Multi-sensor multi-rate fusion estimation for networked systems: Advances and perspectives

In industrial systems, the multi-rate sampling strategy has been widely used due to the advantage in balancing cost and performance as well as the psychical characteristics of the hardware. Accordingly, the analysis and synthesis problems of the multi-rate systems (MRSs) have received considerable research attentions owing to the significant engineering background. Among others, the state estimation problem, aims to estimate the system state based on the contaminated measurement signals, is one of the most important topics in the area of signal processing. In the past decades, plenty of research results have been obtained on the state estimation problems for MRSs. The intent of this survey is to provide a timely and systematic review with respect to the available state estimation algorithms for networked MRSs and the corresponding fusion methods. First, a general state–space model of the MRSs is given and the methods that transform the MRSs into single-rate ones are introduced. Then, the recent advances on the state estimation as well as fusion estimation problems for MRSs are discussed based on the performance indices used. Finally, some future research topics are given in the MRS state estimation problems.     

有界丢包网络环境下不确定系统的预测控制

研究了有界丢包网络环境下的多包不确定系统的鲁棒预测控制．首先在构建无限时域性能代价函数时,不同于传统预测控制方法,只考虑成功数据传输序列,并由此提出了两种鲁棒预测控制方法：将无限时域控制作用参数化为一个状态反馈控制律;或参数化为一个自由控制作用接一个状态反馈控制律．与传统方法一样,采用性能代价函数作为Ｌｙａｐｕｎｏｖ函数证明了系统的闭环稳定性．仿真实例验证了此方法的有效性．     

Robust fault detection for networked systems with communication delay and data missing

In this paper, the robust fault detection problem is investigated for a class of discrete-time networked systems with unknown input and multiple state delays. A novel measurement model is utilized to represent both the random measurement delays and the stochastic data missing phenomenon, which typically result from the limited capacity of the communication networks. The network status is assumed to vary in a Markovian fashion and its transition probability matrix is uncertain but resides in a known convex set of a polytopic type. The main purpose of this paper is to design a robust fault detection filter such that, for all unknown inputs, possible parameter uncertainties and incomplete measurements, the error between the residual signal and the fault signal is made as small as possible. By casting the addressed robust fault detection problem into an auxiliary robust H_∞ filtering problem of a certain Markovian jumping system, a sufficient condition for the existence of the desired robust fault detection filter is established in terms of linear matrix inequalities. A numerical example is provided to illustrate the effectiveness and applicability of the proposed technique.     

Modeling and estimation for networked systems with multiple random transmission delays and packet losses

In networked systems, data packets are transmitted through networks from a sensor to a data processing center. Due to the unreliability of communication channels, a packet may be delayed even lost during the transmission. At each moment, the data processing center may receive one or multiple data packets or nothing at all. A novel model is developed to describe the possible multiple random transmission delays and data packet losses by employing a group of Bernoulli distributed random variables. It is transformed to a measurement model with multiple random delayed states and noises. Based on the model, an optimal linear filter in the linear minimum variance sense is proposed by using the orthogonal projection approach which is a universal tool to find the optimal linear estimate. It does not have a steady-state performance since it depends on the values of random variables that depict the phenomena of delays and losses at each moment. So it needs to be computed online. To reduce the online computational cost, a suboptimal linear filter dependent on the probabilities of random variables is also proposed. However, it is worth noting that it is linearly optimal among all the linear filters dependent on the probabilities. It can be computed offline since it has the steady-state performance. A sufficient condition of existence for the steady-state performance is given. A simulation example shows the effectiveness.     

Optimal full-Order filtering for discrete-time systems with random measurement delays and multiple packet dropouts

This paper is concerned with the estimation problem for discrete-time stochastic linear systems with possible single unit delay and multiple packet dropouts. Based on a proposed uncertain model in data transmission, an optimal full-order filter for the state of the system is presented, which is shown to be of the form of employing the received outputs at the current and last time instants. The solution to the optimal filter is given in terms of a Riccati difference equation governed by two binary random variables. The optimal filter is reduced to the standard Kalman filter when there are no random delays and packet dropouts. The steady-state filter is also investigated. A sufficient condition for the existence of the steady-state filter is given. The asymptotic stability of the optimal filter is analyzed.     

