Multi-sensor distributed fusion filtering for networked systems with different delay and loss rates

This paper mainly focuses on the multi-sensor distributed fusion estimation problem for networked systems with time delays and packet losses. Measurements of individual sensors are transmitted to local processors over different communication channels with different random delay and packet loss rates. Several groups of Bernoulli distributed random variables are employed to depict the phenomena of different time delays and packet losses. Based on received measurements of individual sensors, local processors produce local estimates that have been developed in a new recent literature. Then local estimates are transmitted to the fusion center over a perfect connection, where a distributed fusion filter is obtained by using the well-known matrix-weighted fusion estimation algorithm in the linear minimum variance sense. The filtering error cross-covariance matrices between any two local filters are derived. The steady-state property of the proposed distributed fusion filter is analyzed. A simulation example verifies the effectiveness of the algorithm.     

Global output feedback regulation of nonlinear time delay systems subject to sensor measurement faults

This article studied the global output feedback regulation problem for a class of uncertain nonlinear time delay systems subject to unknown measurement faults on sensors. Different from the existing works, we consider the unknown time‐varying delays on the system states and relax their conservative condition on nonlinear functions. By introducing two novel time‐varying gains, a new global output feedback regulation algorithm is proposed, which ensures control parameters can be chosen flexibly. The proposed linear‐like controller is independent of the unknown time‐varying delays. Moreover, it has a simple structure, which is convenient for the implementation in practice. Based on the Lyapunov stability theory, it is strictly proved that all signals of the resulting closed‐loop system are globally bounded with the designed controller. Finally, a simulation example is presented to illustrate the effectiveness of the proposed output feedback regulation algorithm.     

具有一步随机时滞和多丢包的网络系统H∞滤波器设计

在网络系统中由于连接传感器和滤波器的网络带宽有限,系统测量数据在传输中会出现随机时延甚至丢失. 本文讨论了具有一步随机时延和丢包的网络系统的H∞滤波器设计问题.基于新近提出的同时描述随机时延和丢包的模型,利用线性矩阵不等式方法设计线性滤波器,使得滤波误差系统均方指数稳定,并具有给定的H∞性能. 滤波器参数通过求解一个线性矩阵不等式得到.仿真研究说明了所提出算法的有效性.     

大时滞网络自适应预测PI主动队列管理算法

针对网络中存在的大时滞和网络参数时变问题，提出一种自适应预测PI主动队列管理算法．将Smith预估器与达林算法相结合，既克服了大时滞带来的不利影响，也减少了控制器参数整定数量．利用网络参数与控制参数所具有的确定关系，通过在线估计网络参数来实时调节控制参数，使得控制器能够适应网络参数的变化，同时采用线性化方法分析了系统局部稳定性．仿真结果表明，所提出的算法是可行而有效的。     

Short‐Time Linear Quadratic Form Technique for Estimating Fast‐Varying Parameters in Feedback Loops

The precision of a closed‐loop controller system designed for an uncertain plant depends strongly upon the maximum extent to which it is possible to track the trend of time‐varying parameters of the plant. The aim of this study is to describe a new parameter estimation algorithm that is able to follow fast‐varying parameters in closed‐loop systems. The short‐time linear quadratic form (STLQF) estimation algorithm introduced in this paper is a technique for tracking time‐varying parameters based on short‐time analysis of the regressing variables in order to minimize locally a linear quadratic form cost function. The established cost function produces a linear combination of errors with several delays. To meet this objective, mathematical development of the STLQF estimation algorithm is described. To implement the STLQF algorithm, the algorithm is applied to a planar mobile robot with fast‐varying parameters of inertia and viscous and coulomb frictions. Next, performance of the proposed algorithm is assessed against noise effects and variation in the type of parameters.     

OPTIMAL AND ROBUST SLIDING MODE CONTROL FOR LINEAR SYSTEMS WITH MULTIPLE TIME DELAYS IN CONTROL INPUT

This paper presents the optimal regulator for a linear system with multiple time delays in control input and a quadratic criterion. The optimal regulator equations are obtained using the duality principle, which is applied to the optimal filter for linear systems with multiple time delays in observations. Performance of the obtained optimal regulator is verified in the illustrative example against the best linear regulator available for linear systems without delays. Simulation graphs and comparison tables demonstrating better performance of the obtained optimal regulator are included. The paper then presents a robustification algorithm for the obtained optimal regulator based on integral sliding mode compensation of disturbances. The general principles of the integral sliding mode compensator design are modified to yield the basic control algorithm oriented to time‐delay systems, which is then applied to robustify the optimal regulator. As a result, the sliding mode compensating control leading to suppression of the disturbances from the initial time moment is designed. The obtained robust control algorithm is verified by simulations in the illustrative example.     

