State Estimation Research Articles

Processes monitoring using adaptive confidence limit based on T-S fuzzy model and Luenberger observer

In hazard-sensitive processes, the monitoring upsets and malfunctions correctly is an important challenge to operation safely and enhance the performance. Conventional process monitoring frequently assumes that process data follow only one Gaussian distribution, which generates a constant confidence limit and hence produces a high number of false alarms. However, in fact, industrial processes usually include various operating modes. To ovoid this drawback, the suggested approach employs an adaptive confidence limit (ACL) when a substantial number of false alarms are created. The fundamental concept underlying this study is to extract internally several local linear sub-modes of the monitored variables. In typical operating circumstances, the Gaussian mixture model (GMM) is utilized to extract several local linear sub-modes, followed by fuzzy linearization using the Takagi-Sugeno model, thereafter a bank of Luenberger observers to construct the residual spaces. An abnormal event is detected when the squared prediction error (SPE) is too great or exceeds the adaptive threshold designed to prevent the false alarms. Furthermore, an enhanced contribution plots is effectively used to identify the defective variable.

Antisaturation Extended State Observer for Speed Predictive Control of Servo System

Antisaturation Extended State Observer for Speed Predictive Control of Servo System

Oxygen excess ratio feedforward control based on ESO for PEMFC in DC off-grid hydrogen production systems

Oxygen excess ratio (OER), which is influenced by the air flow input to the cathode, is one of the important factors affecting the performance of fuel cell system. Insufficient oxygen supply will result in a sharp drop in output voltage, while an excessive supply will bring about a large parasitic power load on the air compressor, reducing the net output power of the system and operating efficiency. Both insufficient and excess oxygen supply can adversely affect the operating life of the proton exchange membrane fuel cell (PEMFC). Due to the serious nonlinear, hysteresis, uncertainty and interference in the process of OER control, this paper proposes an OER feedforward control based on extended state observer (ESO) for a PEMFC auxiliary system with the ESO to predict the total disturbance to improve the dynamic response of the system with large time delay utilizing terminal sliding mode control (TSMC). The control strategy is divided into two stages. The first stage constructs an ESO to realize real-time observation of the state variables required for the internal operation of the auxiliary machine system and the PEMFC, and yields the air compressor voltage control signal through terminal sliding mode control. In the second stage, combined with V/f control for the compressor, the transmitted air flow is adjusted, ultimately achieving the goal of changing the OER of the PEMFC and improving operating efficiency. The simulation results show that the proposed control strategy has good anti-interference capabilities, a faster response, and less overshoot. The gas flow transmission in the gas supply system is more robust, the PEMFC operation is safer, and its lifespan is extended.

Survival Probability, Particle Imbalance, and Their Relationship in Quadratic Models

We argue that the dynamics of particle imbalance in quadratic fermionic models is, for the majority of initial many-body product states in the site occupation basis, virtually indistinguishable from the dynamics of survival probabilities of single-particle states. We then generalize our statement to a similar relationship between the non-equal time and space density correlation functions in many-body states, and the transition probabilities of single-particle states at nonzero distances. Finally, we study the equal-time connected density–density correlation functions in many-body states, which exhibit certain qualitative analogies with the survival and transition probabilities of single-particle states. Our results are numerically tested for two paradigmatic models of single-particle localization: the 3D Anderson model and the 1D Aubry–André model. This work gives an affirmative answer to the question of whether it is possible to measure features of single-particle survival and transition probabilities by the dynamics of observables in many-body states.

Path following of underactuated surface vessels based active disturbance rejection control considering lateral drift

To address the challenges posed by unmodeled dynamics, lateral drift, external disturbances, and the difficulty in measuring velocities in underactuated surface vessels (USVs) motion systems, an active disturbance rejection control (ADRC) with two linear extended state observers (LESO) is proposed for USV path following. Firstly, we employ the Backstepping technique to create a virtual heading angle while estimating lateral drift and unmodeled dynamics using LESO. Subsequently, the ADRC algorithm is employed to control the heading angle. Since measuring the velocities of USV is challenging, velocity observers are introduce to estimate surge and sway velocities. Finally, we utilize the MMG model with high precision to track straight-line and curved paths, respectively. Simulation results validate that our developed controller accurately follows the reference path even in the presence of disturbances, affirming the effectiveness of our proposed control strategy.

