Observable Subsystem Research Articles

State of charge estimation for lithium-ion batteries with pre-set convergence time based on a comprehensive unobservable model

State of charge (SOC) is a critical metric for assessing the remaining capacity of lithium-ion batteries, while convergence time is an essential indicator for evaluating the performance of SOC estimation. To reduce the convergence time of SOC estimation, a novel strategy has been proposed. Initially, an accurate and comprehensive model is employed for lithium-ion batteries, which includes the nonlinear behavior of the open circuit voltage (OCV) with respect to SOC. However, the linear component of this model is unobservable. Then, the nonlinear relationship between OCV and SOC is approximated using a first-order piecewise fitting function. This approach ensures accuracy while reducing computational complexity compared to non-linearization methods. Next, the unobservable model is decomposed into observable and unobservable subsystems, addressing the challenge of designing observers for unobservable models. Furthermore, a finite time observer is designed based on the observable subsystem, enabling SOC estimation to be achieved within any pre-set convergence time. Finally, theoretical analysis and extensive experimental results validate the feasibility and reliability of the proposed estimation strategy, demonstrating its superiority over traditional methods.

Designing a completely distributed observer with robustness against disturbances

AbstractDistributed observer is an effective scheme for state estimation of a targeted system, utilizing a network of sensors to perform local output measurements. In this article, the problem of designing a distributed observer for a linear time‐invariant system with external disturbances is addressed. The objective is to perform state estimation of such a system while simultaneously mitigating the impact of external disturbances and maintaining the distributed communication characteristics among the sensors. To achieve this, a completely distributed observer with robustness against disturbances is developed by combining the high‐gain observer technique and the adaptive coupling strength strategy. Initially, a traditional high‐gain observer is used for the state estimation on the observable subsystem, and subsequently a high‐gain observer without peaking is introduced to avoid the peaking phenomenon. Finally, numerical simulations are presented to verify the theoretical results.

Complementing XCO2 imagery with ground-based CO2 and 14CO2 measurements to monitor CO2 emissions from fossil fuels on a regional to local scale

Abstract. Various satellite imagers of the vertically integrated column of carbon dioxide (XCO2) are under development to enhance the capabilities for the monitoring of fossil fuel (FF) CO2 emissions. XCO2 images can be used to detect plumes from cities and large industrial plants and to quantify the corresponding emission using atmospheric inversions techniques. However, this potential and the ability to catch the signal from more diffuse FF CO2 sources can be hampered by the mix between these FF signals and a background signal from other types of CO2 surface fluxes, and in particular of biogenic CO2 fluxes. The deployment of dense ground-based air-sampling networks for CO2 and radiocarbon (14CO2) could complement the spaceborne imagery by supporting the separation between the fossil fuel and biogenic or biofuel (BF) CO2 signals. We evaluate this potential complementarity with a high-resolution analytical inversion system focused on northern France, western Germany, Belgium, Luxembourg, and a part of the Netherlands and with pseudo-data experiments. The inversion system controls the FF and BF emissions from the large urban areas and plants, in addition to regional budgets of more diffuse emissions or of biogenic fluxes (NEE, net ecosystem exchange), at an hourly scale over a whole day. The system provides results corresponding to the assimilation of pseudo-data from a single track of a 300 km swath XCO2 imager at 2 km resolution and from surface ground-based CO2 and/or 14CO2 networks. It represents the diversity of 14CO2 sources and sinks and not just the dilution of radiocarbon-free FF CO2 emissions. The uncertainty in the resulting FF CO2 emissions at local (urban area/plant) to regional scales is directly derived and used to assess the potential of the different combinations of observation systems. The assimilation of satellite observations yields estimates of the morning regional emissions with an uncertainty down to 10 % (1σ) in the satellite field of view, from an assumed uncertainty of 15 % in the prior estimates. However, it does not provide direct information about emissions outside the satellite field of view or about afternoon or nighttime emissions. The co-assimilation of 14CO2 and CO2 surface observations leads to a further reduction of the uncertainty in the estimates of FF emissions. However, this further reduction is significant only in administrative regions with three or more 14CO2 and CO2 sampling sites. The uncertainty in the estimates of 1 d emission in North Rhine-Westphalia, a region with three sampling sites, decreases from 8 % to 6.6 % when assimilating the in situ 14CO2 and CO2 data in addition to the satellite data. Furthermore, this additional decrease appears to be larger when the ground stations are close to large FF emission areas, providing an additional direct constraint for the estimate of these sources rather than supporting the characterization of the background signal from the NEE and its separation from that of the FF emissions. More generally, the results indicate no amplification of the potential of each observation subsystem when they are combined into a large observation system with satellite and surface data.

