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Full- and reduced-order observers have been used in many engineering applications, particularly for energy systems. Applications of observers to energy systems are twofold: (1) the use of observed variables of dynamic systems for the purpose of feedback control and (2) the use of observers in their own right to observe (estimate) state variables of particular energy processes and systems. In addition to the classical Luenberger-type observers, we will review some papers on functional, fractional, and disturbance observers, as well as sliding-mode observers used for energy systems. Observers have been applied to energy systems in both continuous and discrete time domains and in both deterministic and stochastic problem formulations to observe (estimate) state variables over either finite or infinite time (steady-state) intervals. This overview paper will provide a detailed overview of observers used for linear and linearized mathematical models of energy systems and review the most important and most recent papers on the use of observers for nonlinear lumped (concentrated)-parameter systems. The emphasis will be on applications of observers to renewable energy systems, such as fuel cells, batteries, solar cells, and wind turbines. In addition, we will present recent research results on the use of observers for distributed-parameter systems and comment on their actual and potential applications in energy processes and systems. Due to the large number of papers that have been published on this topic, we will concentrate our attention mostly on papers published in high-quality journals in recent years, mostly in the past decade.

In this study, an observer based control strategy is proposed for load frequency control (LFC) scheme against cyber-attack uncertainties. Most of research work focused on detection scheme or delay estimation scheme in presence of cyber-attack vulnerabilities and paid less attention on design of counteractive robust control scheme for LFC problem. Thus, observer based control scheme is designed here and provides robust performance against unknown input attack uncertainty and communication time-delay attack uncertainty. The generalized extended state observer (GESO) is used not only for state and disturbance estimation but also for disturbance rejection of the system. The said observer ensures accurate estimation of the actual states leading to convergence of estimation error to zero. So, the observer based linear quadratic regulator (LQR) is used to regulate the closed-loop damping ratio against cyber-attack uncertainty. In addition to fast response in terms of settling time and reduced over/undershoots, the proposed control scheme satisfactorily compensates the cyber-attack uncertainties in power system cyber physical networks and also compared with existing traditional PI and PID controllers. The simulation results demonstrate the robustness in terms of stability and effectiveness in terms of system security with proposed controller when subjected to cyber-attack uncertainties and load disturbances.

An accurate dynamic model of a robot is fundamentally important for a control system, while uncertainties residing in the model are inevitable in a physical robot system. The uncertainties can be categorized as internal disturbances and external disturbances in general. The former may include dynamic model errors and joint frictions, while the latter may include external payloads or human-exerted force to the robot. Disturbance observer is an important technique to estimate and compensate for the uncertainties of the dynamic model. Different types of disturbance observers have been developed to estimate the lumped uncertainties so far. In this paper, we conducted a brief survey on five typical types of observers from a perspective of practical implementation in a robot control system, including generalized momentum observer (GMO), joint velocity observer (JVOB), nonlinear disturbance observer (NDOB), disturbance Kalman filter (DKF), and extended state observer (ESO). First, we introduced the basics of each observer including equations and derivations. Two common types of disturbances are considered as two scenarios, that is, constant external disturbance and time-varying external disturbance. Then, the observers are separately implemented in each of the two simulated scenarios, and the disturbance tracking performance of each observer is presented while their performance in the same scenario has also been compared in the same figure. Finally, the main features and possible behaviors of each type of observer are summarized and discussed. This survey is devoted to helping readers learn the basic expressions of five typical observers and implement them in a robot control system.

