State Estimation Research Articles

Pipe roughness calibration approach for water distribution network models using a nonlinear state observer

This paper introduces an online approach based on a nonlinear state observer (NSO) to calibrate the roughness of each pipe within a water distribution network (WDN) or a sector thereof. The NSO is designed to obtain the estimations of pipes' friction factors, which are then used to estimate the roughness. The core of the NSO is a dynamic WDN model formulated through a structured set of ordinary differential equations derived from fundamental physical principles and taking advantage of both graph theory and rigid water column theory. By applying a coordinate transformation, the WDN model is represented as a fully connected network of damped nonlinear oscillators, with each oscillator formulated as a Liénard system. This representation allows for estimating the friction factors for each pipe using only flow rate information. The proposed approach facilitates a continuous calibration when hydrodynamic data are readily accessible, which is a capability that empowers engineers to enhance, concurrently or proactively, the day-to-day operations of water distribution networks, such as control or diagnose tasks, whenever data are available. The results of numerical simulations are presented to illustrate the practical utility of the proposed method.

Prescribed-Time Trajectory Tracking Control for Unmanned Surface Vessels with Prescribed Performance Considering Marine Environmental Interferences and Unmodeled Dynamics

This article investigates a prescribed-time trajectory tracking control strategy for USVs considering marine environmental interferences and unmodeled dynamics. Firstly, a fixed-time extended state observer is introduced to quickly and accurately observe the compound perturbations including ocean disturbances and unmodeled dynamics. Subsequently, a prescribed-time prescribed performance function is utilized to obtain guaranteed transient performance within a predefined time. Finally, combining the fixed-time extended state observer, dynamic surface control technique, and prescribed-time prescribed performance control, a prescribed-time prescribed performance control strategy is developed to guarantee that the tracking errors converge to a predefined performance constraint boundary within a prescribed time. The effectiveness and superiority of the presented control strategy is verified by the simulation results.

Quality of life in older adults with mood states associated with bipolar disorder: A secondary analysis of the English longitudinal study of ageing data.

To investigate: (i) whether mood states associated with bipolar disorder are associated with poorer quality of life in older adults, and (ii) what are some of the predictors of quality of life in older adults with mood states associated with bipolar disorder. The authors completed a cross-sectional multilevel analysis of panel data from seven waves of The English Longitudinal Study of Ageing dataset. The main analysis included 567 participants who reported experiencing mood states associated with bipolar disorder. Some participants reported this in more than one wave, resulting in 835 observations of mood states associated with bipolar disorder across the seven waves. Quality of life was assessed using the Control, Autonomy, Self-realization, and Pleasure-19 (CASP-19) measure. The presence of mood states associated with bipolar disorder was significantly associated with poorer quality of life, even after controlling for multiple covariates (age, sex, social isolation, loneliness, alcohol use, education level, and economic status). Loneliness significantly predicted poorer quality of life in older adults with mood states associated with bipolar disorder. In contrast, higher educational attainment and being female predicted better quality of life in this group. Older adults with mood states associated with bipolar disorder have potentially worse quality of life compared to the general population, which may be partly driven by loneliness. This has ramifications for the support offered to this population and suggests that treatments should focus on reducing loneliness to improve outcomes.

A novel joint estimation for core temperature and state of charge of lithium-ion battery based on classification approach and convolutional neural network

