allow us also to look through some main tendencies in the next years in the research area

Bibliographic Review on Distributed Kalman Filtering

In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area

Design methods for networked control systems with unreliable channels focusing on packet dropouts

Texto completo descargado en TeseoLos sistemas de control a través de redes se han convertido en un área importante dentro de la comunidad de control, lo cual es debido a su bajo coste y a la flexibilidad de sus aplicaciones. Los sistemas de control a través de redes (NCSs) se componen de sensores, actuadores y controladores; las operaciones entre ellos se coordinan a través de una red de comunicación. Típicamente, estos sistemas están espacialmente distribuidos, y pueden funcionar de manera asíncrona, pero sus operaciones han de estar coordinadas para conseguir los objetivos deseados. En este resumen se presenta una perspectiva general de los NCSs, y en particular, los casos específicos en los que se ha basado esta tesis, abordando los temas principales relacionados con NCS, con todos los problemas y ventajas asociados, se describen en este resumen. Por último, se presenta un índice de la tesis con sus contribuciones más relevantes. - Introducción a los Sistemas de Control a través de Red Los Sistemas de Control a través de Red (NCSs) son sistemas espacialmente distribuidos donde la comunicación entre plantas, sensores, actuadores y controladores se realiza a través de una red de comunicación. La complejidad en el diseño y la realización, el coste del cableado, la instalación y el mantenimiento pueden ser reducidos drásticamente incluyendo una red de comunicación. Sin embargo, las redes de comunicación en los sistemas también traen algunos incovenientes como los retrasos y la pérdida de datos, los errores de codificación, etc. Estos incovenientes pueden ser la causa de la de la degradación del comportamiento del sistema e incluso causar su desestabilización. Hoy en día, hay un gran número de situaciones prácticas en las que el uso de redes de comunicación para el control son necesarias para aplicaciones o procesos de control en ingeniería. Algunos ejemplos son: Situaciones en las que el espacio y el peso están limitados. Situaciones en las que las distancias a considerar son grandes. Aplicaciones de control donde el cableado no es posible. El uso de redes de comunicación digitales proporciona también algunas ventajas: La complejidad en el cableado en conexiones punto a punto se reduce mucho, así como el coste. Además, los costes de instalación pueden reducirse también drásticamente. La reducción en la complejidad del cableado hace mucho más fácil el diagnóstico y el mantenimiento del sistema, dando lugar a un ahorro en el coste debido a que la instalación y el funcionamiento tienen una eficiencia mayor. Los NCSs son flexibles y reconfigurables. Fiabilidad, redundancia y robustez ante los fallos. Los NCSs proporcionan modularidad, control descentralizado y diagnósticos integrados. Todas estas ventajas sugieren que los NCSs jugarán un papel principal en un futuro cercano, siendo un área de investigación muy prometedora. - Objetivos de la tesis La idea general de esta tesis es proponer algunas soluciones novedosas a diferentes problemas relacionados con NCSs. Todos los problemas considerados son típicos dentro del marco del control a través de redes, considerándose principalmente el de las pérdidas de paquetes en la transmisión de datos. Dentro del contexto de sistemas con pérdida de paquetes, se han estudiado diferentes problemas. Para obtener soluciones diferentes para este tipo de sistemas, se han considerado los siguientes objetivos: Diseño de controladores. Controladores Hinf, que consigan la robustificación de sistemas con incertidumbres. Controladores MPC, combinados con estrategias de buffer. Diseño de filtros. Filtros Hinf para sistemas con incertidumbres, usando técnicas frecuenciales y cadenas de Markov. Diseño de algoritmos. Localización dinámica de un control distribuido en una red formada por una estructura matricial de nodos. Localización dinámica del estimador de la salida del sistema, en una red formada por una estructura lineal de nodos. Estimación distribuida cooperativa. Basada en observadores locales de Luenberger. - Conclusiones Uno de los objetivos de esta tesis ha sido el análisis de la estabilidad y comportamiento de sistemas bajo control. En algunos casos, el diseño se ha realizado imponiendo restricciones en cuanto a la estabilidad. La robustificación de sistemas, en particular la de aquellos con incertidumbres, ha sido también tenida en cuenta. Las técnicas de control Hinf se han usado en los casos de análisis y diseño de sistemas de control. Otro objetivo importante de esta tesis ha sido el diseño de algoritmos para una red dinámica, la cual está compuesta por cierta estructura de nodos. El algoritmo es capaz de decidir qué nodo será el controlador o el estimador de la salida del sistema en la red. La estabilidad y el comportamiento del sistema de control ha sido analizado. También se ha abordado el diseño de estimación y esquemas distribuidos. Se han considerado redes que introducen retrasos temporales, junto con pérdidas aleatorias. La reducción en el consumo de energía ha sido un objetivo importante en esta parte de la tesis. En este caso, se ha examinado una política de comunicación entre agentes basada en eventos, la cual da lugar a un compromiso entre el comportamiento del sistema y los ahorros en la comunicación