多传感器集中式增量卡尔曼滤波融合算法

在单个传感器的状态估计系统中,标准的增量卡尔曼滤波方法可以有效消除量测系统误差。对于多传感器情况,标准算法失效。针对该问题,提出了多传感器集中式增量卡尔曼滤波融合算法,即:增量卡尔曼滤波的扩维融合算法和增量卡尔曼滤波的序贯融合算法。在标准增量卡尔曼滤波算法的基础上,结合扩维融合和序贯融合的思想来实现多传感器数据的融合。实验结果表明,当存在量测系统误差时,提出的集中式融合算法与传统的集中式融合算法相比,提高了滤波精度,并且能够成功地消除量测系统误差。     

Fault tolerant control for networked control systems with packet loss and time delay

In this paper, a fault tolerant control with the consideration of actuator fault for a networked control system (NCS) with packet loss is addressed. The NCS with data packet loss can be described as a switched system model. Packet loss dependent Lyapunov function is used and a fault tolerant controller is proposed respectively for arbitrary packet loss process and Markovian packet loss process. Considering a controlled plant with external energy-bounded disturbance, a robust H _∞ fault tolerant controller is designed for the NCS. These results are also expanded to the NCS with packet loss and networked-induced delay. Numerical examples are given to illustrate the effectiveness of the proposed design method.     

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.     

Optimal filtering for networked systems with Markovian communication delays

This paper is concerned with optimal filter problems for networked systems with random transmission delays, while the delay process is modeled as a multi-state Markov chain. By defining a delay-free observation sequence, the optimal filter problems are transformed into ones of the Markov jumping parameter system. We first present an optimal Kalman filter, which is with time-varying, path-dependent filter gains, and the number of the paths grows exponentially in time delay. Thus an alternative optimal Markov jump linear filter is presented, in which the filter gains just depend on the present value of the Markov chain. Further, an optimal filter with constant-gains is developed, the existence condition for the stabilizing solutions to the filter is given, and it can be shown that the proposed Markov jump linear filter converges to the constant-gain filter under appropriate assumptions.     

具有时延和数据包丢失的动车组网络控制系统建模方法研究

提出了一种具有时延和数据包丢失的动车组网络控制系统(NCSs)建模方法。假设网络时延小于采样周期,数据包传输成功率是固定的。动车组网络控制系统可以被描述为一个事件驱动的异步动态系统,使用动态输出反馈控制方法,讨论了闭环网络控制系统的稳定性。     

Tradeoffs between quantization and packet loss in networked control of linear systems

In this paper, we consider to derive the coarsest memoryless quantizer which can stabilize a single-input discrete-time linear time-invariant system with stochastic packet loss in the sense of stochastic quadratic stability. We show that the upper bound of the coarseness is strictly given by the packet loss probability and the unstable poles of the plants. We furthermore deal with permissible dead-zone width around the origin of the quantizers and time-varying finite quantizers in order to realize control using finite quantization steps.     

具有长时延及丢包的网络控制系统H∞ 鲁棒滤波

研究了同时具有大于一个采样周期的随机传输时延及数据包丢失的网络控制系统H∞滤波问题.对于给定的丢包率,滤波误差系统被建模为具有两个事件速率约束的异步动态系统,利用异步动态系统理论给出滤波误差系统满足指数稳定和H∞性能指标的充分条件.H∞滤波器可通过解一组线性矩阵不等式求出.仿真结果验证了所提出方法的有效性.     

Wave-variable framework for networked robotic systems with time delays and packet losses

This paper investigates the problem of networked control system for nonlinear robotic manipulators under time delays and packet loss by using passivity technique. With the utilisation of wave variables and a passive remote controller, the networked robotic system is demonstrated to be stable with guaranteed position regulation. For the input/output signals of robotic systems, a discretisation block is exploited to convert continuous-time signals to discrete-time signals, and vice versa. Subsequently, we propose a packet management, called wave-variable modulation, to cope with the proposed networked robotic system under time delays and packet losses. Numerical examples and experimental results are presented to demonstrate the performance of the proposed wave-variable-based networked robotic systems.     