Objectives To investigate the intra- and inter-rater reliability of the total radiomics quality score (RQS) and the reproducibility of individual RQS items’ score in a large multireader study. Methods Nine raters with different backgrounds were randomly assigned to three groups based on their proficiency with RQS utilization: Groups 1 and 2 represented the inter-rater reliability groups with or without prior training in RQS, respectively; group 3 represented the intra-rater reliability group. Thirty-three original research papers on radiomics were evaluated by raters of groups 1 and 2. Of the 33 papers, 17 were evaluated twice with an interval of 1 month by raters of group 3. Intraclass coefficient (ICC) for continuous variables, and Fleiss’ and Cohen’s kappa ( k ) statistics for categorical variables were used. Results The inter-rater reliability was poor to moderate for total RQS (ICC 0.30–055, p  < 0.001) and very low to good for item’s reproducibility ( k  − 0.12 to 0.75) within groups 1 and 2 for both inexperienced and experienced raters. The intra-rater reliability for total RQS was moderate for the less experienced rater (ICC 0.522, p  = 0.009), whereas experienced raters showed excellent intra-rater reliability (ICC 0.91–0.99, p  < 0.001) between the first and second read. Intra-rater reliability on RQS items’ score reproducibility was higher and most of the items had moderate to good intra-rater reliability ( k  − 0.40 to 1). Conclusions Reproducibility of the total RQS and the score of individual RQS items is low. There is a need for a robust and reproducible assessment method to assess the quality of radiomics research. Clinical relevance statement There is a need for reproducible scoring systems to improve quality of radiomics research and consecutively close the translational gap between research and clinical implementation. Key Points • Radiomics quality score has been widely used for the evaluation of radiomics studies. • Although the intra-rater reliability was moderate to excellent, intra- and inter-rater reliability of total score and point-by-point scores were low with radiomics quality score. • A robust, easy-to-use scoring system is needed for the evaluation of radiomics research.

Purpose This retrospective work aims to evaluate the possible impact on intra‐ and inter‐observer variability, contouring time, and contour accuracy of introducing a pelvis computed tomography (CT) auto‐segmentation tool in radiotherapy planning workflow. Methods Tests were carried out on five structures (bladder, rectum, pelvic lymph‐nodes, and femoral heads) of six previously treated subjects, enrolling five radiation oncologists (ROs) to manually re‐contour and edit auto‐contours generated with a male pelvis CT atlas created with the commercial software MIM MAESTRO. The ROs first delineated manual contours (M). Then they modified the auto‐contours, producing automatic‐modified (AM) contours. The procedure was repeated to evaluate intra‐observer variability, producing M1, M2, AM1, and AM2 contour sets (each comprising 5 structures × 6 test patients × 5 ROs = 150 contours), for a total of 600 contours. Potential time savings was evaluated by comparing contouring and editing times. Structure contours were compared to a reference standard by means of Dice similarity coefficient (DSC) and mean distance to agreement (MDA), to assess intra‐ and inter‐observer variability. To exclude any automation bias, ROs evaluated both M and AM sets as “clinically acceptable” or “to be corrected” in a blind test. Results Comparing AM to M sets, a significant reduction of both inter‐observer variability (p < 0.001) and contouring time (‐45% whole pelvis, p < 0.001) was obtained. Intra‐observer variability reduction was significant only for bladder and femoral heads (p < 0.001). The statistical test showed no significant bias. Conclusion Our atlas‐based workflow proved to be effective for clinical practice as it can improve contour reproducibility and generate time savings. Based on these findings, institutions are encouraged to implement their auto‐segmentation method.

In traditional observer designs, simultaneous state estimation, unknown input reconstruction (UIR), and measurement noise reconstruction have been fully discussed. However, in distributed observer, this remains to be investigated, and few papers deal with this problem. This paper designs a reduced-order distributed unknown input observer (DUIO) to offer the asymptotic convergent estimations of both the states, the unknown input (UI) and measurement noise (MN), for the uncertain target system with UI and each sensor node with MN. Firstly, by introducing an auxiliary output, the local system in every node is transformed into a system whose output contains no MN. Secondly, for the transformed local system, a distributed reduced-order observer produces asymptotically convergent state estimations. Thirdly, based on the interval observer and state estimation, a UIR is designed, and the UIR can estimate the real UI asymptotically. Moreover, the UIR decouples the control input. Finally, a simulation example is given to illustrate the effectiveness and advantages of the proposed method.

