Weight Try-Once-Discard Protocol-Based L_2 L_infinity State Estimation for Markovian Jumping Neural Networks with Partially Known Transition Probabilities

It was the L_2 L_infinity performance index that for the first time is initiated into the discussion on state estimation of delayed MJNNs with with partially known transition probabilities, which provides a more general promotion for the estimation error.The WTOD protocol is adopted to dispatch the sensor nodes so as to effectively alleviate the updating frequency of output signals. The hybrid effects of the time delays, Markov chain, and protocol parameters are apparently reflected in the co-designed estimator which can be solved by a combination of comprehensive matrix inequalities

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

Communication-protocol-based analysis and synthesis of networked systems: progress, prospects and challenges

In recent years, the communication-protocol-based synthesis and analysis issues have gained substantial research interest owing mainly to their significance in networked systems. In this work, we survey the control and filtering problems of networked systems under the effects induced by communication protocols. First, we introduce the engineering background of networked systems as well as the theoretical frameworks established to deal with the communication-protocol-based analysis and synthesis problems. Then, recent advances (especially the latest results) are reviewed on the stability analysis issue subject to protocol scheduling. Subsequently, the particular effort is devoted to presenting the latest progress on various communication-protocol-based control and filtering problems according to the characteristics of networked systems (e.g. time-varying nature, random behaviours, types of parameter uncertainties, and kinds of distributed structure). After that, we provide a systematic review of the communication-protocol-based fault diagnosis problems. Finally, some research challenges of communication-protocol-based control and filtering problems are outlined for future research

Fault estimation for time-varying systems with Round-Robin protocol

summary:This paper is concerned with the design problem of finite-horizon $H_\infty$ fault estimator for a class of nonlinear time-varying systems with Round-Robin protocol scheduling. The faults are assumed to occur in a random way governed by a Bernoulli distributed white sequence. The communication between the sensor nodes and fault estimators is implemented via a shared network. In order to prevent the data from collisions, a Round-Robin protocol is utilized to orchestrate the transmission of sensor nodes. By means of the stochastic analysis technique and the completing squares method, a necessary and sufficient condition is established for the existence of fault estimator ensuring that the estimation error dynamics satisfies the prescribed $H_\infty$ constraint. The time-varying parameters of fault estimator are obtained by recursively solving a set of coupled backward Riccati difference equations. A simulation example is given to demonstrate the effectiveness of the proposed design scheme of the fault estimator

On hybrid consensus-based extended Kalman filtering with random link failures over sensor networks

summary:This paper is concerned with the distributed filtering problem for nonlinear time-varying systems over wireless sensor networks under random link failures. To achieve consensus estimation, each sensor node is allowed to communicate with its neighboring nodes according to a prescribed communication topology. Firstly, a new hybrid consensus-based filtering algorithm under random link failures, which affect the information exchange between sensors and are modeled by a set of independent Bernoulli processes, is designed via redefining the interaction weights. Second, a novel observability condition, called parameterized jointly uniform observability, is proposed to ensure the stochastic boundedness of the error covariances of the hybrid consensus-based filtering algorithm. Finally, an example is given to demonstrate the effectiveness of the derived theoretical results

Distributed Computation and Optimization over Networks

This dissertation is devoted to the development of efficient, robust, and scalable distributed algorithms, which enable agents in a large-scale, multi-hop network to cooperatively compute a global quantity, or solve an optimization problem, with only local interactions and without any centralized coordination. Algorithms of this nature are attracting growing interest from a number of scientific communities due to their broad application, for example, to autonomous agent coordination and control in mobile ad hoc networks, distributed signal processing and data fusion in wireless sensor networks, and studies of opinion dynamics in social networks.In this dissertation, we address three fundamental problems in the area, namely: averaging, solving of positive definite linear equations, and unconstrained separable convex optimization. Based on a blend of tools and ideas from system, optimization, and graph theories, we construct a novel set of distributed algorithms---including continuous- and discrete-time, gossip and asynchronous---which solve these problems over undirected networks with arbitrary (and, in some cases, time-varying) topologies and agent memberships. We also analyze the properties of these algorithms, including their convergence rates and complexity characteristics, and compare them with existing schemes, showing analytically and numerically that our algorithms possess several appealing features.The major contributions of this dissertation are as follows: first, we show that Lyapunov stability theory may be used to shape the behavior of asynchronous distributed algorithms. This finding allows us to introduce the notion of greedy, decentralized, feedback iteration control, leading to a class of Controlled Hopwise algorithms, which are highly bandwidth/energy efficient in wireless networks. The finding also creates a new paradigm in the design of asynchronous distributed algorithms, where iterations are opportunistically controlled, as opposed to being randomized.Second, we show that the Bregman divergence of the Lagrangian of a separable convex optimization problem may be used to form a common Lyapunov function. This result enables us to derive a family of Zero-Gradient-Sum algorithms, which yield nonlinear networked dynamical systems on an invariant manifold, and which differ fundamentally from, and have pros and cons over, the existing subgradient algorithms. The derivation also shows that a gossip variant within the family generalizes the classic Pairwise Averaging, and the family itself is a natural generalization of several well-known algorithms for distributed consensus, to distributed convex optimization.Finally, we provide a series of analysis of the properties of our algorithms (e.g., boundedness, asymptotic and exponential convergence, lower and upper bounds on convergence rates, scalability) on various networks (e.g., path, cycle, regular, complete, and general graphs), describing explicitly the dependency of such properties on network topologies, problem characteristics, and algorithm parameters, including the algebraic connectivity, Laplacian spectral radius, and function curvatures

Stochastic Event-Based Control and Estimation

Digital controllers are traditionally implemented using periodic sampling, computation, and actuation events. As more control systems are implemented to share limited network and CPU bandwidth with other tasks, it is becoming increasingly attractive to use some form of event-based control instead, where precious events are used only when needed. Forms of event-based control have been used in practice for a very long time, but mostly in an ad-hoc way. Though optimal solutions to most event-based control problems are unknown, it should still be viable to compare performance between suggested approaches in a reasonable manner. This thesis investigates an event-based variation on the stochastic linear-quadratic (LQ) control problem, with a fixed cost per control event. The sporadic constraint of an enforced minimum inter-event time is introduced, yielding a mixed continuous-/discrete-time formulation. The quantitative trade-off between event rate and control performance is compared between periodic and sporadic control. Example problems for first-order plants are investigated, for a single control loop and for multiple loops closed over a shared medium. Path constraints are introduced to model and analyze higher-order event-based control systems. This component-based approach to stochastic hybrid systems allows to express continuous- and discrete-time dynamics, state and switching constraints, control laws, and stochastic disturbances in the same model. Sum-of-squares techniques are then used to find bounds on control objectives using convex semidefinite programming. The thesis also considers state estimation for discrete time linear stochastic systems from measurements with convex set uncertainty. The Bayesian observer is considered given log-concave process disturbances and measurement likelihoods. Strong log-concavity is introduced, and it is shown that the observer preserves log-concavity, and propagates strong log-concavity like inverse covariance in a Kalman filter. A recursive state estimator is developed for systems with both stochastic and set-bounded process and measurement noise terms. A time-varying linear filter gain is optimized using convex semidefinite programming and ellipsoidal over-approximation, given a relative weight on the two kinds of error