Optimal location of process measurements

The problem of optimally locating a given number of Bensors for observing a general linear distributed parameter system is considered. Measurements at the sensors are assumed to be available continuously in time, and the design criterion is minimization of a scalar measure of the covariance of the estimate error in the optimal linear filter. Necessary conditions for optimality are derived based on the formulation of a distributed parameter matrix minimum principle. A computational algorithm is developed for determining the optimum set of measurement locations. The algorithm is applied to the problem of optimally locating temperature sensors in a solid undergoing transient heat conduction.     

带有色观测噪声系统多传感器标量加权最优信息融合稳态Kalman滤波器

基于标量加权多传感器线性最小方差最优信息融合准则,对被多传感器观测的带有色观测噪声的离散线性随机控制系统,提出了一种具有两层融合结构的标量加权信息融合稳态Kalman滤波器,它等价于相应的带相关噪声系统的最优信息融合稳态Kalman预报器.最优信息融合稳态预报器可在所有局部预报器达到稳态时,通过一次融合获得,且任两个子系统之间的稳态预报误差互协方差阵可通过任选初值迭代求得,并证明了它的收敛性.通过将它应用到带三个传感器的雷达跟踪系统验证了其有效性.     

基于MEMS/GPS的航向姿态测量系统设计

姿态信息是飞行控制中最关键的参数之一。针对飞行控制系统首要解决的姿态测量问题。本文利用多微机电系统（MEMS）传感器与GPS组合,研制了一种微型航向姿态测量系统。考虑在以往的姿态测量系统中,动加速度的影响限制其应用,通过设计利用GPS进行辅助修正的姿态解算算法,有效规避了运动加速度对测量精度的影响,使系统可同时满足静态情况和动态情况的使用。将试验结果与商用姿态测量产品MTI-G-700的姿态结果进行了比较,各项指标均达到系统设计的性能要求,验证了航向姿测量系统设计的有效性。     

Distributed optimal fusion steady-state Kalman filter for systems with coloured measurement noises

Based on the optimal fusion criterion weighted by matrices in the linear minimum variance sense, an optimal information fusion steady-state Kalman filter is given for the discrete time-invariant linear stochastic control system measured by multiple sensors with coloured measurement noises, which is equivalent to an optimal information fusion steady-state Kalman predictor with a two-layer fusion structure for system with correlated noises. Furthermore, the steady-state optimal fusion predictor can be obtained only by fusing once after all local subsystems enter the steady-state predictions. The solution of steady-state prediction error cross-covariance matrix between any two subsystems can be obtained by iteration with an arbitratry initial value, whose convergence is proved. Applying it to a tracking system with three sensors shows its effectiveness.     

Fault detection and isolation of the attitude control subsystem of spacecraft formation flying using extended Kalman filters

In this paper, the problem of fault detection and isolation (FDI) of the attitude control subsystem (ACS) of spacecraft formation flying systems is considered. For developing the FDI schemes, an extended Kalman filter (EKF) is utilised which belongs to a class of nonlinear state estimation methods. Three architectures, namely centralised, decentralised, and semi-decentralised, are considered and the corresponding FDI strategies are designed and constructed. Appropriate residual generation techniques and threshold selection criteria are proposed for these architectures. The capabilities of the proposed architectures for accomplishing the FDI tasks are studied through extensive numerical simulations for a team of four satellites in formation flight. Using a confusion matrix evaluation criterion, it is shown that the centralised architecture can achieve the most reliable results relative to the semi-decentralised and decentralised architectures at the expense of availability of a centralised processing module that requires the entire team information set. On the other hand, the semi-decentralised performance is close to the centralised scheme without relying on the availability of the entire team information set. Furthermore, the results confirm that the FDI results in formations with angular velocity measurement sensors achieve higher level of accuracy, true faulty, and precision, along with lower level of false healthy misclassification as compared to the formations that utilise attitude measurement sensors.     

Optimal LQG control across packet-dropping links

We examine two special cases of the problem of optimal linear quadratic Gaussian control of a system whose state is being measured by sensors that communicate with the controller over packet-dropping links. We pose the problem as an information transmission problem. Using a separation principle, we decompose the problem into a standard LQR state-feedback controller design, along with an optimal encoder–decoder design for propagating and using the information across the unreliable links. Our design is optimal among all causal algorithms for any arbitrary packet-drop pattern. Further, the solution is appealing from a practical point of view because it can be implemented as a small modification of an existing LQG control design.     