Discrete- and Continuous-Time Probabilistic Models and Algorithms for Inferring Neuronal UP and DOWN States

UP and DOWN states, the periodic fluctuations between increased and decreased spiking activity of a neuronal population, are a fundamental feature of cortical circuits. Understanding UP-DOWN state dynamics is important for understanding how these circuits represent and transmit information in the brain. To date, limited work has been done on characterizing the stochastic properties of UP-DOWN state dynamics. We present a set of Markov and semi-Markov discrete- and continuous-time probability models for estimating UP and DOWN states from multiunit neural spiking activity. We model multiunit neural spiking activity as a stochastic point process, modulated by the hidden (UP and DOWN) states and the ensemble spiking history. We estimate jointly the hidden states and the model parameters by maximum likelihood using an expectation-maximization (EM) algorithm and a Monte Carlo EM algorithm that uses reversible-jump Markov chain Monte Carlo sampling in the E-step. We apply our models and algorithms in the analysis of both simulated multiunit spiking activity and actual multi- unit spiking activity recorded from primary somatosensory cortex in a behaving rat during slow-wave sleep. Our approach provides a statistical characterization of UP-DOWN state dynamics that can serve as a basis for verifying and refining mechanistic descriptions of this process.National Institutes of Health (U.S.) (Grant R01-DA015644)National Institutes of Health (U.S.) (Director Pioneer Award DP1- OD003646)National Institutes of Health (U.S.) (NIH/NHLBI grant R01-HL084502)National Institutes of Health (U.S.) (NIH institutional NRSA grant T32 HL07901

Control Design and Filtering for Wireless Networked Systems

This dissertation is concerned with estimation and control over wireless networked systems. Several problems are addressed, including estimator design over packet loss links, control and estimation over cognitive radio systems, modeling and prediction of wireless sensor networks (WSNs), and localization with the Theater Positioning System (TPS). The first problem addressed is the state estimation of a discrete-time system through a packet loss link modeled by a Bernoulli random variable. The optimal filter is derived by employing exact hybrid filtering. The performance of the optimal filter is illustrated by numerical simulations. Next, we consider the problem of estimation and control over cognitive radio (CR) systems. A two-switch model is first used to model this link. The linear optimal estimator and controller are derived over a single CR link. Also discussed here is estimation and control of the closed-loop system over two CR links. Furthermore, a more practical semi-Markov model for the CR system is proposed. Two cases are considered, where one assumes that acknowledgement of the information arrival is not available while the other assumes it is available. In the former, a suboptimal estimator is proposed and, in the latter, sufficient conditions are derived for the stability of a peak covariance process. Then, a controller design for the semi-Markov model is developed using linear matrix inequalities (LMIs). Additionally, the third problem addressed is modeling, identification, and prediction of the link quality of WSNs, such as the packet reception rate (PRR) and received signal strength indicator (RSSI). The state-space model is applied for this purpose. The prediction error minimization method (PEM) is employed for estimating parameters in the proposed model. The method employed is demonstrated through real measurements sampled by wireless motes. The last problem analyzed is localization using a new navigation system, TPS. In this study, we focus on users\u27 position estimation with the TPS when a GPS signal is not available. Several models are proposed to model transmission delays utilizing previous GPS signals. Last, a navigation scheme is provided for the TPS to improve its localization accuracy when the GPS signal is unavailable

