Sensor allocation with guaranteed exponential stability for linear multi‐rate sampled‐data systems

This paper addresses sensor allocation with guaranteed exponential stability for linear multi‐rate sampled‐data systems. It is assumed that a continuous‐time linear plant is exponentially stabilized by a continuous‐time linear controller. Given sensors with incommensurate sampling rates, the objective is to allocate each state to a sensor such that the resulting multi‐rate sampled‐data system remains exponentially stable. The main contributions of this paper are twofold. First, we propose sufficient Krasovskii‐based conditions to partition the state vector among sensors such that exponential stability of the closed‐loop system is guaranteed. Second, the problem of finding a partition that guarantees exponential stability is cast as a mixed integer program subject to linear matrix inequalities. The theoretical results are successfully applied to two robotic problems: path‐following in unicycles and hovering in quadrotors. Copyright © 2015 John Wiley & Sons, Ltd.     

Coding Scheme Based on Boundary Functions for Distributed System with Finite Channel Data Rates and Packet Dropouts

This paper considers discrete linear systems with multiple distributed sensors that are connected to one controller via channels of finite data rate. Each encoder has access to only one component of the current system output vector. By the proposed boundary function method, a condition expressed in terms of output feedback gain matrix, the parameters of boundary functions, and the channel data rates is given to guarantee the convergence of the state in the case of reliable channels. Then, we extend the result to the case of erasure channels.     

Sensor Fusion System Using Recurrent Fuzzy Inference

In robotic and manufacturing systems, it is difficult to measure the state of systems accurately because of many uncertain factors and noise, and it is very important to estimate the state of systems. We must measure the phenomena of systems by multiple sensors and estimate the state of systems by acquiring information of sensors. However, we can not acquire all of sensor information synchronically, because each sensor has particular sensor information and measuring time. For estimating the state of systems by multiple sensors, a multi-sensor fusion system fusing various sensory information is needed. In this paper, we propose a Recurrent Fuzzy Inference (RFI) with recurrent inputs and apply it to a multi-sensor fusion system for estimating the state of systems. The membership functions of RFI are expressed by Radial Basis Function (RBF) with insensitive ranges. The shape of the membership functions can be adjusted by a learning algorithm. The learning algorithm is based on the steepest descent method and incremental learning which can add new fuzzy rules. The effectiveness of the multi-sensor fusion system using RFI will be shown through a numerical experiment of moving robot and estimation of surface roughness in grinding process.     

一种基于MEMS传感器的无人飞艇航姿测量系统

提出了一种基于微机电系统(MEMS)惯性传感器的航姿测量系统。分析了一些传统姿态解算算法融合过程中的不足,提出一种高效的融合算法,利用梯度法将加速度计和磁力计对地球重力场和磁场矢量的观测量去修正陀螺给出的姿态信息。针对实际测量系统中振动对姿态的干扰问题,提出切比雪夫II型低通数字滤波器进行传感器数据预处理,并结合运动状态修正融合算法从而进一步抑制振动。通过实验表明:该系统的算法具有较低的计算负荷,能有效地估计出姿态,抑制振动有害加速度对姿态估计的影响,测量动态误差小于2°,静态误差小于0.8°。     

Decentralized robust set-valued state estimation in networked multiple sensor systems

This paper addresses a decentralized robust set-valued state estimation problem for a class of uncertain systems via a data-rate constrained sensor network. The uncertainties of the systems satisfy an energy-type constraint known as an integral quadratic constraint. The sensor network consists of spatially distributed sensors and a fusion center where set-valued state estimation is carried out. The communications from the sensors to the fusion center are through data-rate constrained communication channels. We propose a state estimation scheme which involves coders that are implemented in the sensors, and a decoder–estimator that is located at the fusion center. Their construction is based on the robust Kalman filtering techniques. The robust set-valued state estimation results of this paper involve the solution of a jump Riccati differential equation and the solution of a set of jump state equations.     