Optimal sensor scheduling for hidden Markov model state estimation

Consider the Hidden Markov model where the realization of a single Markov chain is observed by a number of noisy sensors. The sensor scheduling problem for the resulting hidden Markov model is as follows: design an optimal algorithm for selecting at each time instant, one of the many sensors to provide the next measurement. Each measurement has an associated measurement cost. The problem is to select an optimal measurement scheduling policy, so as to minimize a cost function of estimation errors and measurement costs. The problem of determining the optimal measurement policy is solved via stochastic dynamic programming. Numerical results are presented.     

基于LMP91000的电化学传感器调理电路设计

介绍了TI公司推出的新型电化学模拟前端芯片LMP91000,基于该芯片设计了新式化学传感器前端调理电路,并对电路进行了验证。采用该芯片可以大大简化电路设计,减少电路的复杂性,提高电路的可靠性。同时可编程的增益控制和温度补偿,能够实现电化学传感器的自动调零和智能校准,大大提高测量精度。芯片具有的I2C总线,可以实现传感器的阵列设计,实现多气体测量。     

通信受限下网络化多传感器系统的Kalman融合估计

研究了一类通信受限下网络化多传感器系统的 Kalman 融合估计问题, 其中通信受限 是指系统在一个采样周期内只允许有限个传感器与融合中心通信. 首先, 提出了一种周期性分组传输的通信策略, 并将每组传感器所对应的局部估计系统描述成一个离散周期子系统模型. 其次, 每个子系统根据最新测量信息的更新时刻, 选择相应的 Kalman 估计器 (滤波器或预报器), 从而得到各子系统在每一时刻的一个局部最优估计, 再通过矩阵加权线性最小方差最优融合准则得到最优融合估计,并给出了Kalman融合估计器的设计方法. 最后, 通过一个目标跟踪例子验证所提方法的有效性.     

Optimal activation policies for continuous scanning observations in parameter estimation of distributed systems

The problem of determining an optimal measurement scheduling for identification of unknown parameters in distributed systems described by partial differential equations is discussed. The discrete-scanning observations are performed by an optimal selection of measurement data from spatially fixed sensors. In the adopted approach, the sensor scheduling problem is converted to a constrained optimal control problem. In this framework, the control value represents the selected sensor configuration. Thus the control variable is constrained to take values in a discrete set and switchings between sensors may occur in continuous time. By applying the control parameterization enhancing transform technique, a computational procedure for solving the optimal scanning measurement problem is obtained. The numerical scheme is then tested on a computer example regarding an advection-diffusion problem.     

A game-theoretic approach to a finite-time disturbance attenuationproblem

A disturbance attenuation problem over a finite-time interval is considered by a game theoretic approach where the control, restricted to a function of the measurement history, plays against adversaries composed of the process and measurement disturbances, and the initial state. A zero-sum game, formulated as a quadratic cost criterion subject to linear time-varying dynamics and measurements, is solved by a calculus of variation technique. By first maximizing the quadratic cost criterion with respect to the process disturbance and initial state, a full information game between the control and the measurement residual subject to the estimator dynamics results. The resulting solution produces an n-dimensional compensator which expresses the controller as a linear combination of the measurement history. A disturbance attenuation problem is solved based on the results of the game problem. For time-invariant systems it is shown that under certain conditions the time-varying controller becomes time-invariant on the infinite-time interval. The resulting controller satisfies an H _∞ norm bound     

Multi-sensor optimal information fusion Kalman filter

This paper presents a new multi-sensor optimal information fusion criterion weighted by matrices in the linear minimum variance sense, it is equivalent to the maximum likelihood fusion criterion under the assumption of normal distribution. Based on this optimal fusion criterion, a general multi-sensor optimal information fusion decentralized Kalman filter with a two-layer fusion structure is given for discrete time linear stochastic control systems with multiple sensors and correlated noises. The first fusion layer has a netted parallel structure to determine the cross covariance between every pair of faultless sensors at each time step. The second fusion layer is the fusion center that determines the optimal fusion matrix weights and obtains the optimal fusion filter. Comparing it with the centralized filter, the result shows that the computational burden is reduced, and the precision of the fusion filter is lower than that of the centralized filter when all sensors are faultless, but the fusion filter has fault tolerance and robustness properties when some sensors are faulty. Further, the precision of the fusion filter is higher than that of each local filter. Applying it to a radar tracking system with three sensors demonstrates its effectiveness.     

