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.     

Delay-dependent robust stability of uncertain networked control systems with multiple state time-delays

In this paper, delay-dependent robust stability for a class of uncertain networked control systems （NCSs） with multiple state time-delays is investigated. Modeling of multi-input and multi-output （MIMO） NCSs with networkinduced delays and uncertainties through new methods are proposed. Some new stability criteria in terms of LMIs are derived by using Lyapunov stability theory combined with linear matrix inequalities （LMIs） techniques. We analyze the delay-dependent asymptotic stability and obtain maximum allowable delay bound （MADB） for the NCSs with the proposed methods. Compared with the reported results, the proposed results obtain a much less conservative MADB which are more general. Numerical example and simulation is used to illustrate the effectiveness of the proposed methods.     

Decentralized control and state estimation of linear time-periodic systems

The main contributions of this article are the design of a decentralized controller and state estimator for linear time-periodic systems with fixed network topologies. The proposed method to tackle both problems consists of reformulating the linear periodic dynamics as a linear time-invariant system by applying a time-lifting technique and designing a discrete-time decentralized controller and state estimator for the time-lifted formulation. The problem of designing the decentralized estimator is formulated as a discrete-time Kalman filter subject to sparsity constraints on the gains. Two different algorithms for the computation of steady-state observer gains are tested and compared. The control problem is posed as a state feedback gain optimization problem over an infinite-horizon quadratic cost, subject to a sparsity constraint on the gains. An equivalent formulation that consists in the optimization of the steady-state solution of a matrix difference equation is presented and an algorithm for the computation of the decentralized gain is detailed. Simulation results for the practical cases of the quadruple-tank process and an extended 40-tank process are presented that illustrate the performance of the proposed solutions, complemented with numerical simulations using the Monte Carlo method.     

An event-triggered approach to state estimation with multiple point- and set-valued measurements

In this work, we consider state estimation based on the information from multiple sensors that provide their measurement updates according to separate event-triggering conditions. An optimal sensor fusion problem based on the hybrid measurement information (namely, point- and set-valued measurements) is formulated and explored. We show that under a commonly-accepted Gaussian assumption, the optimal estimator depends on the conditional mean and covariance of the measurement innovations, which applies to general event-triggering schemes. For the case that each channel of the sensors has its own event-triggering condition, closed-form representations are derived for the optimal estimate and the corresponding error covariance matrix, and it is proved that the exploration of the set-valued information provided by the event-triggering sets guarantees the improvement of estimation performance. The effectiveness of the proposed event-based estimator is demonstrated by extensive Monte Carlo simulation experiments for different categories of systems and comparative simulation with the classical Kalman filter.     

Moving horizon estimation for networked systems with multiple packet dropouts

The moving horizon estimation (MHE) problem is investigated in this paper for a class of networked systems with multiple packet dropouts. The packet dropout process is modeled as a Bernoulli random process and the networked system is described as a stochastic parameter system model. By choosing a stochastic cost function, a novel solution strategy is presented for MHE optimization problem with multiple packet dropouts. By considering noise constraints, the LOQO algorithm is used to solve the constrained MHE problem. Moreover, the convergence properties of the estimator are studied. Finally, two examples are given to demonstrate the effectiveness of the proposed method and the practical advantages of the MHE method.     

Decentralized robust output and estimated state feedback controls for large-scale uncertain systems

This paper considers the design of decentralized robust output controls. The system under consideration is composed of interconnected subsystems with time-varying uncertainties. The decentralized control is based on the information of the local output. No communication is allowed among subsystems. The proposed control strategy is a combination of direct and indirect decentralized controls. The salient feature of the controls is that they require only information about the possible bounds of uncertainties.     

Decentralized robust control of mechanical systems

For mechanical systems described by Euler-Lagrange equations and involving high-order interconnections, this paper proposes a decentralized control, which guarantees the uniform ultimate stability when the control objective is tracking a smooth desired trajectory. The controller design is based on some physical properties of such systems and Lyapunov design methodology. The application of the proposed controller to robot manipulators is presented, and experiment results are included to illustrate the performance of the proposed control algorithm     

Event-triggered robust distributed state estimation for sensor networks with state-dependent noises

This paper is concerned with the event-triggered distributed state estimation problem for a class of uncertain stochastic systems with state-dependent noises and randomly occurring uncertainties over sensor networks. An event-triggered communication scheme is proposed in order to determine whether the measurements on each sensor should be transmitted to the estimators or not. The norm-bounded uncertainty enters into the system in a random way. Through available output measurements from not only the individual sensor but also its neighbouring sensors, a sufficient condition is established for the desired distributed estimator to ensure that the estimation error dynamics are exponentially mean-square stable. These conditions are characterized in terms of the feasibility of a set of linear matrix inequalities, and then the explicit expression is given for the distributed estimator gains. Finally, a simulation example is provided to show the effectiveness of the proposed event-triggered distributed state estimation scheme.     