A nonparametric learning approach to range sensing from omnidirectional vision

We present a novel approach to estimating depth from single omnidirectional camera images by learning the relationship between visual features and range measurements available during a training phase. Our model not only yields the most likely distance to obstacles in all directions, but also the predictive uncertainties for these estimates. This information can be utilized by a mobile robot to build an occupancy grid map of the environment or to avoid obstacles during exploration—tasks that typically require dedicated proximity sensors such as laser range finders or sonars. We show in this paper how an omnidirectional camera can be used as an alternative to such range sensors. As the learning engine, we apply Gaussian processes, a nonparametric approach to function regression, as well as a recently developed extension for dealing with input-dependent noise. In practical experiments carried out in different indoor environments with a mobile robot equipped with an omnidirectional camera system, we demonstrate that our system is able to estimate range with an accuracy comparable to that of dedicated sensors based on sonar or infrared light.     

3-D Unitary ESPRIT: Accurate attitude estimation for unmanned aerial vehicles with a hexagon-shaped ESPAR array

Accurate estimation of the attitude of unmanned aerial vehicles (UAVs) is crucial for their control and displacement. Errors in the attitude estimate may misuse the limited battery energy of UAVs or even cause an accident. For attitude estimation, proprioceptive sensors such as inertial measurement units (IMUs) are widely applied, but they are susceptible to inertial guidance error. With antenna arrays currently being installed in UAVs for communication with ground base stations, we can take advantage of the array structure in order to improve the estimates of IMUs via data fusion. In this paper, we therefore propose an attitude estimation system based on a hexagon-shaped 7-element electronically steerable parasitic antenna radiator (ESPAR) array. The ESPAR array is well-suited for installment in the UAVs with broad wings and short bodies. Our proposed solution returns an estimation for the pitch and roll based on the inter-element phase delay estimates of the line-of-sight path of the impinging signal over the antenna array. By exploiting the parallel and centrosymmetric structure in the hexagon-shaped ESPAR array, the 3-dimensional Unitary ESPRIT algorithm is applied for phase delay estimation to achieve high accuracy as well as computational efficiency. We devise an attitude estimation algorithm by exploiting the geometrical relationship between the UAV attitude and the estimated phase delays. An analytical closed-form expression of the attitude estimates is obtained by solving the established simultaneous nonlinear equations. Simulations results show the feasibility of our proposed solution for different signal-to-noise ratio levels as well as multipath scenarios.     

Robust partially mode delay dependent ℋ ∞ control of discrete-time networked control systems

This article proposes a methodology for designing a partially mode delay dependent ?_∞ controller design for discrete-time systems with random communication delays. Communication delays between sensors and controller are modelled by a finite state Markov chain where the transition probability matrix is partially known. Stability criteria are obtained based on Lyapunov–Krasovskii functional and a novel methodology for designing a partially mode delay dependent state feedback controller has been proposed. The controller is obtained by solving linear matrix inequality optimisation problems using cone complimentarity linearisation algorithm. A numerical example is provided to illustrate the effectiveness of the proposed controller.     

Covariance-based fusion filtering for networked systems with random transmission delays and non-consecutive losses

The distributed and centralized fusion filtering problems for multi-sensor networked systems with transmission random one-step delays and non-consecutive packet losses are addressed. The signal evolution model is not required, as only covariance information is used. The measurements of individual sensors, subject to uncertainties modeled by random matrices and correlated noises, are transmitted to local processors through different communication channels and, due to random transmission failures, some of the data packets may be delayed or even definitely lost. The random transmission delays and non-consecutive packet losses are modeled by sequences of Bernoulli variables with different probabilities. By an innovation approach, local least squares linear filtering estimators are obtained by recursive algorithms; the distributed fusion framework is then used to obtain the optimal matrix-weighted combination of the local filters, using the mean squared error as optimality criterion. Also, a recursive least squares linear estimation algorithm is designed within the centralized fusion framework.     

广义系统信息融合稳态与自校正满阶Kalman滤波器

基于线性最小方差标量加权融合算法和射影理论,对带多个传感器和带相关噪声的广义系统,提出了分布式标量加权融合稳态满阶Kalman滤波器.推得了任两个传感器子系统之间的稳态满阶滤波误差互协方差阵,其解可任选初值离线迭代计算.所提出的稳态融合滤波器避免了每时刻计算协方差阵和融合权重,减小了在线计算负担.当系统含有未知模型参数时,基于递推增广最小二乘算法和标量加权融合算法,提出了一种两段融合自校正状态滤波器.其中第1段融合获得未知参数的融合估计;第2段融合获得分布式自校正融合状态滤波器.与局部估计和加权平均融合估计相比,所提出的标量加权融合参数估计和自校正状态估计都具有更高的精度.仿真研究验证了其有效性.     