The core temperature (CT) of lithium-ion batteries (LIBs) is crucial for effective thermal management, and is significantly influenced by the state of charge (SOC) among other battery parameters. However, directly measuring CT and SOC poses challenges. This study introduces a novel joint estimation method for CT and SOC, in which classification approach is pioneeringly employed through a two-dimensional convolutional neural network (2D-CNN). SOC is initially estimated and subsequently utilized as an input to refine CT estimation. This process is facilitated by two 2D-CNNs that incorporate measured indices such as current, terminal voltage, surface temperature, and ambient temperature into their inputs. Techniques including data augmentation, sequential optimization, and Gaussian filtering are employed to enhance estimation effect. The methodology is validated using LIB charge/discharge data derived from experiments based on the urban dynamometer driving schedule (UDDS). Results demonstrate that the root-mean-square errors (RMSEs) for CT and SOC estimation are below 0.267 °C and 0.7 %, respectively, across a broad ambient temperature range of −15 °C–55 °C. Consequently, this study confirms the effectiveness of using classification for CT and SOC estimation, illustrating that the proposed method can precisely estimate CT and SOC of LIBs across a wide temperature spectrum. The effectiveness of the deep learning method based on classification in estimating CT and SOC has been validated. Furthermore, a comparative analysis reveals that incorporating SOC as an input for CT estimation notably reduces the training time required by the method.

Adaptive sliding mode control with disturbance estimation for hydraulic actuator systems and application to rock drilling jumbo

For achieving high-performance control of hydraulic actuator systems, an adaptive sliding mode control with disturbance estimation algorithm is proposed and successfully applied to the hydraulic actuator systems of rock drilling jumbo. A switching extended state observer for hydraulic actuator systems is proposed, which combines the advantages of the linear extended state observer and the nonlinear extended state observer to estimate system disturbances more efficiently. The designed parameter adaptive law provides adaptivity to the system parameters, while the disturbance estimation algorithm can online estimate the unknown disturbances, such integration can enhance the adaptability and robustness of the sliding mode control algorithm. Rigorous simulation tests and practical application experiments are conducted to evaluate the performance of the proposed controller. The reliability of the designed parameter adaptive law and switching extended state observer is validated in detail in simulation studies. In the application experiments of rock drill jumbo, the results indicate the proposed controller is superior to sliding mode control with disturbance estimation and PID control. The average error of the developed controller is reduced by 58 %, 54 %, and 56 % compared to the sliding mode control with disturbance estimation controller, and 82 %, 78 %, and 79 % compared to the PID controller in tracking the low-frequency, high-frequency, and superimposed sinusoidal signals, respectively. Moreover, both the convergence of the uncertain parameters and the estimation of the unknown disturbances in the experiments are satisfactory. These experimental results adequately demonstrate the practical application value of the proposed controller.

Motion/force coordinated trajectory tracking control of nonholonomic wheeled mobile robot via LMPC-AISMC strategy

Nonholonomic constrained wheeled mobile robot (WMR) trajectory tracking requires the enhancement of the ground adaptation capability of the WMR while ensuring its attitude tracking accuracy, a novel dual closed-loop control structure is developed to implement this motion/force coordinated control objective in this paper. Firstly, the outer-loop motion controller is presented using Laguerre functions modified model predictive control (LMPC). Optimised solution condition is introduced to reduce the number of LMPC solutions. Secondly, an inner-loop force controller based on adaptive integral sliding mode control (AISMC) is constructed to ensure the desired velocity tracking and output driving torques by combining second-order nonlinear extended state observer (ESO) with the estimation of dynamic uncertainties and external disturbances during WMR travelling process. Then, Lyapunov stability theory is utilised to guarantee the consistent final boundedness of the designed controller. Finally, the system is numerically simulated and practically verified. The results show that the double-closed-loop control strategy devised in this paper has better control performance in terms of complex trajectory tracking accuracy, system resistance to strong interference and computational timeliness, and is able to realise effective coordinated control of WMR motion/force.

Justifying and Implementing Concept of Object-Oriented Observers of Thermal State of Rolling Mill Motors

