State estimator for multisensor systems with irregular sampling and time-varying delays

This article addresses the state estimation in linear time-varying systems with several sensors with different availability, randomly sampled in time and whose measurements have a time-varying delay. The approach is based on a modification of the Kalman filter with the negative-time measurement update strategy, avoiding running back the full standard Kalman filter, the use of full augmented order models or the use of reorganisation techniques, leading to a lower implementation cost algorithm. The update equations are run every time a new measurement is available, independently of the time when it was taken. The approach is useful for networked control systems, systems with long delays and scarce measurements and for out-of-sequence measurements

Jump state estimation with multiple sensors with packet dropping and delaying channels

This work addresses the design of a state observer for systems whose outputs are measured through a communication network. The measurements from each sensor node are assumed to arrive randomly, scarcely and with a time-varying delay. The proposed model of the plant and the network measurement scenarios cover the cases of multiple sensors, out-of-sequence measurements, buffered measurements on a single packet and multirate sensor measurements. A jump observer is proposed that selects a different gain depending on the number of periods elapsed between successfully received measurements and on the available data. A finite set of gains is pre-calculated offline with a tractable optimisation problem, where the complexity of the observer implementation is a design parameter. The computational cost of the observer implementation is much lower than in the Kalman filter, whilst the performance is similar. Several examples illustrate the observer design for different measurement scenarios and observer complexity and show the achievable performance

Estimation in multisensor networked systems with scarce measurements and time varying delays

In this paper, the problem of estimating signals from a dynamic system at regular periods from scarce, delayed and possibly time disordered measurements acquired through a network is addressed. A model based predictor that takes into account the delayed and irregularly gathered measurements from different devices is used. Robustness of the predictor to the time-delays and scarce data availability as well as disturbance and noise attenuation is dealt with via H∞ performance optimization. The result is a time variant estimator gain that depends on the measurement characteristics, but belonging to an offline precalculated finite set, and hence, the online needed computer resources are low. An alternative to reduce the number of gains to be stored has been proposed, based on defining the gain as a function of the sampling parameters. The idea allows reaching a compromise between online computer cost and performance.This work has been granted by the MICINN project number DPI2008-06731-C02-02/DPI.Albertos Pérez, P. (2012). Estimation in multisensor networked systems with scarce measurements and time varying delays. Systems and Control Letters. 61(4):555-562. https://doi.org/10.1016/j.sysconle.2012.02.009S55556261

Inferential networked control with accessibility constraints in both the sensor and actuator channels

The predictor and controller design for an inferential control scheme over a network is addressed. A linear plant with disturbances and measurement noise is assumed to be controlled by a controller that communicates with the sensors and the actuators through a constrained network. An algorithm is proposed such that the scarce available outputs are used to make a prediction of the system evolution with an observer that takes into account the amount of lost data between successful measurements transmissions. The state prediction is then used to calculate the control actions sent to the actuator. The possibility of control action drop due to network constraints is taken into account. This networked control scheme is analyzed and both the predictor and controller designs are addressed taking into account the disturbances, the measurement noise, the scarce availability of output samples and the scarce capability of control actions update. The time-varying sampling periods that result for the process inputs and outputs due to network constraints have been determined as a function of the probability of successful transmission on a specified time with a Bernoulli distribution. For both designs H∞ performance has been established and LMI design techniques have been used to achieve a numerical solution

Handling Out-of-Sequence Data: Kalman Filter Methods or Statistical Imputation?

The issue of handling sensor measurements data over single and multiple lag delays also known as outof-sequence measurement (OOSM) has been considered. It is argued that this problem can also be addressed using model-based imputation strategies and their application in comparison to Kalman filter (KF)-based approaches for a multi-sensor tracking prediction problem has also been demonstrated. The effectiveness of two model-based imputation procedures against five OOSM methods was investigated in Monte Carlo simulation experiments. The delayed measurements were either incorporated (or fused) at the time these were finally available (using OOSM methods) or imputed in a random way with higher probability of delays for multiple lags and lower probability of delays for a single lag (using single or multiple imputation). For single lag, estimates of target tracking computed from the observed data and those based on a data set in which the delayed measurements were imputed were equally unbiased; however, the KF estimates obtained using the Bayesian framework (BF-KF) were more precise. When the measurements were delayed in a multiple lag fashion, there were significant differences in bias or precision between multiple imputation (MI) and OOSM methods, with the former exhibiting a superior performance at nearly all levels of probability of measurement delay and range of manoeuvring indices. Researchers working on sensor data are encouraged to take advantage of software to implement delayed measurements using MI, as estimates of tracking are more precise and less biased in the presence of delayed multi-sensor data than those derived from an observed data analysis approach.Defence Science Journal, 2010, 60(1), pp.87-99, DOI:http://dx.doi.org/10.14429/dsj.60.11

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

AbstractThis 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

Multirate control with incomplete information over Profibus-DP network

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Systems Science on 2014, available online:http://www.tandfonline.com/10.1080/00207721.2013.844286When a process ¿eld bus-decentralized peripherals (Pro¿bus-DP) network is used in an industrial environment, a deterministic behaviour is usually claimed. However, due to some concerns such as bandwidth limitations, lack of synchronisation among different clocks and existence of time-varying delays, a more complex problem must be faced. This problem implies the transmission of irregular and, even, random sequences of incomplete information. The main consequence of this issue is the appearance of different sampling periods at different network devices. In this paper, this aspect is checked by means of a detailed Pro¿bus-DP timescale study. In addition, in order to deal with the different periods, a delay-dependent dual-rate proportional-integral-derivative control is introduced. 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Linear Estimation in Interconnected Sensor Systems with Information Constraints

A ubiquitous challenge in many technical applications is to estimate an unknown state by means of data that stems from several, often heterogeneous sensor sources. In this book, information is interpreted stochastically, and techniques for the distributed processing of data are derived that minimize the error of estimates about the unknown state. Methods for the reconstruction of dependencies are proposed and novel approaches for the distributed processing of noisy data are developed

Linear Estimation in Interconnected Sensor Systems with Information Constraints

A ubiquitous challenge in many technical applications is to estimate an unknown state by means of data that stems from several, often heterogeneous sensor sources. In this book, information is interpreted stochastically, and techniques for the distributed processing of data are derived that minimize the error of estimates about the unknown state. Methods for the reconstruction of dependencies are proposed and novel approaches for the distributed processing of noisy data are developed