Structured residual vector-based approach to sensor fault detection and isolation

This paper proposes a novel approach to detection and isolation of faulty sensors in multivariate dynamic systems. After formulating the problem of sensor fault detection and isolation in a dynamic system represented by a state space model, we develop the optimal design of a primary residual vector for fault detection and a set of structured residual vectors for fault isolation using an extended observability matrix and a lower triangular block Toeplitz matrix of the system. This work is, therefore, a vector extension to the earlier scalar-based approach to fault detection and isolation. Besides proposing a new algorithm for consistent identification of the Toeplitz matrix from noisy input and output observations without identifying the state space matrices {A, B, C, D} of the system, the main contributions of this newly proposed fault detection and isolation scheme are: (1) a set of structured residual vectors is employed for fault isolation; (2) after determination of the maximum number of multiple sensors that are most likely to fail simultaneously, a unified scheme for isolation of single and multiple faulty sensors is proposed; and (3) the optimality of the primary residual vector and the structured residual vectors is proven. We prove the advantage of our newly proposed vector-based scheme over the existing scalar element-based approach for fault isolation and illustrate its practicality by simulated and experimental evaluation on a multivariate pilot scale, computer interfaced system.     

Combination of model-based observer and support vector machines for fault detection of wind turbines

Support vector machines and a Kalman-like observer are used for fault detection and isolation in a variable speed horizontalaxis wind turbine composed of three blades and a full converter. The support vector approach is data-based and is therefore robust to process knowledge. It is based on structural risk minimization which enhances generalization even with small training data set and it allows for process nonlinearity by using flexible kernels. In this work, a radial basis function is used as the kernel. Different parts of the process are investigated including actuators and sensors faults. With duplicated sensors, sensor faults in blade pitch positions,generator and rotor speeds can be detected. Faults of type stuck measurements can be detected in 2 sampling periods. The detection time of offset/scaled measurements depends on the severity of the fault and on the process dynamics when the fault occurs. The converter torque actuator fault can be detected within 2 sampling periods. Faults in the actuators of the pitch systems represents a higher difficulty for fault detection which is due to the fact that such faults only affect the transitory state(which is very fast) but not the final stationary state. Therefore, two methods are considered and compared for fault detection and isolation of this fault: support vector machines and a Kalman-like observer. Advantages and disadvantages of each method are discussed. On one hand, support vector machines training of transitory states would require a big amount of data in different situations, but the fault detection and isolation results are robust to variations in the input/operating point. On the other hand, the observer is model-based, and therefore does not require training, and it allows identification of the fault level, which is interesting for fault reconfiguration. But the observability of the system is ensured under specific conditions, related to the dynamics of the inputs and outputs. The whole fault detection and isolation scheme is evaluated using a wind     

一类不确定广义系统的分散容错控制

讨论一类不确定广义系统分散容错控制器设计问题.首先利用线性矩阵不等式(LMI)设计分散状态反馈控制器,使得广义系统执行器未出现故障时渐近稳定;接着针对广义系统的部分执行器出现故障的情况设计分散状态反馈控制器,使得闭环广义系统渐近稳定;进而利用LMI设计广义系统在分散状态反馈作用下具有完整性的容错控制器;同时对传感器故障情形设计了广义系统在分散输出反馈作用下具有完整性的容错控制器,得到了不确定广义系统关于执行器和传感器的分散容错控制器设计的方法.将所设计的控制器用于实际电子网络系统,验证了所提出方法的有效性.     

Optimal addition of servers for a time-dependent queueing process

Necessary conditions for optimal control of systems containing a time delay that is a function of the state of the system and of time are derived by utilizing calculus of variations. The time delay may be in the state vector and in the control vector. The state vector and the control vector can be constrained by inequality constraints. A transformation to eliminate state variable inequality constraints by increasing the dimensions of state space, developed by Jacobson for an undelayed system, is extended to a system with time delays. Necessary conditions to obtain an optimal delay are shown, and an example of finding an optimal delay is included. A gradient algorithm for systems with state dependent time delays has been developed.     