Unmanned Floating Object Transportation Applying Multiple Dynamic Positioning Tugboats: A Novel Cooperative Control Algorithm

Abstract To promote the automation and intelligence of marine floating object transportation, this paper proposes a cooperative consensus control algorithm for multiple unmanned dynamic positioning (DP) tugboats to transport an unactuated floating structure object using cables. The major concept of present algorithm is to implement a leader-follower formation strategy among distributed tugboats, with the aim of effectively maneuvering the object by adjusting the relative position between the virtual leader and the object in a reasonable manner. A cooperative consensus controller is integrated into the feedback controller of each tugboat local DP system in order to facilitate cooperative coordination among tugboats in a formation. A customized DP system, which incorporates the radial basis function neural network supervisor control into the DP controller design and introduces nonlinear disturbance observer into the low-frequency motion state observer design, is proposed to address cable tension disturbances, and thus, the tracking performance of the DP tugboat is improved. Full-scale nonlinear time domain simulations are performed under variable environmental conditions, verifying the effectiveness of the proposed automatic towing control scheme.

Addressing Launch and Deployment Uncertainties in UAVs with ESO-Based Attitude Control

This paper describes the design and implementation of a novel three-axis attitude control autopilot scheme for tube-launched, air-deployed UAVs. In early flight tests, various factors, such as model uncertainties during launch, aerodynamic uncertainties, geometric parameter changes during deployment, and significant uncertainties in booster rocket installation, exceeded the control capabilities of the attitude autopilot, causing flight instability. In order to address these issues, a numerical simulation model of the full launch process considering deviations was established based on early flight tests. A cascade attitude controller was then designed using an extended state observer (ESO), and the boundedness of control errors under unknown bounded disturbances was theoretically proven, providing requirements for the parameter tuning of the cascade controller. Comparative experiments and a second flight test both demonstrate that the ESO-based cascade attitude controller exhibits strong feedforward disturbance compensation under high-uncertainty conditions, effectively achieving stable control within the flight envelope.

Composite sliding mode control for cyber‐physical systems with multi‐source disturbances and false data injection attack

AbstractThis paper investigates the sliding mode control problem for cyber‐physical systems subject to multi‐source disturbances and actuator false data injection attacks simultaneously. Firstly, a series of extended state observers are designed to estimate the unknown multi‐source disturbances. For the actuator false data injection attacks, a sliding mode observer is designed to online estimate the values of attacks. Then, by introducing the disturbance and attack estimations, a dynamic sliding manifold and a composite sliding mode controller are constructed. Under the proposed approach, the unknown disturbances and false data injection attacks can be timely estimated and compensated, which can improve the disturbance rejection ability and guarantee security of the cyber‐physical system. Lyapunov theory is utilized to prove the stability of the closed‐loop system. Finally, simulations of both the numerical example and application to a power converter system demonstrate the effectiveness and superiority of the proposed composite sliding mode control approach.

Distributed control for DC microgrids with cyber attacks and constraints: A fault-tolerant model predictive controller

Communication security issues pose severe challenges to stability of microgrid system. This paper designs a cooperative control method based on fault-tolerant distributed model predictive control (DMPC) for DC microgrid against possible false information injection (FDI) attacks and communication constraints problems. First, a state variable is defined based on the voltage and current information, thereby deriving a system model of the microgrid under the cyber-physical framework. Then, since a combined uncertainty term consisting of attacks and disturbances exists in the constructed system model, an expanded state observer (ESO) is proposed to observe the uncertainty term. Further, based on the constructed system model and the designed ESO, a fault-tolerant distributed model predictive controller is designed. In the controller, DMPC is utilized to constrain the communication information and ensure the boundedness of the communication state and physical state. Simulation results indicate that the proposed DMPC-based distributed control method can achieve stable voltage regulation and accurate current sharing, and maintain smooth operation even under FDI attack, which verifies the feasibility for the proposed method.