Background Simulation is widely used in health professional education. The convention that learners are actively involved may limit access to this educational method. The aim of this paper is to review the evidence for learning methods that employ directed observation as an alternative to hands-on participation in scenario-based simulation training. We sought studies that included either direct comparison of the learning outcomes of observers with those of active participants or identified factors important for the engagement of observers in simulation. We systematically searched health and education databases and reviewed journals and bibliographies for studies investigating or referring to observer roles in simulation using mannequins, simulated patients or role play simulations. A quality framework was used to rate the studies. Methods We sought studies that included either direct comparison of the learning outcomes of observers with those of active participants or identified factors important for the engagement of observers in simulation. We systematically searched health and education databases and reviewed journals and bibliographies for studies investigating or referring to observer roles in simulation using mannequins, simulated patients or role play simulations. A quality framework was used to rate the studies. Results Nine studies met the inclusion criteria. Five studies suggest learning outcomes in observer roles are as good or better than hands-on roles in simulation. Four studies document learner satisfaction in observer roles. Five studies used a tool to guide observers. Eight studies involved observers in the debrief. Learning and satisfaction in observer roles is closely associated with observer tools, learner engagement, role clarity and contribution to the debrief. Learners that valued observer roles described them as affording an overarching view, examination of details from a distance, and meaningful feedback during the debrief. Learners who did not value observer roles described them as passive, or boring when compared to hands-on engagement in the simulation encounter. Conclusions Learning outcomes and role satisfaction for observers is improved through learner engagement and the use of observer tools. The value that students attach to observer roles appear contingent on role clarity, use of observer tools, and inclusion of observers’ perspectives in the debrief.

Simultaneous state and parameter estimation is essential for control system design and dynamic modeling of physical systems. This capability provides critical real-time insight into system behavior, supports the discovery of underlying mechanisms, and facilitates adaptive control strategies. Surveyed in this review paper are two classes of state and parameter estimation methods: Kalman Filters and Luenberger Observers. The Kalman Filter framework, including its major variants such as the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Cubature Kalman Filter (CKF), and Ensemble Kalman Filter (EnKF), has been widely applied for joint and dual estimation in linear and nonlinear systems under uncertainty. In parallel, Luenberger observers, typically used in deterministic settings, offer alternative approaches through high-gain, sliding mode, and adaptive observer structures. This review focuses on the theoretical foundations, algorithmic developments, and application domains of these methods and provides a comparative analysis of their advantages, limitations, and practical relevance across diverse engineering scenarios.

This paper presents a control technique to achieve trajectory tracking for a quadrotor subject to internal noises and external disturbance. The methodology involves designing a nonlinear disturbance observer to estimate and reject six different types of disturbances within the quadrotor model, followed by employing state transformation using tracking error and backstepping state space variables to formulate a robust control algorithm. In addition, adaptive laws are obtained using Lyapunov criteria to achieve online control gain tuning. Furthermore, a state observer technique utilizing a reduced-order observer is designed to estimate only the rotational and translational rates using measurable outputs. Finally, the control algorithm is finalized by using the estimated states and disturbances, and adaptive laws. The developed robust adaptive control technique relies solely on desired trajectory information and measurable rotational and translational outputs, thereby reducing the number of onboard sensors required for measurements of rotational and translational rates. The stability analysis is carried out using Lyapunov theory proving asymptotic convergence to the neighbourhood of origin. Simulations are performed on a DJI F450 quadrotor, demonstrating effectiveness and tracking performance of controller for two desired reference trajectories.

Induction motor parameters are essential for high-performance control. However, motor parameters vary because of winding temperature rise, skin effect, and flux saturation. Mismatched parameters will consequently lead to motor performance degradation. To provide accurate motor parameters, in this paper, a comprehensive review of offline and online identification methods is presented. In the implementation of offline identification, either a DC voltage or single-phase AC voltage signal is injected to keep the induction motor standstill, and the corresponding identification algorithms are discussed in the paper. Moreover, the online parameter identification methods are illustrated, including the recursive least square, model reference adaptive system, DC and high-frequency AC voltage injection, and observer-based techniques, etc. Simulations on selected identification techniques applied to an example induction motor are presented to demonstrate their performance and exemplify the parameter identification methods.