Continuous-time decentralized smoothers based on two-filter form: identical local and global models

This paper proposes some decentralized smoothing algorithms for a continuous-time linear estimation structure consisting of a central processor and of two local processors, in which the local models are assumed to be identical to the global model. The philosophy of the paper is to solve the problem in terms of the local forward and backward information (or Kalman) filters. The resulting algorithms are somewhat different from those based on the local smoothing estimates which have been studied by some other authors. Smoothing update and real-time smoothing algorithms are also presented, ft is shown that the present algorithms have some advantages: the global filtered estimates can be obtained in the course of computing the decentralized smoothing estimates and the central and local processors can be derived in a completely parallel fashion     

Inventory model with two rates of production for deteriorating items with permissible delay in payments

Some decentralized smoothing problems are solved by applying a forward-pass fixed-interval smoother formula in discrete-time systems. It is assumed that a simple estimation structure consists of a global processor and of two local processors. Two cases are considered for the problems of decentralized smoothing and smoothing update: when the local backward-pass information filtered estimates are available, and when the local-smoothed estimates are available. Some features of present algorithms are discussed from the point of view of data transmissions and numerical computations, etc.     

分布网络中离散时间系统的平滑算法*

本文考虑具有一个中心处理器和两个局部处理器下的估计结构的平滑合成问题,讨论了当已知局部滤波估计和当已知局部平滑估计时两种情况下的分布平滑估计,并导出了合成算法。     

Decentralized smoothing algorithms of continuous-time systems in distributed-sensor networks

Some decentralized smoothing algorithms are derived by applying a Rauch-Tung-Striebel fixed-interval smoother formula in continuous-time systems supposing that an estimation structure is comprised of a global processor and of two local processors. Two cases are investigated for the problems of decentralized smoothing: when the local-filtered estimated are available and when the local-smoothed estimates are known. Some features of present algorithms are discussed from the point of view of data transmissions and communication bandwidth, etc.     

A Parallel Interval Computation Model for Global Optimization with Automatic Load Balancing

In this paper,we propose a decentralized parallel computation model for global optimization using interval analysis.The model is adaptive to any number of processors and the workload is automatically and evenly distributed among all processors by alternative message passing.The problems received by each processor are processed based on their local dominance properties,which avoids unnecessary interval evaluations.Further,the problem is treated as a whole at the beginning of computation so that no initial decomposition scheme is required.Numerical experiments indicate that the model works well and is stable with different number of parallel processors,distributes the load evenly among the processors,and provides an impressive speedup,especially when the problem is time-consuming to solve.     

Tracking and formation control of multiple autonomous agents: A two-level consensus approach

Simultaneous tracking and formation control is addressed for a team of autonomous agents that evolve dynamically in a space containing a measurable vector field. Each agent measures the local value of the field along its trajectory and occasionally shares relevant information with other agents, in order to estimate the spatial average obtained from averaging measurements across all agents. Using shared information, agents control their trajectories in a cooperative manner, with the dual goals of driving the average field measurement to a specified value and maintaining a desired formation about the average. Two approaches to virtual leader estimation are considered. The first involves the synthesis of a common virtual leader state, whereas the second involves decentralized estimation of the virtual leader by individual agents. Under the second approach, control is posed as a two-level consensus problem, where agents reach agreement on the virtual leader state at one level and reach formation about the virtual leader at the other level. The decentralized approach is effective even when communication among agents is limited, in the sense that the associated network graph can be disconnected in frozen time.     

A decentralized model for flow shop production with flexible transportation system

The recent advances in technology sectors often clash with traditional organizational paradigms which can limit or make difficult an efficient implementation in the real world. In this paper we show how it is possible to exploit the advantages of innovative technologies in manufacturing when these are supported by new and efficient methods for production management. More in details, we face a flow shop scheduling problem in a shoe manufacturing system in which overtaking of jobs is allowed thanks to an innovative transportation system. Overtaking means that a job can be put in waiting state and another job can surpass it, allowing the change of the scheduling sequence. Preemption is not allowed. The objective function of the problem is the minimization of the maximum lateness. We propose a decentralized model, based on multi-agent system theory, to represent the production cells of the plant and to include the potentiality offered by overtaking of jobs at decisional level. The adoption of a decentralized approach increases the system flexibility since each machine is able to solve its local scheduling problem. Adding or removing machines to the plant will not imply a change in the scheduling algorithms. The outcomes of this work are reached firstly through a formulation of the problem with three flow shop scheduling models, secondly through a comparison of the models with respect to different performance indicators. The results highlight as the decentralized approach is able to reach comparable performances with the centralized one for a relevant number of instances. Moreover sensitivity analysis shows as in the decentralized model the computational time required to solve bigger instances increases less quickly than in the case of centralized ones. Finally, simulations of the decentralized approach clarify as the correlation of the local solution procedure is effected by the number of machines of the flow shop and the coordination mechanism is effected by the number of the jobs to be scheduled.     

