5,340 research outputs found
Distributed filtering of networked dynamic systems with non-gaussian noises over sensor networks: A survey
summary:Sensor networks are regarded as a promising technology in the field of information perception and processing owing to the ease of deployment, cost-effectiveness, flexibility, as well as reliability. The information exchange among sensors inevitably suffers from various network-induced phenomena caused by the limited resource utilization and complex application scenarios, and thus is required to be governed by suitable resource-saving communication mechanisms. It is also noteworthy that noises in system dynamics and sensor measurements are ubiquitous and in general unknown but can be bounded, rather than follow specific Gaussian distributions as assumed in Kalman-type filtering. Particular attention of this paper is paid to a survey of recent advances in distributed filtering of networked dynamic systems with non-Gaussian noises over sensor networks. First, two types of widely employed structures of distributed filters are reviewed, the corresponding analysis is systematically addressed, and some interesting results are provided. The inherent purpose of adding consensus terms into the distributed filters is profoundly disclosed. Then, some representative models characterizing various network-induced phenomena are reviewed and their corresponding analytical strategies are exhibited in detail. Furthermore, recent results on distributed filtering with non-Gaussian noises are sorted out in accordance with different network-induced phenomena and system models. Another emphasis is laid on recent developments of distributed filtering with various communication scheduling, which are summarized based on the inherent characteristics of their dynamic behavior associated with mathematical models. Finally, the state-of-the-art of distributed filtering and challenging issues, ranging from scalability, security to applications, are raised to guide possible future research
Non-fragile estimation for discrete-time T-S fuzzy systems with event-triggered protocol
summary:This paper investigates the non-fragile state estimation problem for a class of discrete-time T-S fuzzy systems with time-delays and multiple missing measurements under event-triggered mechanism. First of all, the plant is subject to the time-varying delays and the stochastic disturbances. Next, a random white sequence, the element of which obeys a general probabilistic distribution defined on , is utilized to formulate the occurrence of the missing measurements. Also, an event generator function is employed to regulate the transmission of data to save the precious energy. Then, a non-fragile state estimator is constructed to reflect the randomly occurring gain variations in the implementing process. By means of the Lyapunov-Krasovskii functional, the desired sufficient conditions are obtained such that the Takagi-Sugeno (T-S) fuzzy estimation error system is exponentially ultimately bounded in the mean square. And then the upper bound is minimized via the robust optimization technique and the estimator gain matrices can be calculated. Finally, a simulation example is utilized to demonstrate the effectiveness of the state estimation scheme proposed in this paper
Robust model predictive control under redundant channel transmission with applications in networked DC motor systems
In networked systems, intermittent failures in data transmission are usually inevitable due to the limited bandwidth of the communication channel, and an effective countermeasure is to add redundance so as to improve the reliability of the communication service. This paper is concerned with the model predictive control (MPC) problem by using static output feedback for a class of polytopic uncertain systems with redundant channels under both input and output constraints. By utilizing the min-max control approach combined with stochastic analysis, sufficient conditions are established to guarantee the feasibility of the designed MPC scheme that ensures the robust stability of the closed-loop system. In terms of the solution to an auxiliary optimization problem, an easy-to-implement MPC algorithm is proposed to obtain the desired sub-optimal control sequence as well as the upper bound of the quadratic cost function. Finally, to illustrate its effectiveness, the proposed design method is applied to control a networked direct current motor system
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Simultaneous State and Unknown Input Estimation for Complex Networks With Redundant Channels Under Dynamic Event-Triggered Mechanisms
National Natural Science Foundation of China (Grant Number: 62003121, 61873082, 61873148 and 61933007); Zhejiang Provincial Natural Science Foundation of China (Grant Number: LQ20F030014);
Outstanding Youth Science Foundation of Heilongjiang Province of China (Grant Number: JC2018001);
Fundamental Research Foundation for Universities of Heilongjiang Province of China (Grant Number: 2019-KYYWF-0215); Royal Society of the U.K.; Alexander von Humboldt Foundation of Germany
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Multi-sensor multi-rate fusion estimation for networked systems: Advances and perspectives
National Natural Science Foundation of China under Grants 62103095, 61873058, 61873148 and 61933007; AHPU Youth Top-notch Talent Support Program of China under Grant 2018BJRC009; Natural Science Foundation of Anhui Province of China under Grant 2108085MA07; Royal Society of the UK; Alexander von Humboldt Foundation of Germany
Distributed Event-Triggered Nonlinear Fusion Estimation under Resource Constraints
This paper studies the event-triggered distributed fusion estimation problems
for a class of nonlinear networked multisensor fusion systems without noise
statistical characteristics. When considering the limited resource problems of
two kinds of communication channels (i.e., sensor-to-remote estimator channel
and smart sensor-to-fusion center channel), an event-triggered strategy and a
dimensionality reduction strategy are introduced in a unified networked
framework to lighten the communication burden. Then, two kinds of compensation
strategies in terms of a unified model are designed to restructure the
untransmitted information, and the local/fusion estimators are proposed based
on the compensation information. Furthermore, the linearization errors caused
by the Taylor expansion are modeled by the state-dependent matrices with
uncertain parameters when establishing estimation error systems, and then
different robust recursive optimization problems are constructed to determine
the estimator gains and the fusion criteria. Meanwhile, the stability
conditions are also proposed such that the square errors of the designed
nonlinear estimators are bounded. Finally, a vehicle localization system is
employed to demonstrate the effectiveness and advantages of the proposed
methods.Comment: 15 pages,9 figures. The first draft was completed in June 2021, and
this is the revised versio
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