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This article studies unknown input observer design for linear parameter-varying systems in a bounded error context. By presenting a novel unknown input observer structure, the output equation of linear parameter-varying systems is not restricted to be in a linear form. Moreover, by integrating decoupling technique and set-membership approach, a compromise is achieved between the restrictiveness and the conservativeness of estimation. Based on P-radius criteria, sufficient design conditions are derived in terms of linear matrix inequalities. Finally, numerical simulations of a vehicle lateral dynamics system are conducted to demonstrate the validity of the presented method. [ABSTRACT FROM AUTHOR]

This paper investigates interval estimation methods for discrete-time linear time-invariant systems. We propose a novel interval estimation method by integrating robust observer design with reachability analysis. By introducing $H_{\infty }$ design into interval estimation, the proposed method is effective in improving the accuracy of interval estimation. Moreover, the comparisons and relationships between the existing methods and the proposed method are discussed in detail. Finally, simulation results are presented to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This technical note proposes a novel set-membership state estimation approach based on zonotopes for discrete-time descriptor linear parameter-varying systems. The consistency test between the system model and measured outputs is implemented to construct a parameterized intersection zonotope with respect to a correction matrix. With a defined zonotope minimization criterion, we propose a novel offline optimization problem to obtain the optimal correction matrix. In addition, with the proposed approach, an adaptive bound of the radius of the intersection zonotope is also provided. Finally, a case study with a truck-trailer system is shown to illustrate the proposed approach. [ABSTRACT FROM AUTHOR]

Purpose H2O, CO2 and CO are three main species in combustion systems which have high volume fractions. In addition, soot has strong absorption in the infrared band. Thus, H2O, CO2, CO and soot may take important roles in radiative heat transfer. To provide calculations with high accuracy, all of the participating media should be considered non-gray media. Thus, the purpose of this paper is to study the effect of non-gray participating gases and soot on radiative heat transfer in an inhomogeneous and non-isothermal system.Design/methodology/approach To solve the radiative heat transfer, the fluid flow as well as the pressure, temperature and species distributions were first computed by FLUENT. The radiative properties of the participating media are calculated by the Statistical Narrow Band correlated K-distribution (SNBCK), which is based on the database of EM2C. The calculation of soot properties is based on the Mie scattering theory and Rayleigh theory. The radiative heat transfer is calculated by the discrete ordinate method (DOM).Findings Using SNBCK to calculate the radiative properties and DOM to calculate the radiative heat transfer, the influence of H2O, CO2, CO and soot on radiation heat flux to the wall in combustion system was studied. The results show that the global contribution of CO to the radiation heat flux on the wall in the kerosene furnace was about 2 per cent, but that it can reach up to 15 per cent in a solid fuel gasifier. The global contribution of soot to the radiation heat flux on the wall was 32 per cent. However, the scattering of soot has a tiny influence on radiation heat flux to the wall.Originality/value This is the first time H2O, CO2, CO and the scattering of soot were all considered simultaneously to study the radiation heat flux in combustion systems. [ABSTRACT FROM AUTHOR]

In this paper, an interval observer-based control problem under the FlexRay protocols is investigated for a class of multirate systems in which the noises are assumed to be unknown but bounded. The FlexRay protocol is employed to schedule the information transfer so as to reduce the communication burden. Based on the FlexRay protocol and the multirate sampling mechanism, a new measurement output representation is established with the help of the lifting technique and the augmentation technique, and then, a sufficient condition is proposed to guarantee the interval observer estimation error dynamics are bounded. In addition, the interval observer gain and the H ∞ controller gain are obtained by solving the linear matrix inequalities, which further ensure the H ∞ performance of the closed-loop system. Finally, a numerical simulation verifies the effectiveness of the FlexRay protocols and interval observer-based controllers. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of state and unknown input estimation for linear discrete-time systems subject to unknown but bounded process disturbance and measurement noise. The existing methods treat this problem based on coordinate transformation, which is complex and may incorporate some conservatism. Unlike the existing methods, we estimate the state and unknown input simultaneously based on an augmented approach. Then a novel observer structure and an L ∞ norm based approach are presented to obtain a tight interval of estimation. The viability and validity of the proposed method are demonstrated by numerical simulations. [ABSTRACT FROM AUTHOR]