Modelling and optimal controller design of networked control systems with multiple delays

In this paper we discuss the modelling and control of networked control systems (NCS) where sensors, actuators and controllers are distributed and interconnected by a common communication network. Multiple distributed communication delays as well as multiple inputs and multiple outputs (MIMO) are considered in the modelling algorithm. In addition, the asynchronous sampling mechanisms of distributed sensors are characterized to obtain the actual time delays between sensors and the controller. Due to the characteristics of a network architecture, piecewise constant plant inputs are assumed and discrete-time models of plant and controller dynamics are adopted to analyse the stability and performance of a closed-loop NCS. The analysis result is used to verify the stability and performance of an NCS without considering the impact of multiple time delays in the controller design. In addition, the proposed NCS model is used as a foundation for optimal controller design. The proposed control algorithm utilizes the information of delayed signals and improves the control performance of a control system encountering distributed communication delays. Several simulation studies are provided to verify the control performance of the proposed controller design.     

Event-triggered controller design of nonlinear discrete-time networked control systems in T-S fuzzy model

This article is concerned with event-triggered fuzzy control design for a class of discrete-time nonlinear networked control systems (NCSs) with time-varying communication delays. Firstly, a more general mixed event-triggering scheme (ETS) is proposed. Secondly, considering the effects of the ETS and communication delays, based on the T-S fuzzy model scheme and time delay system approach, the original nonlinear NCSs is reformulated as a new event-triggered networked T-S fuzzy systems with interval time-varying delays. Sufficient conditions for uniform ultimately bound (UUB) stability are established in terms of linear matrix inequalities (LMIs). In particular, the quantitative relation between the boundness of the stability region and the triggering parameters are studied in detail. Thirdly, a relative ETS is also provided, which can be seen as a special case of the above proposed mixed ETS. As a difference from the preceding results, sufficient conditions on the existence of desired fuzzy controller are derived to ensure the asymptotic stability of the closed-loop system with reduced communication frequency between sensors and controllers. Moreover, a co-design algorithm for simultaneously determining the gain matrices of the fuzzy controller and the triggering parameters is developed. Finally, two illustrative examples are presented to demonstrate the advantage of the proposed ETS and the effectiveness of the controller design method.     

Suboptimal control of linear stochastic multivariable systems with unknown parameters

An algorithm is proposed for suboptimal control of linear multivariable systems with unknown parameters and output noise covariances. This algorithm is based on the idea of explicitly separating the functions of identification, estimation and control. The parameters and states of the system are estimated in a bootstrap manner by the stochastic approximation method. A suboptimal controller is then obtained which utilizes a deterministic control gain derived using the estimates obtained from the parameter and state estimators. This suboptimal controller will approach the optimal strategy when the estimated system parameters approach their true values.     

An Efficient on‐Line Parameter Identification Algorithm for Nonlinear Servomechanisms with an Algebraic Technique for State Estimation

This paper presents a methodology for on‐line closed‐loop identification of a class of nonlinear servomechanisms. First, a system is defined with the same structure as the actual servomechanism, but using time‐varying estimated parameters. No coupling between the actual and the estimation systems is present. Position, velocity and acceleration errors, defined as the difference of the actual respective signals and the signals coming from the estimation system, are required in the identification method. Then, a recursive algorithm for on‐line identification of the system parameters is derived from a cost function depending on a linear combination of all the estimation errors. Velocity and acceleration estimates, required in the proposed parameter identification algorithm, are obtained by using an algebraic methodology. The identification algorithm is compared by means of real‐time experiments with an on‐line least squares algorithm with forgetting factor and an off‐line least squares algorithm with data preprocessing. Experimental results show that the proposed approach has a performance comparable to that obtained with the off‐line least squares algorithm, but with the advantage of avoiding any preprocessing.     

Review of: “Glaciers-Ocean-Atmosphere Interactions”. Edited by V. M. KOTLYAKOV,A. USHAKOV and A. GLAZOVSKY,Proceedings of the International Symposium,St Petersburg, 24-29 September 1990 (Wallingford,UK: IAHS Press, 1991.) [Pp. 549.] Price US$60.