Partial observer canonical form for multi-output nonlinear forced system: a new method

PurposeThe purpose of this paper is to investigate the design method of partial observer canonical form (POCF), which is one of the important research tools for industrial plants.Design/methodology/approachMotivated by the two-steps method proposed in Xu et al. (2020), this paper extends this method to the case of Multi-Input Multi-Output (MIMO) nonlinear system. It decomposes the original system into two subsystems by observable decomposition theorem first and then transforms the observable subsystem into OCF. Furthermore, the necessary and sufficient conditions for the existing of POCF are proved.FindingsThe proposed method has a wide range of applications including completely observable nonlinear system, noncompletely observable nonlinear system, autonomous nonlinear system and forced nonlinear system. Besides, comparing to the existing results (Saadi et al., 2016), the method requires less verified conditions.Originality/valueThe new method concerning design POCF has better plants compatibility and less validation conditions.

Reliability analysis of underground rock bolters using the renewal process, the non-homogeneous Poisson process and the Bayesian approach

PurposeReliability plays an important role in the execution of the maintenance improvement and the understanding of its concepts is essential to predict the type of maintenance according to the equipment state. Thereby, a computational tool was developed and programming with VBA in Excel® for reliability and failure analysis in a mining context. The paper aims to discuss these issues.Design/methodology/approachThe developed approach use the modeling of stochastic processes, such as the renewal process, the non-homogeneous Poisson process and less conventional method as the Bayesian approach, by considering Jeffreys non-informative prior. The resolution gives the best associated model, the parameters estimation, the mean time between failure and the reliability estimate. This approach is validated with the reliability analysis of inter-failure times from underground rock bolters subsystems, over a two-year period.FindingsResults show that Weibull and lognormal probability distribution fit to the most subsystems inter-failure times. The study revealed that the bolting head, the rock drill, the screen handler, the electric/electronic system, the hydraulic system, the drilling feeder and the structural consume the most repair frequency. The hydraulic and electric/electronic subsystems represent the lowest reliability after 50 operation hours.Originality/valueFor the first time, this case study defines practical failures and reliability information for rock bolter subsystems based on real operation data. This paper is useful to the comparative evaluation of rock bolter by detecting the weakest elements and understanding failure patterns in the individual observation subsystems on the overall machine performance.

A framework for operator – workstation interaction in Industry 4.0

We draw on cognitive and behavioural theories and on the artificial intelligence literature in order to propose a framework of future operator – workstation interaction in the ‘Industry 4.0’ era. We name the proposed framework ‘Operator – Workstation Interaction 4.0’. The latter’s capabilities permit an adaptive, ongoing interaction that aims to improve operator safety, performance, well-being, and satisfaction as well as the factory’s production measures. The framework is composed of three subsystems: (1) the observation subsystem which observes the operator and the processes occurring in the workstation, (2) the analysis subsystem which generates understanding and implications of the observations output, (3) the reaction subsystem which determines if and how to respond. The paper describes these elements and illustrate them using an example of a fatigued worker. The contributions, implications, and limitations of the proposed framework are discussed, and future research directions are presented.