Stochastic Processes with Applications

Stochastic processes have wide relevance in mathematics both for theoretical aspects and for their numerous real-world applications in various domains. They represent a very active research field which is attracting the growing interest of scientists from a range of disciplines.This Special Issue aims to present a collection of current contributions concerning various topics related to stochastic processes and their applications. In particular, the focus here is on applications of stochastic processes as models of dynamic phenomena in research areas certain to be of interest, such as economics, statistical physics, queuing theory, biology, theoretical neurobiology, and reliability theory. Various contributions dealing with theoretical issues on stochastic processes are also included

Sequential Monte Carlo Samplers For Nonparametric Bayesian Mixture Models

Tez (Doktora) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2012Thesis (PhD) -- İstanbul Technical University, Institute of Science and Technology, 2012Bu çalışmanın temel amacı, parametrik olmayan Bayesçi model seçim teknikleri içinde önemli bir yere sahip olan Dirichlet süreci karışım modelleri (DPM) için etkin ardışık Monte Carlo (SMC) örnekleyiciler tasarlamaktır. Tasarlanan algoritmalar, önerilen sınıf güncelleme metotları sayesinde, yeni gelen gözlemlerin ışığında parçacık gezingelerinde değişiklik yaparak gerçek DPM sonsal dağılımına daha iyi bir yaklaşıklık sağlamaktadır. Önerilen metot, DPM sonsal dağılımının çözümünde kullanılan diğer ardışık Monte Carlo örnekleyicileri genelleme özelliğe sahiptir. Tek ve çok boyutlu olasılık dağılımı kestirim problemlerinde yapılan değerlendirmelerde, özellikle sonsal dağılımın izole modlara sahip olduğu koşullarda, önerilen metodun klasik metotlara göre çok daha yüksek doğrulukta sonuca yakınsayabildiği görülmüştür. Ayrıca, manevralı hedeflerin takibinde ortaya atılan en yenilikçi modellerden biri olan değişken oranlı parçacık süzgeçleri (VRPF) tezde ele alınmış ve çoklu model yaklaşımları değişken oranlı modeller ile birleştirilerek, takip başarımını arttıran çoklu model değişken oranlı parçacık süzgeçleri (MM-VRPF) önerilmiştir. Çoklu model yaklaşımının manevralı hedef gezingelerini daha iyi modellediği benzetim sonuçları ile gösterilmiştir.In this thesis, we developed a novel online algorithm for posterior inference in Dirichlet Process Mixture (DPM) models that is based on the sequential Monte Carlo (SMC) samplers framework. The proposed method enables us to design new clustering update schemes, such as updating past trajectories of the particles in light of recent observations, and still ensures convergence to the true DPM posterior distribution asymptotically. Our method generalizes many sequential importance sampling based approaches and provides a computationally efficient improvement to particle filtering that is less prone to getting trapped in isolated modes. Performance has been evaluated in univariate and multivariate infinite Gaussian mixture density estimation problems. It is shown that the proposed algorithm outperforms conventional Monte Carlo approaches in terms of estimation variance and average log-marginal. Moreover, in this thesis we dealt with the maneuvering target tracking problem. We incorporated multiple model approach with the recently introduced variable rate particle filters (VRPF) in order to improve the tracking performance. The proposed variable rate model structure, referred as Multiple Model Variable Rate Particle Filter (MM-VRPF) results in a much more accurate tracking.DoktoraPh