Cascaded safety trajectory tracking control for a hovercraft with double-loop prescribed performance and state constraints

ABSTRACT This paper presents a cascaded safety trajectory tracking control scheme for hovercraft, addressing dump disturbances with a novel double-loop strategy that enforces prescribed performance and state constraints. Initially, an Adaptive Prescribed-Time Extended State Observer (APESO) is meticulously crafted to estimate lumped disturbances accurately. Subsequent to this, virtual velocity control laws are established through Double Power Appointed-Time Performance Function (DPATPF) combined with an Asymmetric Barrier Lyapunov Function (ABLF), streamlining the position error into a more manageable, equivalent unconstrained control system format. To effectively manage velocity safety constraints, a Unified Barrier Function (UBF) is introduced, ensuring that hovercraft velocities adhere to asymmetric safety boundary. The entire system state is rigorously ultimately uniformly bounded, in strict compliance with cascade control theories and Lyapunov stability criteria. Through simulation, we demonstrate the superior performance and effectiveness of the proposed control strategy, showcasing its potential for enhancing hovercraft navigational safety and reliability.

ОБЗОР СЕЛЕВОЙ ОПАСНОСТИ ДОЖДЕВОГО ГЕНЕЗИСА ПО ГОРНОЙ ТЕРРИТОРИИ РЕСПУБЛИКИ КАЗАХСТАН ЗА 2023 ГОД

The review was prepared based on the results of monitoring the debris-flow hazard caused by rainfall in the mountainous territory of the Republic of Kazakhstan for 2023. Monitoring was carried out for debris-flow hazard areas of Almaty, Zhetysu, Zhambyl, Turkestan, Mangistau, Abay and East Kazakhstan regions. The Ile, Kungey, Teriskey, Zhetysu, Kyrgyz and Talas Alatau, Ugam, Mangystau, Saur, Tarbagatai and Kazakhstan Altai mountains ridges are debris-flow hazard areas. The Review contains a map of debris-flow hazard on the territory of the Republic of Kazakhstan, information on the hydrometeorological situation and debris f low occurrences during the 2023 debris-flow-hazardous period. In the monitoring process information from the state observation network of the RSE «Kazhydromet» in operational mode, synoptic and numerical weather forecasts of the RSE «Kazhydromet» was used, etc.

Adaptive Neural Control for Hysteretic Nonlinear Systems With Hysteresis Neural Direct Inverse Compensator and Its Application.

Aiming at high precision control for a class of hysteretic nonlinear systems, a new hysteresis direct inverse compensator-based adaptive output feedback control scheme is designed in this article. First, a novel long short-term memory neural network (LSTMNN)-based hysteresis inverse compensator is established to compensate the asymmetric hysteresis nonlinearity, where the LSTMNN is used as the prediction mechanism for model operator weights, rather than the overall mapping of hysteresis input and output. Second, by designing the modified high-gain K -Filter states observer and the error transformed function, the unmeasurable states are estimated with arbitrarily small estimation error and the prespecified tracking performance is achieved. Lastly, the biconical dielectric elastomer actuator (DEA) motion platform is constructed. Then, the effectiveness of the proposed LSTMNN-based hysteresis inverse compensator and control scheme are verified on the experimental platform. The experimental results illustrate the effectiveness and advantages of proposed control scheme.

QCD equation of state and thermodynamic observables from computationally minimal Dyson-Schwinger equations

We study the QCD equation of state and other thermodynamic observables including the isentropic trajectories and the speed of sound. These observables are of eminent importance for the understanding of experimental results in heavy ion collisions and also provide a QCD input for studies of the timeline of heavy-ion-collisions with hydrodynamical simulations. They can be derived from the quark propagator whose gap equation is solved within a minimal approximation to the Dyson-Schwinger equations (DSEs) of QCD at finite temperature and density. This minimal approximation aims at a combination of computational efficiency and simplification of the truncation scheme while maintaining quantitative precision. This minimal DSE scheme is confronted and benchmarked with results for correlation functions and observables from lattice QCD at vanishing density and quantitative functional approaches at finite density. Published by the American Physical Society 2024

Tax evasion, education and shadow economy

The aim of the paper is to evaluate the influence of education on tax evasion. This study proposes a theoretical model, in which an agent can choose to allocate his labor effort between two production technologies. Higher levels of education lead to more skilled and specialized workers, and the tax authority can observe the use of advanced tools and equipment. The main result is that an increase in the general level of education leads to a labor effort substitution effect, showing that higher education can reduce tax evasion and the size of the shadow economy. Both static and dynamic estimation techniques are employed to test the theoretical findings, using a sample of 133 countries, over the period 2001–2020. The empirical analysis confirms the significant negative association between education and shadow economy.