Reliable Distributed Integrated Navigation Based on CI during Mars Entry

A reliable distributed covariance intersection (CI) fusion integrated navigation algorithm with information feedback during the Mars atmospheric entry is proposed to meet robust, reliable, and high-precision Mars atmospheric entry navigation strategy. A distributed integrated navigation scheme includes four independent subsystems consisting of an IMU and one radio beacon, but each subsystem is weakly observable under limited Mars entry measurements. The scalar weights are fast obtained by maximizing the information contribution of the corresponding estimate. The distributed framework based upon the CI fusion algorithm is designed by using dynamic information distribution coefficients and information feedback strategy. This distributed fusion approach could meet the lower computation cost and robust and reliable capacity and especially is beneficial to the weakly observable or unobservable subsystems during the Mars entry navigation scheme. Numerical simulations show that the proposed distributed CI fusion integrated navigation algorithm can provide consistent navigation accuracy for the Mars entry vehicle and improve the entry navigation robustness under the weakly observable navigation subsystems.

Integrated design of fault estimation and fault-tolerant control for linear multi-agent systems using relative outputs

In this paper, the problem of fault estimation and fault-tolerant control is investigated for linear multi-agent systems with mismatched uncertainty. Different from the existing results, an integrated design method is proposed to solve the problem of bi-directional uncertainty which arises from the combination of fault estimation and control. In order to realize a distributed integrated design, spectral decomposition and coordinate transformation are introduced so that the leader-following multi-agent systems can be decoupled into independent observable subsystems. For this new subsystem, an unknown input observer and a fault-tolerant controller are constructed by only using the relative outputs, and an integrated design condition is given in terms of linear matrix inequality. Finally, two examples are utilized to illustrate the validity of this method.

From Structural Analysis to Observer–Based Residual Generation for Fault Detection

Abstract This paper combines methods for the structural analysis of bipartite graphs with observer-based residual generation. The analysis of bipartite structure graphs leads to over-determined subsets of equations within a system model, which make it possible to compute residuals for fault detection. In observer-based diagnosis, by contrast, an observability analysis finds observable subsystems, for which residuals can be generated by state observers. This paper reveals a fundamental relationship between these two graph-theoretic approaches to diagnosability analysis and shows that for linear systems the structurally over-determined set of model equations equals the output connected part of the system. Moreover, a condition is proved which allows us to verify structural observability of a system by means of the corresponding bipartite graph. An important consequence of this result is a comprehensive approach to fault detection systems, which starts with finding the over-determined part of a given system by means of a bipartite structure graph and continues with designing an observerbased residual generator for the fault-detectable subsystem found in the first step.

Design of Simulation Training System for the Multi-element Observation System

A simulation training system for the multi-element observation system integrating several observation subsystems is constructed in this paper by applying the distributed simulation with a combination of virtual simulation and semi-physical simulation. The simulation system is designed for providing the basic services of training scene and visual synchronization for the clients of all observation equipment, developing the simulation software at the clients for simulating observation, and utilizing a general transmission platform to comprehensively realize the simulation training functions of interconnection to the information system and observation. The design of such a training system is expatiated in this paper.

Distributed Interacting Multiple Filters for Fault Diagnosis of Navigation Sensors in a Robotic System

In this paper, a distributed interacting multiple model-based fault detection and isolation (FDI) scheme is presented for FDI of navigation sensors composed of inertial (accelerometers and gyroscopes) and camera sensors in a robotic system in which noisy and erroneous measurements of microelectro mechanical system (MEMS)-based inertial sensors are fused with a photogrammetric camera. Multiple models are employed to describe different scenarios of hard faults (failures) in the sensors where the models are different in each scenario because inertial sensor drifts (as soft or partial faults) are also modeled and augmented to the motion state parameters. Having several faulty modes due to the possibility of single and multiple failures in the sensors, it is proposed in this paper to decompose the system to interacting observable subsystems with reduced size and decoupled model sets. The system and the corresponding model set are decomposed using a graph theoretic decomposition approach. Distributed interacting multiple Kalman and extended Kalman filters are then designed for the purpose of FDI. Experimental results based on data from a 3-D MEMS inertial measurement unit and 3-D camera system are used to demonstrate the efficiency of the method.