Extended Kalman and Particle Filtering for sensor fusion in motion control of mobile robots

Motion control of mobile robots and efficient trajectory tracking is usually based on prior estimation of the robots’ state vector. To this end Gaussian and nonparametric filters (state estimators from position measurements) have been developed. In this paper the Extended Kalman Filter which assumes Gaussian measurement noise is compared to the Particle Filter which does not make any assumption on the measurement noise distribution. As a case study the estimation of the state vector of a mobile robot is used, when measurements are available from both odometric and sonar sensors. It is shown that in this kind of sensor fusion problem the Particle Filter has better performance than the Extended Kalman Filter, at the cost of more demanding computations.     

On-demand data assimilation of large-scale spatial temporal systems using sequential Monte Carlo methods

The proliferation of sensors is generating rapidly increasing quantities of data like never before. These extensive amounts of data can provide useful information for more accurate state inference of large-scale spatial temporal systems. Sequential Monte Carlo methods are used to assimilate the observed data from sensors to improve the state estimation of large-scale spatial temporal systems, which highly rely on the available real time observation data. In many scenarios, the real time data are limited in space and time. Therefore, it is important to effectively obtain critical sensor data in real time and then dynamically feed them into the running model. In this paper, we propose the on-demand data assimilation method for large-scale spatial temporal systems, in which we quantify the spatial states using run-time state quantification methods and decide if we need to trigger data assimilation on demand and obtain more relevant real time data when the state uncertainty is high. The effectiveness of the developed framework is evaluated based on large-scale wildfire spread simulations.     

时滞系统的降维状态预测观测器及预测控制器设计

研究控制项含有时滞的线性系统的预测控制问题.利用被控对象的预测输出向量和系统的控制向量,设计了一种降维状态预测观测器,并将该状态观测器用于时滞控制系统的最优状态反馈控制中.利用该状态预测观测器可将闭环系统的时滞项移至系统闭环结构之外,从而最优控制规律完全可以按无时滞系统进行设计.由性能指标计算公式表明,该预测控制器关于二次型性能指标是次优的.     

DigitalBeing - using the environment as an expressive medium for dance

Dancers express their feelings and moods through gestures and body movements. We seek to extend this mode of expression by dynamically and automatically adjusting music and lighting in the dance environment to reflect the dancer’s arousal state. Our intention is to offer a space that performance artists can use as a creative tool that extends the grammar of dance. To enable the dynamic manipulation of lighting and music, the performance space will be augmented with several sensors: physiological sensors worn by a dancer to measure her arousal state, as well as pressure sensors installed in a floor mat to track the dancers’ locations and movements. Data from these sensors will be passed to a three layered architecture. Layer 1 is composed of a sensor analysis system that analyzes and synthesizes physiological and pressure sensor signals. Layer 2 is composed of intelligent systems that adapt lighting and music to portray the dancer’s arousal state. The intelligent on-stage lighting system dynamically adjusts on-stage lighting direction and color. The intelligent virtual lighting system dynamically adapts virtual lighting in the projected imagery. The intelligent music system dynamically and unobtrusively adjusts the music. Layer 3 translates the high-level adjustments made by the intelligent systems in layer 2 to appropriate lighting board, image rendering, and audio box commands. Furthermore the resulting artifact is the DigitalBeing - a personal signature of the dancer in digital space. In this paper, we will describe this architecture in detail as well as the equipment and control systems used.     

Distributed estimation for adaptive sensor selection in wireless sensor networks

Wireless sensor networks (WSNs) are usually deployed for monitoring systems with the distributed detection and estimation of sensors. Sensor selection in WSNs is considered for target tracking. A distributed estimation scenario is considered based on the extended information filter. A cost function using the geometrical dilution of precision measure is derived for active sensor selection. A consensus-based estimation method is proposed in this paper for heterogeneous WSNs with two types of sensors. The convergence properties of the proposed estimators are analyzed under time-varying inputs. Accordingly, a new adaptive sensor selection (ASS) algorithm is presented in which the number of active sensors is adaptively determined based on the absolute local innovations vector. Simulation results show that the tracking accuracy of the ASS is comparable to that of the other algorithms.     