State estimation for stochastic time‐varying multisensor systems with multiplicative noises: Centralized and decentralized data fusion

In this paper, the state estimation problems, including filtering and one‐step prediction, are solved for uncertain stochastic time‐varying multisensor systems by using centralized and decentralized data fusion methods. Uncertainties are considered in all parts of the state space model as multiplicative noises. For the first time, both centralized and decentralized estimators are designed based on the regularized least‐squares method. To design the proposed centralized fusion estimator, observation equations are first rewritten as a stacked observation. Then, an optimal estimator is obtained from a regularized least‐squares problem. In addition, for decentralized data fusion, first, optimal local estimators are designed, and then fusion rule is achieved by solving a least‐squares problem. Two recursive equations are also obtained to compute the unknown covariance matrices of the filtering and prediction errors. Finally, a three‐sensor target‐tracking system is employed to demonstrate the effectiveness and performance of the proposed estimation approaches.     

Decentralized state estimation in large-scale systems

A novel approach to decentralized state estimation in a large-scale interconnected system is proposed. The method assumes a known model for the local subsystem only, and therefore is suitable when the other subsystem models and the interaction matrices are partially or totally unknown. An innovation representation suitable for decentralized subsystem state estimation is derived. The state estimation problem is then solved through the parametric identification of the innovation representation. The identification algorithm is based upon a pseudo-linear regression (PLR) principle that attempts minimization of the innovation variances.     

分布式在线交替方向乘子法

针对如何对分布式网络采集的数据进行在线学习的问题,提出了一种基于交替方向乘子法(ADMM)的分布式在线学习优化算法--分布式在线交替方向乘子法(DOM)。首先,针对分布式在线学习需要各节点根据新采集的数据来更新本地估计,同时保持网络中所有节点的估计趋于一致这一问题,建立了数学模型并设计DOM算法对其进行求解。其次,针对分布式在线学习问题定义了Regret 界,用以表征在线估计的性能;证明了当本地即时损失函数是凸函数时,DOM算法是收敛的,并给出了其收敛速度。最后,通过数值仿真实验结果表明,相比现有的分布式在线梯度下降法(DOGD)和分布式在线自主学习算法(DAOL),所提出的DOM算法具有更快的收敛性能。     

A learning-based synthesis approach todecentralized supervisory control of discrete event systems with unknown plants

In this paper, we consider the problem of automatic synthesis of decentralized supervisor for uncertain discrete event systems. In particular, we study the case when the uncontrolled plant is unknown a priori. To deal with the unknown plants, we first characterize the conormality of prefix-closed regular languages and propose formulas for computing the supremal conormal sublanguages; then sufficient conditions for the existence of decentralized supervisors are given in terms of language controllability and conormality and a learning-based algorithm to synthesize the supervisor automatically is proposed. Moreover, the paper also studies the on-line decentralized supervisory control of concurrent discrete event systems that are composed of multiple interacting unknown modules. We use the concept of modular controllability to characterize the necessary and sufficient conditions for the existence of the local supervisors, which consist of a set of local supervisor modules, one for each plant module and which determines its control actions based on the locally observed behaviors, and an on-line learning-based local synthesis algorithm is also presented. The correctness and convergence of the proposed algorithms are proved, and their implementation are illustrated through examples.     

离散事件系统的混合可靠分散监控

在研究基于混合信息的分散监控时,由于部分监控器与部分控制器发生失败,故提出了一种新型的混合可靠分散监控问题.通过修改局部可控事件集与不可控事件集,提出新的可控语言与可靠联合可观察语言定义,进而得到混合可靠分散监控器存在的充分必要条件就是整体约束语言是可靠联合可观察,可控闭的.之后,又通过研究整体约束与混合子约束之间的关系,给出了判别混合可靠分散监控器存在的一个充分条件,即混合子约束分别满足基于谓词的可观察,可控性与基于语言的联合可观察,可控封闭性.     

Decentralized Adaptive Filtering for Multi-agent Systems with Uncertain Couplings

In this paper, the problem of decentralized adaptive filtering for multi-agent systems with uncertain couplings is formulated and investigated. This problem is challenging due to the mutual dependency of state estimation and coupling estimation. First, the problem is divided into four typical types based on the origin of coupling relations and linearity of the agent dynamics. Then models of the four types are given and the corresponding decentralized adaptive filtering algorithms are designed for the purpose of estimation of the unknown states and couplings which denotes the relations between agents and their neighbor agents in terms of states or outputs simultaneously, with preliminary stability analysis and discussions. For testing the effects of algorithm, with the so-called certainty-equivalence principle, control signals are designed based on the results of state estimation and coupling estimation got by the proposed decentralized adaptive filtering algorithms. Extensive simulations are conducted to verify the effectiveness of considered algorithms.      