Single-experiment observability decomposition of discrete-time analytic systems

This paper addresses the single-experiment observability decomposition of discrete-time analytic systems. Unlike the continuous-time case, there exist systems which cannot be decomposed into observable and unobservable subsystems due to the fact that the observable space is not integrable. In this paper, a necessary and sufficient condition for integrability of observable space is given. As a corollary of this condition it is proven that if the system is reversible, the observability decomposition can be always achieved. Moreover, integrability of observable space is also addressed for delta-domain models of non-uniformly sampled systems.

Model-reduced fault detection for multi-rate sensor fusion with unknown inputs

In multi-sensor fusion, it is hard to guarantee that all sensors work at the single sampling rate, especially in the distributive and/or heterogeneous case, and fault detection (FD) in multi-rate sensor fusion may face the existence of unknown inputs (UIs) in complex environment. Meanwhile, model reduction often refers to propose a possible lower-dimensional model to replace the original model without adding significant error in practical applications. By the fact that FD in dynamic systems should only focus on the fault-related controllability and observability characteristics, it is a good idea to obtain the fault-related controllable and observable subsystem via system decomposition (i.e., model reduction) for FD. Such a kind of model reduction not only guarantee the FD performance, but also reduce the system dimensions. To this end, we propose the model-reduced fault detection (MRFD) problem for multi-rate sensor fusion subject to UIs and faults imposed on the actuator and sensors. Our aim is to design a fast and computation-effective FD scheme based on the reduced model. We use the singular decomposition for UI decoupling, and then obtain the fault-related subsystem via controllability and observability decomposition. And then the multi-rate observer (MRO) with causality constraints is designed. Different from the traditional observer used for FD, the proposed MRO outputs the fault-related partial state estimate as soon as any a sensor measurement is received, resulting in fast and computation-effective FD. Furthermore, conditions for the existence of a stable MRO, fault-to-state controllability, and fault detectability are explored. A simulation example for simplified longitudinal flight control system and method comparison with the existing multi-rate FD algorithms show the effectiveness of the proposed MRFD method.

Fast fault estimation for linear systems with unmatched faults

This paper proposes a fast fault estimation (FE) method for a class of linear time-invariant systems, which relaxes the strict observer matching condition. Firstly, the integration unknown input observer is designed for the more widely left invertible systems. Secondly, for strongly observable systems, by digesting the idea of a finite-time observer, the finite-time integration unknown input observer is designed, and the faults are estimated exactly in pre-designed arbitrary finite time. Furthermore, for strongly detectable systems, after the system decomposed into strongly observable part and nonstrongly observable part, faults are able to be reconstructed very rapidly due to the finite-time observation of the strongly observable subsystem. The simulation results illustrate the effectiveness and the advantage of the proposed fast fault estimation schemes.

Energy‐efficient data fusion over wireless channels with power control

The problem of energy-efficient state estimation for a discrete-time linear system, the states of which are measured by multiple sensors and transmitted over wireless channels, is dealt with in this study. The effect of packet loss on estimation performance is considered here, and the packet loss probability depends on time-varying channel gains, packet lengths and transmitted power of sensors. The data fusion strategy is based on covariance intersection fusion of local state estimates of the observable subsystem of each sensor. The sufficient conditions, imposing constraint on the packet loss probability of each channel, are established by taking into account each observable subsystem structure to guarantee the expectation of the trace of estimation error covariance matrices is exponentially bounded, and the upper bound is given. The conditions obtained in this study can also be utilised to minimise the energy used by each sensor. Simulation examples are provided to demonstrate the effectiveness of the results.

Exact State Estimation for Linear Systems with Unbounded Disturbances

A finite-time observer is designed for linear invariant systems in the presence of unknown inputs or disturbances with unavailable upper bound. The main condition for designing the observer is strongly detectability. Geometric control theory is used to decompose the given system into two parts: strongly observable subsystem and strongly detectable subsystem. Through a series of transformations, the former can be partitioned into two parts: affected and unaffected by unknown inputs (UI-free and UI-dependent), and then the states can be exactly estimated via time-delayed observer in pre-defined time. The nonstrongly observable subsystem can be observed asymptotically. Without the upper bound of disturbances, the observer ensures that the convergence time can be set arbitrarily. A numerical example illustrates the effective of the proposed estimation schemes.