Computation and transmission requirements for a decentralized linear-quadratic-Gaussian control problem

A decentralized control problem involvingKnodes is formulated At each node are sensors and controls. The object is to share the information of each sensor, processed with a local Kalman estimator, with all the other nodes so that the controllers can be computed using the best estimate of the state of the system given the information from all the sensors. The controls are determined so that the expected value of a quadratic performance index is minimized. The problem is formulated as a decentralized control problem without a central supervisor so that the system performance will degrade gracefully under structural perturbations. Therefore, the transmission of data is from each node to every other node; there aresumliminf{i-1}limsup{k}(i-1)links connecting all nodes. It is shown that if the dimension of the controls at each nodelis less than both the dimension of the data at nodemand the dimension of the state, then a data vector with dimension of the control atlcan be transmitted frommtol. compression of data transmission is done at the expense of propagating an additionaal data dependent vector at each node beyond the usual Kalman filter equations.     

交互式多模型异步航迹融合算法

对分布式融合系统中的异步航迹融合算法进行了研究.已有的异步航迹融合算法要求传感器所采用的状态方程与目标运动模式相同,这在实际应用中很难满足.针对已有异步航迹融合算法的不足,建立了基于状态方程集合的异步航迹融合模型.所有传感器均采用基于同一状态方程集合的交互式多模型(IMM)算法来实现航迹生成,融合中心对来自于相同状态方程的异步航迹数据进行加权平均来获得同步航迹,提出了交互式多模型异步航迹融合(IMMASTF)算法.仿真结果验证了IMMASTF算法的有效性.     

Partial state and input observability recovering by additional sensor implementation: a graph–theoretic approach

This article deals with the problem of additional sensor location in order to recover the observability of any given part of the state and unknown input for structured linear systems. The proposed method is based on a graph-theoretic approach and assumes only the knowledge of the system's structure. We first provide graphical and necessary sufficient conditions for the generic observability of any given part of the state and input. Then, we study the number and location of additional sensors in order to satisfy the latter conditions. On the one hand, we provide necessary requirements to be satisfied by these additional sensors. On the other hand, we give other sufficient simple conditions allowing us to add a number, which is guaranteed to be minimal, of sensors to ensure the observability of any given part of the state and unknown input.     

Nested saturation control of multiple vector integrators and its application to motion control of UAVs

This paper presents two nested input saturation control schemes for a special class of multiple vector integrators with bounded additive disturbances. The considered systems originate from the motion control of rotary‐wing unmanned aerial vehicles (UAVs). The first scheme is based on a feedforward form, which requires state transformation and can be applied to stabilize arbitrary order vector integrator systems. The second scheme is constructed with original state variables using a new approach, applied here to double vector integrator systems. The capability of handling external disturbance using the two schemes is also analyzed. The two schemes are applied to design motion controllers for rotary‐wing UAVs and simulation results are provided to show the performances of two controllers.     

Application of Cayley-Klein parameters in the problem of strapdown navigation system stochastic state vector synthesis for a self-contained space vehicle

The state vector and observer equations are obtained for a strapdown (gimballess) navigation system with three accelerometers, three angular rate sensors, and a barometric altimeter. These equations are based on the nonlinear filtration methods that ensure required estimation accuracy under arbitrary space maneuver of a vehicle.     

Sub-optimal solutions of linear control and eigenvalue pattern problems†

The present paper deals with sub-optimal solutions for problems of minimizing integral cost fimetioiuils which consider the state variables alone for stationary linear processes. The control vector and its time derivative are assumed to be bounded. The resulting control policies are linear with respect to the state vector. The performance of the proposed sub-optimal policies is compared with that of classical control systems and a procedure for sub-optimal eigenvalue patterns is presented for systems of any order.