一类线性离散时滞大系统的分散镇定

用一组线性矩阵不等式给出一类线性离散时滞大系统分散能镇定的一个充分条件,进而,通过建立和求解一个凸优化问题,提出了具有较反馈增益参数的分散稳定化状态反馈控制律的设计方法,所得到的控制器不仅使得闭环境系统是稳定的,而且还可以使得闭环系统状态具有给定的衰减度。     

Multi‐sensor track‐to‐track fusion via linear minimum variance sense estimators

An integrated approach that consists of sensor‐based filtering algorithms, local processors, and a global processor is employed to describe the distributed fusion problem when several sensors execute surveillance over a certain area. For the sensor tracking systems, each filtering algorithm utilized in the reference Cartesian coordinate system is presented for target tracking, with the radar measuring range, bearing, and elevation angle in the spherical coordinate system (SCS). For the local processors, each track‐to‐track fusion algorithm is used to merge two tracks representing the same target. The number of 2‐combinations of a set with N distinct sensors is considered for central track fusion. For the global processor, the data fusion algorithms, simplified maximum likelihood (SML) estimator and covariance matching method (CMM), based on linear minimum variance (LMV) estimation fusion theory, are developed for use in a centralized track‐to‐track fusion situation. The resulting global fusers can be implemented in a parallel structure to facilitate estimation fusion calculation. Simulation results show that the proposed SML estimator has a more robust capability of improving tracking accuracy than the CMM and the LMV estimators. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Reliable decentralized supervisory control of discrete eventsystems

We consider a discrete event system controlled by a decentralized supervisor consisting of n local supervisors, and formulate a new decentralized supervisory control problem, called a reliable decentralized supervisory control problem. A decentralized supervisor is said to be k-reliable (1相似文献   

A partially distributed deadlock detection algorithm

In this paper, a partially distributed deadlock detection algorithm [PDDDA] with multiple outstanding requests is presented for use in distributed database systems. This algorithm allows a process to request many resources simultaneously and uses a central controller for detecting multisite deadlocks. The detection of local deadlocks and the maintenance of local deadlock information are performed at each of the local sites. This partially distributed algorithm alleviates the problem of congestion at the central controller in a centralized algorithm and needs fewer messages and smaller storage space than a fully decentralized algorithm. A set of criteria for comparing deadlock detection algorithms are also given and then used to compare PDDDA with a fully decentralized algorithm proposed by Isloor and Marsland.Research reported herein was supported by US Army CECOM, Ft. Monmouth, New Jersey, under Contract No. DAAB07-83-K-K542. The views, opinions, and/or findings contained in this paper are those of the authors and should not be construed as an official Deportment of the Army position, policy or decision.     

Supporting load distribution strategies in message-passing multiprocessors: a case study

This paper describes a software architecture designed as a support for tackling the load distribution problem when solving complex problems on concurrent processors. We have considered transputer-based MIMD multiprocessors as concurrent processors and a simulator for biologically inspired neural networks as a case study. Biologically inspired neural networks are characterized by having many thousands of neurons and synapses and topologically based connection schemes. It has been our main aim to give the user the possibility of simply defining and modifying widely differing load distribution strategies, in order to make it possible to deal with a broad range of neural network architectures and processor topologies. Furthermore we provide a real tool for hiding communication delays.     

Globally Optimal Distributed Kalman Fusion With Local Out-of-Sequence-Measurement Updates

In a distributed multisensor fusion systems, observations produced by sensors can arrive at local processors out of sequence. The resulting problem at the central processor/fusion center-how to update current estimate using multiple local out-of-sequence-measurement (OOSM) updates - is a nonstandard distributed estimation problem. In this note, based on the centralized update algorithm with multiple asynchronous (1-step-lag) OOSMs see we firstly deduce the optimal distributed fusion update algorithm with multiple local asynchronous (1-step-lag) OOSM updates, which is proved, under some regularity conditions, to be equivalent to the corresponding optimal centralized update algorithm with all-sensor 1-step-lag OOSMs. Then, we propose an optimal distributed fusion update algorithm with multiple local arbitrary-step-lag OOSM updates.     

Optimization of decentralized random field estimation networks under communication constraints through Monte Carlo methods

We propose a new methodology for designing decentralized random field estimation schemes that takes the tradeoff between the estimation accuracy and the cost of communications into account. We consider a sensor network in which nodes perform bandwidth limited two-way communications with other nodes located in a certain range. The in-network processing starts with each node measuring its local variable and sending messages to its immediate neighbors followed by evaluating its local estimation rule based on the received messages and measurements. Local rule design for this two-stage strategy can be cast as a constrained optimization problem with a Bayesian risk capturing the cost of transmissions and penalty for the estimation errors. A similar problem has been previously studied for decentralized detection. We adopt that framework for estimation, however, the corresponding optimization schemes involve integral operators that are impossible to evaluate exactly, in general. We employ an approximation framework using Monte Carlo methods and obtain an optimization procedure based on particle representations and approximate computations. The procedure operates in a message-passing fashion and generates results for any distributions if samples can be produced from, e.g., the marginals. We demonstrate graceful degradation of the estimation accuracy as communication becomes more costly.