Possibilities of using ICT for increase of customer feeling of exceptionality

Accession of the Internet undoubtedly has contributed to the shift of marketing activities. With the increasing availability and wealth of online information customers become more knowledgeable and sophisticated. If a company wants to keep its customers in long term, it must offer something more than an “ordinary” product, which has comparable substitutes in tens of other companies.At first it is necessary to identify customers behavior and thinking. Observation of his/her preferences of a demanded product line discovers products playing the key role from his/her point of view. We use methodical tools for this purpose e.g. CRM systems, Business Intelligence etc. Over gained metrics from data in data warehouses (observation subsystem) we make evaluations according to various criteria (subsystem of evaluation). In this report we methodically open up not just e.g. data mining technologies, but also based on aggregated data we infer e.g. consumer’s trends.The goal of the article is to point out suitable instruments which enable to create interaction with customers, where they’ll be able to participate in creation products of their own. The Internet seems as a perfect communication channel for this interaction. Web application will consist of solution of an observation subsystem as a database application and of evaluation subsystem as the application of a row of methods e.g. multicriteria evaluation. Web applications feedback effect leads into knowledge of selected customer’s characteristics, which enables to offer him/her among others a special line of standard products.By creating a disposable web application it’s possible e.g. to offer customers absolutely common products with different visual angle. Thanks to gained information and knowledge we divide customers into groups and then target on them specifically.

Distributed observers for pose estimation in the presence of inertial sensory soft faults

Distributed Particle-Kalman Filter based observers are designed in this paper for inertial sensors (gyroscope and accelerometer) soft faults (biases and drifts) and rigid body pose estimation. The observers fuse inertial sensors with Photogrammetric camera. Linear and angular accelerations as unknown inputs of velocity and attitude rate dynamics, respectively, along with sensory biases and drifts are modeled and augmented to the moving body state parameters. To reduce the complexity of the high dimensional and nonlinear model, the graph theoretic tearing technique (structural decomposition) is employed to decompose the system to smaller observable subsystems. Separate interacting observers are designed for the subsystems which are interacted through well-defined interfaces. Kalman Filters are employed for linear ones and a Modified Particle Filter for a nonlinear non-Gaussian subsystem which includes imperfect attitude rate dynamics is proposed. The main idea behind the proposed Modified Particle Filtering approach is to engage both system and measurement models in the particle generation process. Experimental results based on data from a 3D MEMS IMU and a 3D camera system are used to demonstrate the efficiency of the method.

Robust Fault Estimation Using Relative Information in Linear Multi-Agent Networks

In this technical note, a robust fault estimation method, based on sliding mode observers, is proposed for a collection of agents undertaking a shared task and exchanging only relative information over a communication network. Since the `system of systems' formed by the agents is not observable with respect to relative sensing information, by appropriate transformations and scalings of the inputs and outputs of the actual system, a meaningful observable subsystem is created. For this new subsystem, after modal decomposition based on the associated Laplacian, decoupled sliding mode observers, depending only on the individual node level dynamics of the network, can be created exploiting an existing design philosophy. These collectively form a centralized fault estimation scheme for the original system.

Stochastic Sensor Scheduling for Networked Control Systems

Optimal sensor scheduling with applications to networked estimation and control systems is considered. We model sensor measurement and transmission instances using jumps between states of a continuous-time Markov chain. We introduce a cost function for this Markov chain as the summation of terms depending on the average sampling frequencies of the subsystems and the effort needed for changing the parameters of the underlying Markov chain. By minimizing this cost function through extending Brockett's recent approach to optimal control of Markov chains, we extract an optimal scheduling policy to fairly allocate the network resources among the control loops. We study the statistical properties of this scheduling policy in order to compute upper bounds for the closed-loop performance of the networked system, where several decoupled scalar subsystems are connected to their corresponding estimator or controller through a shared communication medium. We generalize the estimation results to observable subsystems of arbitrary order. Finally, we illustrate the developed results numerically on a networked system composed of several decoupled water tanks.