Prescribed-time adaptive state observer approach for distributed output-feedback formation tracking of networked uncertain underactuated surface vehicles

Prescribed-time adaptive state observer approach for distributed output-feedback formation tracking of networked uncertain underactuated surface vehicles

High-Performance Position Estimation Method Based on the Error-Corrected State Observer and Its Application on Permanent Magnet Synchronous Machine

High-Performance Position Estimation Method Based on the Error-Corrected State Observer and Its Application on Permanent Magnet Synchronous Machine

Observer-based adaptive memory event-triggered consensus tracking control for high-speed train under DoS attacks

AbstractThis paper focuses on the event-triggered consensus tracking of high-speed trains under denial-of-service (DoS) attacks. A novel anti-disturbance control strategy is proposed, based on an adaptive memory state observer and a disturbance observer. A memory is introduced to buffer system output information which enhancing the precision of the state observer, and the gust disturbance during the high-speed train operation is estimated by the disturbance observer. The Linear Matrix inequality technique is used to obtain the observer feedback coefficient and the controller gain, and the Lyapunov theory is employed to demonstrate the controller’s consistency tracking performance. Applying the memory to the event-triggered mechanism, a new adaptive memory event-triggered scheme is designed to better ensure system performance and effectively prevent the occurrence of Zeno behavior. To achieve precise identification of DoS attacks in the event-triggered environment, a new detection algorithm is proposed. Finally, simulation examples are used to validate the effectiveness and practicality of the proposed scheme.

Noise-Adaptive State Estimators with Change-Point Detection.

Aiming at tracking sharply maneuvering targets, this paper develops novel variational adaptive state estimators for joint target state and process noise parameter estimation for a class of linear state-space models with abruptly changing parameters. By combining variational inference with change-point detection in an online Bayesian fashion, two adaptive estimators-a change-point-based adaptive Kalman filter (CPAKF) and a change-point-based adaptive Kalman smoother (CPAKS)-are proposed in a recursive detection and estimation process. In each iteration, the run-length probability of the current maneuver mode is first calculated, and then the joint posterior of the target state and process noise parameter conditioned on the run length is approximated by variational inference. Compared with existing variational noise-adaptive Kalman filters, the proposed methods are robust to initial iterative value settings, improving their capability of tracking sharply maneuvering targets. Meanwhile, the change-point detection divides the non-stationary time sequence into several stationary segments, allowing for an adaptive sliding length in the CPAKS method. The tracking performance of the proposed methods is investigated using both synthetic and real-world datasets of maneuvering targets.

Model following adaptive control for nodes in complex dynamical network via the state observer of links

Geometrically, a complex dynamical network (CDN) can be regarded as the interconnected system composed of the node subsystem (NS) and the link subsystem (LS) coupled with each other. Guided by this idea, in order to achieve the goal of each node following asymptotically its own reference target in a CDN, this paper investigates the model following adaptive control (MFAC) problem of NS via the dynamics of links, which implies that the LS plays the important dynamic auxiliary role in the MFAC realization of nodes. Meanwhile, we focus on the condition that the links state information is unavailable, due to sensor practical application and measurement cost constraints. To obviate this restriction, we construct the asymptotical state observer for the LS. Next, to achieve the control goal of this paper, an appropriate Lyapunov candidate function is selected, by which the MFAC scheme for NS is synthesized based on the state observer of LS. Finally, the simulation example is performed to demonstrate the theoretical results in this paper.

Investigation of full-charm and full-bottom pentaquark states

The continuous advancement of experimental techniques and investigations has led to observations of various exotic states in particle physics. Each addition to this family of states not only raises expectations for future discoveries but also focuses attention on such potential new states. Building upon this motivation and inspired by recent observations of various traditional and exotic particles containing an increased number of heavy quarks, our study provides a spectroscopic search for potential pentaquark states with spin-parity 32-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\frac{3}{2}^-$$\\end{document} and composed entirely of charm or bottom quarks. We predict the masses for full-charm and full-bottom pentaquark states as m=7628±112\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$m = 7628 \\pm 112$$\\end{document} MeV and m=21,982±144\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$m = 21,982 \\pm 144$$\\end{document} MeV, respectively. We also compute the current couplings of these states to vacuum, which are main inputs in investigations of their various possible decays.