具有随机通讯时延的网络化系统的l2-l∞滤波

研究具有随机通讯时延的离散网络化系统的l2-l∞滤波器设计问题.采用满足Bernoulli分布随机变量描述测量数据的一步随机时延;利用线性矩阵不等式方法设计线性滤波器,使得滤波误差系统在均方意义下指数稳定,并保证对于所有有界的外界扰动信号,滤波误差系统具有一定的l2-l∞扰动衰减水平.数值仿真表明了所提出设计方法的有效性.     

具有长时延及丢包的网络控制系统稳定性分析

研究了同时具有大于一个采样周期的随机传输时延及数据包丢失的网络控制系统的稳定性问题．对于给定的数据包丢失率,网络控制系统被建模为具有两个事件速率约束的异步动态系统,利用异步动态系统理论给出了网络控制系统指数稳定的充分条件,状态反馈控制器可通过解一组矩阵不等式求出．仿真示例验证了所提出方法的有效性．     

Guaranteed cost control of distributed parameter networked control systems with time‐varying delay

Distributed parameter networked control systems mean distributed parameter systems are controlled through a network, where the control loops are closed. In this paper, the problem of guaranteed cost and state feedback controller design is investigated for a class of distributed parameter networked control systems. With the network factors, such as transmission delays, data packet dropouts considered, the distributed parameter networked control system is modeled as a linear closed‐loop system with time‐varying delay and uncertain parameters. By selecting an appropriate Lyapunov‐Krasovskii function and using linear matrix inequality (LMI) approach, the controller is designed to render the system stable and it can keep the cost function less than a certain upper value. In addition, numerical simulation is included to demonstrate the theoretical results.     

Compensation for control packet dropout in networked control systems

With the integration of communication networks and distributed control in modern manufacturing and process industries, networked control systems (NCSs) are becoming increasingly important due to its simplicity, scalability, flexibility, and cost effectiveness. However, there are still significant technical barriers that limit the applications of NCS technologies. Two challenges are network-induced time delay and data packet dropout. Applying a real-time queuing protocol that we developed recently, we are able to limit the sum of the network-induced communication delay and the control computation delay within a control period. This one-period delay is further guaranteed by well designed compensation for control packet dropout. Then, this paper proposes to compensate for the control packet dropout at the actuator using past control signals. Three model-free strategies for control packet dropout compensation, namely, PD (proportional plus derivative), PD2 (proportional plus up to the second-order derivative), and PD3 (proportional plus up to the third-order derivative) are developed. They are suitable for a large number of NCSs without the need to tune the compensator parameters. The proposed dropout compensation schemes are demonstrated through numerical examples.     

多传感器时滞系统信息融合最优Kalman滤波器

基于线性最小方差最优加权融合估计算法,对多传感器的离散线性状态时滞随机系统,给出了一种非增广分布式加权融合最优Kalman滤波器.推导了状态时滞系统任两个传感器子系统之间的滤波误差互协方差阵的计算公式.它与状态增广加权融合滤波器具有相同的精度.与每个传感器的局部滤波器相比,分布式融合滤波器具有更高的精度.与状态和观测增广最优滤波器相比,具有较小的精度.但避免了增广所带来的高维计算和大的空间存储,可减小计算负担.仿真例子验证了其有效性.     

Distributed fusion estimation for multi-sensor asynchronous sampling systems with correlated noises

This paper is concerned with the distributed fusion estimation problem for a class of multi-sensor asynchronous sampling systems with correlated noises. The state updates uniformly and the sensors sample randomly. Based on the measurement augmentation method, the asynchronous sampling system is transformed to the synchronous sampling one. Local filter is designed by using an innovation analysis approach. Then, the filtering error cross-covariance matrix between any two local filters is derived. Finally, the optimal distributed fusion filter is proposed by using matrix-weighted fusion algorithm in the linear minimum variance sense. Simulation results show the effectiveness of the proposed algorithms.     

非线性离散系统的相关观测融合时变Kalman滤波

针对带相关观测噪声和带不同观测函数的多传感器离散非线性系统,利用推广的离散Kalman滤波方法对状态系统和观测系统进行线性化处理,提出了基于岭估计的加权最小二乘(REWLS)分布式融合Kalman滤波算法.以风险函数为评价指标,利用信息滤波器比较了各种观测融合Kalman滤波算法,其中REWLS分布式融合算法精度最高.同时,分布式融合算法减少了计算负担,便于实时应用.仿真例子表明了理论分析的正确性.