Implementing the IIoT concept in industry involves the development and implementation of online systems monitoring the technical state of electromechanical equipment. This is achieved through the use of digital twins and digital shadows (object state observers). The tasks of mastering new rolling profiles and optimizing plate mill rolling programs require improved methods for calculating equivalent motor currents and torques. Known methods are generally based on calculations using smoothed load diagrams, which are assumed to be identical for the upper and lower main drive (UMD and LMD) rolls. These methods do not consider the differences in actual loads (currents or torques) in steady rolling states. Experiments performed on the 5000 plate mill have shown that due to speed mismatches, the UMD and LMD torques differ three times or more. This causes overheating of the more heavily loaded motor, insulation life reduction, and premature failure. Therefore, the problem of developing and implementing techniques for monitoring the load and thermal regimes of motors using digital observers is relevant. The paper’s contribution is the first justification of the concept of object-oriented digital shadows. They are developed for specific classes of industrial units using open-source software. This research justifies a methodology for assessing motor load and temperature by processing arrays of motor currents or torques generated during rolling. An equivalent load observer and a temperature observer were proposed and implemented using Matlab-Simulink resources. The algorithm was implemented on the mill 5000 and tuned using an earlier-developed virtual commissioning methodology with digital twins. Thermal regimes were studied, proving that torque alignment ensures equal motor temperatures. The proposed considerations contribute to the development of the theory and practice for creating digital systems to monitor the technical condition of electromechanical and mechatronic systems and implementing the Industry 4.0 concept at industrial enterprises.

Backstepping control of hypersonic vehicles under input and output constraints with model uncertainties and disturbances

In this paper, considering the physical restriction of hypersonic vehicle systems in a practical flight environment, the altitude tracking problem is studied with input magnitude, input rate, output constraints, system parameter uncertainties and disturbances, which expands the traditional considerations of control methods in the hypersonic vehicle field. The hyperbolic tangent function is employed to address the system input constraints. Furthermore, the incorporation of barrier Lyapunov function serves to ensure that the system output is limited in the prescribed limitations. In particular, two extended state observers are integrated to eliminate errors from model uncertainties and disturbances. Based on the effective combination of these techniques and auxiliary variable designs, a novel backstepping controller is proposed to simultaneously handle problems of input and output constraints, model uncertainties and disturbances. The closed-loop system stability is demonstrated using the Lyapunov theory. Simulation results show the effectiveness and superiority of the proposed controller compared with the traditional backstepping controller.

Bosanac v Federal Commissioner of Taxation: The Future of the Presumptions of Resulting Trust and Advancement

This case note examines the decision of the High Court of Australia in Bosanac v Federal Commissioner of Taxation. Part II provides a case summary. Part III considers the future of the presumptions of resulting trust and advancement following Bosanac. Part IV discusses the judicial references in Bosanac to the presumptions being ‘weak’, and analyses whether these were an observation of the current state of the law or represent a change to it. Part V reviews the Court’s clarification of an earlier case concerning the application of the presumption of resulting trust to married couples, Trustees of the Property of Cummins (a bankrupt) v Cummins. The case note concludes that Bosanac is significant because it has paved the way for the future development of the presumptions, developed the law by weakening the presumptions of resulting trust and advancement, and helpfully clarified the effect of Cummins.

Servo Control of a Current-Controlled Attractive-Force-Type Magnetic Levitation System Using Fractional-Order LQR Control

Recent research on fractional-order control laws has introduced the fractional calculus concept into the field of control engineering. As described herein, we apply fractional-order linear quadratic regulator (LQR) control to a current-controlled attractive-force-type magnetic levitation system, which is a strongly nonlinear and unstable system, to investigate its control performance through experimentation. First, to design the controller, a current-controlled attractive-force-type magnetic levitation system expressed as an integer-order system is extended to a fractional-order system expressed using fractional-order derivatives. Then, target value tracking control of a levitated object is achieved by adding states, described by the integrals of the deviation between the output and the target value, to the extended system. Next, a fractional-order LQR controller is designed for the extended system. For state-feedback control, such as fractional-order servo LQR control, which requires the information of all states, a fractional-order state observer is configured to estimate fractional-order states. Simulation results demonstrate that fractional-order servo LQR control can achieve equilibrium point stabilization and enable target value tracking. Finally, to verify the fractional-order servo LQR control effectiveness, experiments using the designed fractional-order servo LQR control law are conducted with comparison to a conventional integer-order servo LQR control.

