Distributed Fault-Tolerant Consensus Tracking Control of Multi-Agent Systems under Fixed and Switching Topologies

This paper proposes a novel distributed fault-tolerant consensus tracking control design for multi-agent systems with abrupt and incipient actuator faults under fixed and switching topologies. The fault and state information of each individual agent is estimated by merging unknown input observer in the decentralized fault estimation hierarchy. Then, two kinds of distributed fault-tolerant consensus tracking control schemes with average dwelling time technique are developed to guarantee the mean-square exponential consensus convergence of multi-agent systems, respectively, on the basis of the relative neighboring output information as well as the estimated information in fault estimation. Simulation results demonstrate the effectiveness of the proposed fault-tolerant consensus tracking control algorithm

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Robusni adaptivni observer temeljen na algoritmu za kooperaciju mobilnih robota s više kotača

Wheeled mobile robots (WMRs) are of great importance. Therefore, it is necessary to make sure that they are not defected. But, in case of failures, the diagnosis task is very important to predict then solve the problem. The most useful techniques in diagnosis are observers which are based on the observability of the monitored system that is not usually ensured by WMR. Thus, to overcome this drawback, an intelligent cooperative diagnosis algorithm is proposed and tested for a group of mobile robots. The diagnosis algorithm is based on robust adaptive unknown input observer applied on unobservable robot. The local non-observability of each robot is solved by cooperative communication. The idea consists on considering all WMRs as a Large Scale System (LSS) even these robots may have not common task. Then, the LSS is decomposed into subsystems that everyone refers to each robot communicating with its neighbors. Next, a design of cooperative interconnected systems is studied to reassure the new condition of observability. Besides, Fast Adaptive Fault Estimation (FAFE) algorithm is proposed to improve the performances of the fault estimation. Finally, to illustrate the efficiency of the proposed algorithm, a model of three-wheel omnidirectional mobile robot is presented.Mobilni roboti na kotačima od velike su važnosti. Stoga, nužno je osigurati da ne odlutaju. U slučaju kvara važna je dijagnoza kako bi se predvidio i onda riješio problem. Najkorisnije dijagnostičke tehnike su observeri koji se zasnivaju na osmotrivosti nadgledanih sustava koja kod mobilnih robota na kotačima najčešće nije osigurana. Stoga, kako bi se nadišao ovaj problem, koristi se inteligentan algoritam za kooperativnu dijagnozu i testira se na grupi mobilnih robota. Dijagnostički algoritam zasniva se na robusnom adaptivnom observeru s nepoznatim ulazom koji je primijenjen na neosmotrivom robotu. Lokalna neosmotrivost svakog robota riješena je koopreativnom komunikacijom. Ideja je da se svi mobilni roboti promatraju kao sustav velikih razmjera iako roboti nemaju isti zadatak. Sustav velikih razmjera se tada rastavlja na podsutave tako da se svaki odnosi na jednog robota koji komunicira sa svojim susjedima. Zatim se proučava dizajn kooperativnih povezanih sustava kako bi se osigurali uvjeti za osmotrivost. Dodatno, predlaže se korištenje brze adaptivne estimacije pogreške kako bi se poboljšala estimacije pogreške. Konačno, prikazan je model višesmjernog mobilnog robota na tri kotača kako bi se ilustrirala učinkovitost predloženog algoritma

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