Data gathered during the NASA sponsored Multisensor Aircraft Campaign Hydrology (MACHYDRO) experiment in central Pennsylvania (U.S.A.) in July, 1990 have been analysed to study the combined use of active and passive microwave sensors for estimating soil moisture from vegetated areas. These data sets were obtained during an eleven-day period with NASA's Airborne Synthetic Aperture Radar (AIRSAR), and Push-Broom Microwave Radiometer (PBMR) over an instrumented watershed, which included agricultural fields with a number of different crop covers. Simultaneous ground truth measurements were also made in order to characterize the state of vegetation and soil moisture under a variety of meteorological conditions. Various multi-sensor techniques are currently under investigation to improve the accuracy of remote sensing estimates of the soil moisture in the presence of vegetation and surface roughness conditions using these data sets. One such algorithm involving combination of active and passive microwave sensors is presented here, and is applied to representative corn fields in the Mahantango watershed that was the focus of study during the MACHYDRO experiment. In this algorithm, a simple emission model is inverted to obtain Fresnel reflectivity in terms of ground and vegetation parameters. Since Fresnel reflectivity depends on soil dielectric constant, soil moisture is determined from reflectivity using dielectric-soil moisture relations. The algorithm requires brightness temperature, vegetation and ground parameters as the input parameters. The former is measured by a passive microwave technique and the later two are estimated by using active microwave techniques. The soil moisture estimates obtained by this combined use of active and passive microwave remote sensing techniques, show an excellent agreement with the in situ soil moisture measurements made during the MACHYDRO experiment.     

Three-dimensional localization of multiple acoustic sources in shallow ocean with non-Gaussian noise

In this paper, a low-complexity algorithm SAGE-USL is presented for 3-dimensional (3-D) localization of multiple acoustic sources in a shallow ocean with non-Gaussian ambient noise, using a vertical and a horizontal linear array of sensors. In the proposed method, noise is modeled as a Gaussian mixture. Initial estimates of the unknown parameters (source coordinates, signal waveforms and noise parameters) are obtained by known/conventional methods, and a generalized expectation maximization algorithm is used to update the initial estimates iteratively. Simulation results indicate that convergence is reached in a small number of (≤10) iterations. Initialization requires one 2-D search and one 1-D search, and the iterative updates require a sequence of 1-D searches. Therefore the computational complexity of the SAGE-USL algorithm is lower than that of conventional techniques such as 3-D MUSIC by several orders of magnitude. We also derive the Cramér–Rao Bound (CRB) for 3-D localization of multiple sources in a range-independent ocean. Simulation results are presented to show that the root-mean-square localization errors of SAGE-USL are close to the corresponding CRBs and significantly lower than those of 3-D MUSIC.     

Estimating parameters of forest patch transition models from gap models

An algorithm to estimate the parameter values of a transition forest landscape model (MOSAIC) from a gap model (FACET) is presented here. MOSAIC is semi-Markov; it includes random distributed holding times and fixed or deterministic delays in addition to transition probabilities. FACET is a terrain-sensitive version of ZELIG, a spatially explicit gap model. For each topographic class, the input to the algorithm consists of gap model tracer files identifying the cover type of each plot through time. These cover types or states are defined a priori. The method, based on individual plots of the FACET model, requires one FACET run initialized from the “gap” cover type and follows the time history of each plot. The algorithm estimates the transition probability by counting the number of transitions between each pair of states and estimates the fixed lags and the parameters of the probability density functions of the distributed delays by recording the times at which these transitions are made. These density functions are assumed to be Erlang; its two parameters, order and rate, are estimated using a nonlinear least squares procedure. Thus, as output, the algorithm produces four matrices at each terrain class: transition probabilities, fixed delays, and the two parameters for the Erlang distributions. The algorithm is illustrated by its application to two sites, high and low elevation, from the H.J. Andrews Forest in the Oregon Cascades. This scaling-up method helps to bridge the conceptual breach between landscape- and stand-scale models. To reflect landscape heterogeneity, the algorithm can be executed repetitively for many different terrain classes. While the method developed here focuses on FACET and MOSAIC, this general approach could be extended to use other fine-scale models or other forms of meta-models.     

随机时延网络化不确定系统的鲁棒H∞滤波

研究了一类具有随机时延的网络化不确定系统的 H∞ 滤波器设计问题. 这类时延的产生是由于传感器和滤波器通过有限带宽的网络连接而引起的系统测量数据的滞后, 而且它是随机发生的. 本文采用满足 Bernoulli 分布随机变量来描述测量数据的这种随机时延. 利用线性矩阵不等式, 给出了全阶和降阶的滤波器存在的充分条件. 所设计的滤波器使得滤波误差系统是均方指数稳定且具有给定的H∞ 性能. 数值仿真表明设计方法的有效性.     

On smoothing in linear discrete systems with time delays†

An algorithm is presented for optimal linear fixed-point and fixed-lag smoothing in non-stationary linear discrete systems with multiple time delays. Relations derived previously in the problem of filtering are used directly to obtain the fixed-point and fixed-lag smoothing filters. The smoothing error covariance matrix is obtained via a recursive matrix equation. For the particular case of systems without delay terms, the algorithm defines new smoothing procedures.