Robust anti-disturbance interval type-2 fuzzy control for interconnected nonlinear PDE systems via conjunct observer

This paper investigates a robust anti-disturbance interval type-2 (IT2) fuzzy control for interconnected nonlinear partial differential equation (PDE) systems subject to parameter uncertainties by the conjunct observer. First, an IT2 fuzzy model is adopted to remodel the target system. Second, a state observer with mismatched premise variables is constructed to solve the problem that the original system and the observer do not share a uniform premise variable. Moreover, a disturbance observer is designed to estimate the unknown external disturbances, which can be modeled by exogenous PDE systems. Then, utilizing the conjunct observation information, an anti-disturbance IT2 fuzzy control strategy is proposed to attenuate the effect of disturbances on the system performance while ensuring that the closed-loop system is stable. Finally, simulation results verify the effectiveness of the proposed method.

Fixed-time fault-tolerant attitude tracking control for UAV based on fixed-time extended state observer

PurposeTo improve the robustness of carrier-based unmanned aerial vehicle (UAV) with actuator faults attitude tracking control system, this paper aims to propose a fixed-time backstepping (FXTBSC) fault-tolerant control based on a fixed-time extended state observer.Design/methodology/approachA fixed-time extended state observer (FXTESO) is designed to estimate the total disturbance including nonlinear, coupling, actuator faults and external disturbances. The integration of backstepping control and fixed-time technology ensures fixed-time convergence.FindingsThe simulation results of tracking the desired attitude angle show that the anti-interference, fault tolerance and tracking accuracy of FXTBSC-FXTESO are better than the BSC-ESO control method.Originality/valueDifferent from the traditional BSC-ESO, the convergence speed and convergence accuracy of FXTBSC-FXTESO proposed in this paper are better than conventional extended state observer. And the fixed time controller has the advantages of high tracking accuracy, fault tolerance and anti-interference ability.

Prescribed-time and output feedback stabilization of heat equation with an intermediate-point heat source and boundary control

This paper addresses the problem of prescribed-time stabilization for the heat equation with an interior point heat source under output feedback. The study employs a two-step backstepping approach along with time-varying feedback techniques. A prescribed-time state observer and an observer-based boundary control law are developed. The proposed method extends the applicability of the prescribed time-stabilizing algorithm to heat equations with non-strict feedback terms. Numerical simulations validate its effectiveness.

State Estimation of Switched Time-Delay Complex Networks With Strict Decreasing LKF.

State estimation issue is investigated for a switched complex network (CN) with time delay and external disturbances. The considered model is general with a one-sided Lipschitz (OSL) nonlinear term, which is less conservative than Lipschitz one and has wide applications. Adaptive mode-dependent nonidentical event-triggered control (ETC) mechanisms for only partial nodes are proposed for state estimators, which are not only more practical and flexible but also reduce the conservatism of the results. By using dwell-time (DT) segmentation and convex combination methods, a novel discretized Lyapunov-Krasovskii functional (LKF) is developed such that the value of LKF at switching instants is strict monotone decreasing, which makes it easy for nonweighted L2 -gain analysis without additional conservative transformation. The main results are given in the form of linear matrix inequalities (LMIs), by which the control gains of the state estimator are designed. A numerical example is given to illustrate the advantages of the novel analytical method.

Mechanism and Data-Driven Dual Estimation of Coupling Hydraulic-Thermal States for Steam Heating Networks Considering Multi-Time-Scale Characteristics.

For a steam heating network (SHN), which is one of the most important parts of the integrated energy systems in an industrial park, it is essential to provide accurate and reliable estimation of hydraulic and thermal states, to ensure its operational safety and economy. Considering two different time-scale characteristics of the hydraulic and thermal processes in the SHN, this article proposes a mechanism and data-driven dual estimation method for the coupling hydraulic-thermal dynamic states. Due to the fact that the existing coupled estimation methods with one time scale may suffer from heavy computational burden, a data-driven dual sequential acrlong DSE method of the SHN is proposed based on the two-time scales, in which the dynamics of slow thermal states can also be captured when performing the acrlong SE of the fast hydraulic process. Furthermore, to improve the computational and communicational efficiency, a distributed interaction strategy based on the nodal transformation matrix is designed for large-scale steam systems. To verify the effectiveness of the proposed method, a single pipeline system and two real-world industrial superheated steam networks are employed. Compared to other state-of-the-art methods, the proposed method achieves the best tradeoff between the estimation accuracy and computational efficiency.