Distributed fusion Kalman filtering under binary sensors

Binary sensors are special sensors that only transmit one‐bit information at each time and have been widely applied to environmental awareness and medical monitoring. This paper is concerned with the distributed fusion Kalman filtering problem for a class of binary sensor systems. A novel uncertainty approach is proposed to better extract valid information from binary sensors at switching instant. By minimizing a local estimation error covariance, the local robust Kalman estimates are firstly obtained. Then, the distributed fusion Kalman filter is designed by resorting to the covariance intersection fusion criterion. Finally, a blood oxygen content model is employed to show the effectiveness of the proposed methods.     

Optimum experimental designs for dynamic systems in the presence of correlated errors

The problem of determining an optimal measurement time schedule for identification of unknown parameters in multiresponse systems when correlations between observations occur is considered. The measurement process is performed by collecting data at discrete time instants from several outputs. An observation plan is proposed based on a scalar measure of the Fisher information matrix as the design criterion quantifying the accuracy of parameter estimators. A numerical procedure is proposed to determine approximations of optimum designs in the case of correlated measurement errors. The approach is illustrated with an example of the multi-output system of equations describing a chemical kinetic reaction.     

Control of quantized systems based on discrete event models

This paper presents controller design methods for dynamical systems that are observed by discrete sensors. These sensors induce a partitioning of the state space and only this quantized information is available for the controller. The so-called 'quantized system' is modelled by a discrete-event model that serves as a basis for the controller design methods. However, instead of using solely the classical control methodologies for discrete-event systems as found in the literature, improvements are proposed by including additional information provided by the fact that the underlying plant is continuous by nature, such as continuity of the state trajectories and information on derivatives that holds for parts of the state space. The concept of discretely controlled invariant sets will play a crucial role in the development of control strategies and necessary and sufficient conditions for controlled invariance are presented. Also algorithms are included to compute the smallest and largest discretely controlled invariant sets with respect to a given set. All the techniques and computations are explicitly described by Boolean matrices and vectors and are ready for application. This is demonstrated for a benchmark problem of a two tank system.     

Formation Control of Swarm Robots with Multiple Proximity Distance Sensors

This study deals with the formation control problem of swarm robots using position sensitive detector (PSD) proximity distance sensors based on light-emitting diodes (LEDs). These proximity distance sensors are lightweight and quickly responsive, and are expected to enhance the mobility and flexibility of swarm robots. However, as each sensor has a narrow detection angle, the formation control problem becomes more difficult than when wide-directional distance sensors (such as cameras and laser rangefinders) are used. To overcome this difficulty, we design a two-part motion controller that controls both position and attitude. The attitude controller is necessary for continuous detection of other robots through the narrow detection angles. The designed controller is distributed in the sense that it requires only information on measured values of each robot’s own sensors. Next, we derive an appropriate sensor arrangement (positions and detection angles) that achieves the desired formation pattern. Finally, the effectiveness of the proposed method is demonstrated in an experiment performed by six omni-wheeled robots equipped with LED-based PSD proximity distance sensors.     

基于观测器的移动执行器对二维分布参数系统的控制

研究二维分布参数系统在传感器执行器网络下的控制问题.传感器执行器网络由二维平面上固定的传感器与移动的执行器组成.首先,利用传感器对二维分布参数系统的测量信息设计观测器,用来估计二维分布参数系统的状态,并在观测器的基础上给出相应的控制器;然后,利用算子半群理论并结合Lyapunov方法,得到平面上移动的执行器的水平运动速度和垂直运动速度,该速度依赖于观测器的信息;最后,数值仿真表明,二维分布参数系统在该移动执行器控制下的性能得到了有效的提高.     

含大时滞和噪声的网络化控制系统的最优故障诊断

研究含大测量时滞和噪声的网络控制系统(Networked control systems, NCS)的故障诊断问题, 提出一种新的基于无时滞转换方法的最优故障诊断器的设计方法. 该方法首先构造一个隐含故障状态的增广系统, 并利用无时滞转换方法将含有测量时滞的网络控制系统转换为无时滞系统. 然后给出了故障的可诊断性判据, 并利用对偶原理将最优故障诊断器的设计问题转换为状态反馈控制器设计问题. 最后, 通过构造一种满足二次型性能指标的最优故障诊断器, 实现了故障的实时诊断. 仿真示例验证了该方法的可行性和有效性.     

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.     

Sensor selection for parameterized random field estimation in wireless sensor networks

We consider the random field estimation problem with parametric trend in wireless sensor networks where the field can be described by unknown parameters to be estimated. Due to the limited resources, the network selects only a subset of the sensors to perform the estimation task with a desired performance under the D-optimal criterion. We propose a greedy sampling scheme to select the sensor nodes according to the information gain of the sensors. A distributed algorithm is also developed by consensus-based ...