Event‐triggered robust state estimation for wireless sensor networks

Robust state estimation problem subject to a communication constraint is investigated in this paper for a class of wireless sensor networks constituted by multiple remote sensor nodes and a fusion node. An analytical robust fusion estimator using local event‐triggered transmission strategies is derived aiming to reduce energy consumption of the sensor nodes and refrain from network traffic congestion. Some conditions are presented guaranteeing the uniformly bounded estimation errors of the robust state estimator. Several numerical simulations are presented to show the validity of the proposed method.     

Security distributed state estimation for nonlinear networked systems against DoS attacks

This paper is concerned with security distributed state estimation for nonlinear networked systems against denial‐of‐service attacks. By taking the effects of resource constraints into consideration, an event‐triggered scheme and a quantization mechanism are employed to alleviate the burden of network. A mathematical model of distributed state estimation is constructed for nonlinear networked systems against denial‐of‐service attacks. Sufficient conditions ensuring the exponential stability of the estimation error systems are obtained by utilizing the Lyapunov stability theory. The explicit expressions of the designed state estimators are acquired in terms of the linear matrix inequalities. Finally, a numerical example is used to testify the feasibility of the proposed method.     

Decentralized estimation of regression coefficients in sensor networks

Consider a wireless sensor network with a fusion center deployed to estimate a common non-random parameter vector. Each sensor obtains a noisy observation vector of the non-random parameter vector according to a linear regression model. The observation noise is correlated across the sensors. Due to power, bandwidth and complexity limitations, each sensor linearly compresses its data. The compressed data from the sensors are transmitted to the fusion center, which linearly estimates the non-random parameter vector. The goal is to design the compression matrices at the sensors and the linear unbiased estimator at the fusion center such that the total variance of the estimation error is minimized. In this paper, we provide necessary and sufficient conditions for achieving the performance of the centralized best linear unbiased estimator. We also provide the optimal compression matrices and the optimal linear unbiased estimator when these conditions are satisfied. When these conditions are not satisfied, we propose a sub-optimal algorithm to determine the compression matrices and the linear unbiased estimator. Simulation results are provided to illustrate the effectiveness of the proposed algorithm.     

Joint state and parameter robust estimation of stochastic nonlinear systems

Successful implementation of many control strategies is mainly based on accurate knowledge of the system and its parameters. Besides the stochastic nature of the systems, nonlinearity is one more feature that may be found in almost all physical systems. The application of extended Kalman filter for the joint state and parameter estimation of stochastic nonlinear systems is well known and widely spread. It is a known fact that in measurements, there are inconsistent observations with the largest part of population of observations (outliers). The presence of outliers can significantly reduce the efficiency of linear estimation algorithms derived on the assumptions that observations have Gaussian distributions. Hence, synthesis of robust algorithms is very important. Because of increased practical value in robust filtering as well as the rate of convergence, the modified extended Masreliez–Martin filter presents the natural frame for realization of the joint state and parameter estimator of nonlinear stochastic systems. The strong consistency is proved using the methodology of an associated ODE system. The behaviour of the new approach to joint estimation of states and unknown parameters of nonlinear systems in the case when measurements have non‐Gaussian distributions is illustrated by intensive simulations. Copyright © 2015 John Wiley & Sons, Ltd.     

Decentralized event-triggered control strategy in distributed networked systems with delays

Event-triggered control strategies have been recently proposed as alternatives to traditional time-triggered periodic sampling for feedback control systems. This paper studies the event-triggered control problem for distributed networked control systems (NCS). In order to reduce the network traffic and the resource of computation, we propose a new decentralized event-triggered scheme for the NCS, where a subsystem broadcast its state information to its neighbors only when the subsystem’s local state error exceeds a threshold with on-line adaption. The asymptotic stability of the resulting closed-loop system is guaranteed by the event-triggered feedback scheme. In addition, the paper gives the maximal allowable positive bounds on transmission delays for the networked control systems. Finally, the results are illustrated to be efficient by one simulated example.     