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.

Predefined-Time Consensus Tracking Control for Multiagent System With Channel Fading.

This article proposes a predefined-time leader-following consensus control scheme for a second-order multiagent system (MAS) whose communication network is subject to channel fading. New distributed observers are designed to achieve prescribed-time leader's states estimation under undirected graph and digraph over faded communication channel. Then, a new adaptive dynamic surface predefined-time control is developed for the elimination of the mismatched disturbances stemmed from estimation error and achieving practical predefined-time leader-following consensus. It is proved that the developed control approach achieves predefined-time consensus tracking. This article's contribution is to propose novel distributed observers to remove the influence of channel fading and estimate leader's states under undirected graph and digraph within prescribed time and develop a novel predefined-time control to achieve predefined-time consensus tracking over fading channel. A simulation example verifies that the designed control scheme is effective.

Improving Subseasonal Soil Moisture and Evaporative Stress Index Forecasts through Machine Learning: The Role of Initial Land State versus Dynamical Model Output

Abstract The effect of machine learning and other enhancements on statistical–dynamical forecasts of soil moisture (0–10 and 0–100 cm) and a reference evapotranspiration fraction [evaporative stress index (ESI)] on subseasonal time scales (15–28 days) are explored. The predictors include the current and past land surface conditions and dynamical model hindcasts from the Subseasonal to Seasonal Prediction project (S2S). When the methods are enhanced with machine learning and other improvements, the increases in skill are almost exclusively coming from predictors drawn from observations of current and past land surface states. This suggests that operational S2S flash drought forecasts should focus on optimizing use of information on current conditions rather than on integrating dynamically based forecasts, given the current state of knowledge. Nonlinear machine learning methods lead to improved skill over linear methods for soil moisture but not for ESI. Improvements for both soil moisture and ESI are realized by increasing the sample size by including surrounding grid points in training and increasing the number of predictors. In addition, all the improvements in the soil moisture forecasts predominantly impact soil moistening rather than soil drying—i.e., prediction of conditions moving away from drought rather than into drought—especially when the initial soil state is drier than normal. The physical reasons for the nonlinear machine learning improvements are also explored. Significance Statement Rapidly intensifying droughts pose extra challenges for predictability. Here, dynamical forecast model output is combined with nonlinear machine learning methods to improve forecasts of rapid changes in soil moisture and the evaporative stress index (ESI).

Human-in-the-Loop Consensus Control for Multiagent Systems With External Disturbances.

In this article, the human-in-the-loop leader-follower consensus control problem is addressed for multiagent systems (MASs) with unknown external disturbances. A human operator is deployed to monitor the MASs' team by transmitting an execution signal to a nonautonomous leader in response to any hazard detected, with the control input of the leader unknown to all followers. For each follower, a full-order observer, in which the observer error dynamic system decouples the unknown disturbance input, is designed for asymptotic state estimation. Then, an interval observer is constructed for the consensus error dynamic system, where the unknown disturbances and control inputs of its neighbors and its disturbance are treated as unknown inputs (UIs). To process the UIs, a new asymptotic algebraic UI reconstruction (UIR) scheme is proposed based on the interval observer, and one of the significant features of the UIR is the capacity to decouple the control input of the follower. The subsequent human-in-the-loop asymptotic convergence consensus protocol is developed by applying an observer-based distributed control strategy. Finally, the proposed control scheme is validated through two simulation examples.

Antisaturation Extended State Observer for Speed Predictive Control of Servo System

Antisaturation Extended State Observer for Speed Predictive Control of Servo System