This paper is concerned with the event-triggered fault estimation and fault-tolerant control for continuous-time dynamic systems subject to system fault and external disturbance under network environment. Firstly, based on the event-triggered sampling, a fault diagnosis observer is constructed to estimate both the system state and the system fault simultaneously, and a multi-objective constraint is established to guarantee the estimation accuracy. Based on the estimated system state and fault signal, a fault-tolerant controller is proposed to compensate the influence of occurred faults and maintain the system performance. The event-triggered scheme and the fault-tolerant controller are co-designed to guarantee the required performance of faulty system and reduce the consumption of communication resources. Finally, simulation results of an F-404 aircraft engine system are provided to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This note investigates the distributed estimation problem for continuous-time linear time-invariant (LTI) systems observed by a network of observers. Our starting point is a given LTI system whose full state vector we want to estimate, based on the measured output of the system. We will call this the observed system. Each observer in the network has access to only part of the output of the observed system, and communicates with its neighbors according to a given network graph. In this note, we recover the known result that if the observed system is observable and the network graph is a strongly connected digraph, then a distributed observer exists. Moreover, the estimation error can be made to converge to zero at any a priori given decay rate. Our approach leads to a relatively straightforward proof of this result, using the mirror of the balanced graph associated with the original network graph. The numerical design of our distributed observer is reduced to solving linear matrix inequalities. Each observer in the network has state dimension equal to that of the observed plant. [ABSTRACT FROM AUTHOR]

The classification of hazard is critical in industrial informatics, as it enhances early safety alerts, supports decision-making, and facilitates policy assessment. However, previous studies have generally neglected the temporal attributes of hazards, thereby constraining the effectiveness of models. This paper introduces a new model for hazard classification termed DLGM. DLGM represents a deep learning framework, with the structural parameters of grey models to encapsulate the hazard temporal attributes. To better accommodate the fluctuations of hazard series, a new grey model termed FSGM(1,1) equipped with Fourier series is proposed. Moreover, DLGM leverages a novel hierarchical feature fusion neural network (HFFNN) to optimize feature processing. Extensive experiments across three hazard themes involving 18 large-scale industrial processes have demonstrated the competitiveness of DLGM (e.g., it surpasses benchmark models such as Random Forest and BERT by approximately 2% in both accuracy and F1), the suitability of FSGM(1,1) (e.g., its mean absolute percentage error is less than 10% and mean squared error is below 0.02) and the effectiveness of HFFNN (e.g., it enhances the accuracy and F1 of DLGM by about 1%). [ABSTRACT FROM AUTHOR]

This article investigates functional interval observer based estimation methods for discrete-time switched descriptor systems with disturbances. A functional observer is presented using the $H_\infty$ formalism, and then a zonotope-based approach is applied to estimate its boundaries with a reduction of observer design constraints. To avoid the wrapping effect brought by the order reduction operations in the zonotope approach, reachability analysis is combined with the $H_\infty$ technique to design a switched functional interval observer with a better performance. Moreover, the relationship between the existing method, zonotope approach, and reachability analysis method is discussed in detail. The aforementioned methods are applied to an example for comparisons and to show their performance. [ABSTRACT FROM AUTHOR]

This paper investigates the finite-time adaptive neural control and almost disturbance decoupling problems for multi-input/multi-output (MIMO) nonlinear systems with disturbances and non-strict-feedback structure. In the design procedure of the adaptive controller, neural networks are employed to estimate the unknown nonlinearities and Young's inequality is utilized to cope with the disturbance terms derived from all subsystems. In order to characterize the disturbance attenuation performance of finite-time adaptive control, a criterion named finite-time almost disturbance decoupling is first developed. Under this criterion, an adaptive neural controller is designed via the backstepping method and the appropriate selection of Lyapunov function. It is revealed that the proposed controller can guarantee all variables of the closed-loop system are bounded, and the performance of finite-time almost disturbance decoupling is realized. Finally, a practical example is employed to validate the effectiveness of the designed controller. [ABSTRACT FROM AUTHOR]