Variance‐constrained state estimation for networked multi‐rate systems with measurement quantization and probabilistic sensor failures

This paper is concerned with the variance‐constrained state estimation problem for a class of networked multi‐rate systems (NMSs) with network‐induced probabilistic sensor failures and measurement quantization. The stochastic characteristics of the sensor failures are governed by mutually independent random variables over the interval [0,1]. By applying the lifting technique, an augmented system model is established to facilitate the state estimation of the underlying NMSs. With the aid of the stochastic analysis approach, sufficient conditions are derived under which the exponential mean‐square stability of the augmented system is guaranteed, the prescribed H_∞ performance constraint is achieved, and the individual variance constraint on the steady‐state estimation error is satisfied. Based on the derived conditions, the addressed variance‐constrained state estimation problem of NMSs is recast as a convex optimization one that can be solved via the semi‐definite program method. Furthermore, the explicit expression of the desired estimator gains is obtained by means of the feasibility of certain matrix inequalities. Two additional optimization problems are considered with respect to the H_∞ performance index and the weighted error variances. Finally, a simulation example is utilized to illustrate the effectiveness of the proposed state estimation method. Copyright © 2016 John Wiley & Sons, Ltd.     

Decentralized robust stabilization for large-scale feedforward non-linear systems

In this paper, we consider the decentralized robust stabilization problem for large-scale feedforward non-linear systems, which possess an upper-triangular structure, while in the decentralized control area the commonly studied large-scale feedback non-linear systems possess a lower-triangular structure. Our decentralized robust stabilizer for each subsystem takes a nested saturation feedback, with the saturation levels satisfying a set of equations and inequalities. Global asymptotic stability of the closed-loop large-scale system has been proved, and a simulation example is given.     

Decentralized control of networked discrete event systems with communication delays

In many practical systems, supervisory control is not performed by one centralized supervisor, but by multiple local supervisors. When communication networks are used in such a system as the medium of information transmission, the communication channels between local supervisors and the system to be controlled will unavoidably result in communication delays. This paper investigates how to use these local supervisors to control the system in order to satisfy given specifications even under communication delays. The specifications are described by two languages: a minimal required language which specifies the minimal required performance that the supervised system must have and a maximal admissible language which specifies the maximal boundary that the supervised system must be in. The results show that if the control problem is solvable, then there exists the minimal control policy which can be calculated based on state estimates. Furthermore, we derive algorithms to check whether the control problem is solvable or not.     

A service gateway for networked sensor systems

An emerging area in ubiquitous computing is networked sensor systems. The typical approach is to connect sensor-actuator devices using classic network infrastructures at a low level. These networks can serve as infrastructures to dynamically integrate, sensors and actuators into complex interactive systems while providing convenient services and interfaces to users. A prominent scenario for ubiquitous computing and ad hoc networking is an in-house environment using smart sensor system. Shaman, an extendable Java-based service gateway for networked sensor systems, integrates small network-attached sensor-actuator modules (SAMs) into heterogeneous, high-level networking communities. The system unburdens its connected SAMs by transferring functionality from the SAMs to the gateway.     

Nonlinear state estimation with robust convergence

The problem of designing a dynamic measurement processor for the on-line estimation of the state of a multi-input multi-output nonlinear plant is addressed. The plant can be either observable or detectable, encompassing a broad range of cases in process systems engineering. On the basis of the structure of a suitable property of robust nonlinear estimability, an estimator is built and its convergence studied, yielding sufficient conditions for robust convergence, a systematic construction, and a tractable gain tuning procedure. The estimability property has a verifiable test, and the execution of the tuning procedure, as well as the interpretation of its convergence features can be achieved within a conventional control framework for linear single-input controllers or single-output filters. The estimation of a continuous polymer reactor is considered as an application example.     

隐蔽式攻击下网络化控制系统状态与故障的联合区间估计

研究隐蔽式攻击下网络化控制系统状态与执行器故障的联合区间估计问题.首先,根据隐蔽式攻击信号的特性,得到隐蔽式攻击信号的上下界信息;然后,将执行器故障视为增广状态,构造与原系统等价的增广系统,基于所得到的增广系统和隐蔽式攻击信号的上下界信息,利用$L_{\infty     

Decentralized adaptive robust control for a class of large-scale systems including delayed state perturbations in the interconnections

The problem of decentralized stabilization is considered for a class of large-scale time-varying systems with delayed state perturbations in the interconnections. In this note, the upper bounds of the uncertainties in the interconnections are assumed to be unknown. The adaptation laws are proposed to estimate such unknown bounds, and by making use of their updated values, a class of decentralized memoryless state feedback controllers is constructed. Based on Lyapunov stability theory and Lyapunov-Krasovskii functional, it is shown that the solutions to the resulting adaptive closed-loop large-scale time-delay system can be guaranteed to be uniformly ultimately bounded. Finally, a numerical example is given to demonstrate the validity of the results.