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Top-blowing furnace systems, characterized by a large number of sensors and harsh working environments, are prone to sensor failures due to factors like component aging and external interference. These failures can significantly impact the system's safe and reliable operation. However, traditional sensor fault diagnosis methods often neglect the exploration of spatial-temporal characteristics and focus solely on learning temporal relationships between sensors, failing to effectively consider their spatial relationships. In this study, we propose a spatial correlation model based on the maximal information-based graph convolutional network (MI-GCN) by constructing a sensor network knowledge graph using maximal mutual information. The MI-GCN leverages the graph convolution mechanism to extract multi-scale spatial features and capture the spatial relationships between sensors. Additionally, we develop a spatial-temporal graph-level prediction model, known as the spatial-temporal graph transformer (STGT), to extract temporal features. By combining the spatial features extracted by the MI-GCN with the temporal features captured by the STGT, accurate predictions can be achieved. Sensor fault diagnosis is conducted by analysing the normalized residuals between the predicted values and the ground truth. Finally, the feasibility and effectiveness of the proposed method are validated using test data from a top-blowing furnace system in the nickel smelting process.

Zebrafish are considered as model organisms in biological and medical research because of their high degree of homology with human genes. Automatic behavioral analysis of multiple zebrafish based on visual tracking is expected to improve research efficiency. However, vision-based multi-object tracking algorithms often suffer from data loss owing to mutual occlusion. In addition, simply tracking zebrafish as points is not sufficient-more detailed information, which is required for research on zebrafish behavior. In this paper, we propose Zebrafishtracker3D, which utilizes a skeleton stability strategy to reduce detection error caused by frequent overlapping of multiple zebrafish effectively and estimates zebrafish skeletons using head coordinates in the top view. Further, we transform the front- and top-view matching task into an optimization problem and propose a particle-matching method to perform 3D tracking. The robustness of the algorithm with respect to occlusion is estimated on the dataset comprising two and three zebrafish. Experimental results demonstrate that the proposed algorithm exhibits a multiple object tracking accuracy (MOTA) exceeding 90% in the top view and a 3D tracking matching accuracy exceeding 90% in the complex videos with frequent overlapping. It is noteworthy that each instance in the trace saves its skeleton. In addition, Zebrafishtracker3D is applied in the zebrafish courtship experiment, establishes the stability of the method in applications of life science, and proves that the data can be used for behavioral analysis. Zebrafishtracker3D is the first algorithm that realizes 3D skeleton tracking of multiple zebrafish simultaneously.

This paper presents a pioneering cascade estimator, CRESO, which merges reduced-order and full-order extended state observers (ESO) in a novel manner. CRESO is designed to navigate the trade-off between robustness, estimation accuracy, and noise amplification inherent in active disturbance rejection control (ADRC) schemes. An analysis in the frequency domain substantiates CRESO’s performance and robustness capabilities compared to those of single-level ESO and cascade ESO (CESO). These features are quantified using practical metrics, such as stability margins, sensitivity bandwidth, and estimation error at low frequencies. Additionally, the discussion encompasses the impact of selecting bandwidths for the cascade levels of CRESO on noise suppression. Experimental validation on a synchronous buck converter demonstrates the effectiveness of CRESO-based ADRC against control gain uncertainties, frequency-varying external disturbances, and sensor noise. The results highlight the advantages of the proposed approach over ADRC strategies employing singular ESO, two-level CESO, and two independent ESOs, as evidenced by several quality indices derived from the tracking errors and control signals.

This paper focuses on the output-feedback control for path-following of underactuated autonomous underwater vehicles subject to multiple uncertainties and unmeasured velocities. First, a novel extended state observer is proposed to estimate the mismatched lumped disturbance and recover the unmeasured velocities. Based on this premise, to overcome the limitation of relying solely on the accurate kinematic model, a disturbance observer-based stabilizing controller is developed. The difference in bandwidths between the observer and the vehicle dynamics allows for a mathematical setup amenable to standard singular perturbation theory. In the fast mode, a kinematic observer is designed to reject system uncertainty caused by unknown attack angular velocity and prohibitive path-tangential angular velocity, using a novel physical perspective. In the slow mode, an interconnected-system control law is proposed by integrating the backstepping technique with the time scale decomposition method. Furthermore, the stability of the overall closed-loop system is established. Finally, simulation results are presented to demonstrate the effectiveness of the proposed method for path-following of underactuated autonomous underwater vehicles in the vertical plane.

This research article details the design and implementation of a nonlinear adaptive filtering (NAF) technique using an exponential functional link network (EFLN) for a shunt hybrid active power filter (SHAPF) control to solve the current-associated power quality issues on the utility side at the distribution level of electrical power systems. Separation of the fundamental component from the harmonics, achieving unity power factor operation, reducing the reactive power drawn from the source, balancing the currents during transients, and reduction of total harmonic distortion (THD) of the source current are the issues considered to resolve. The proposed technique solves these issues by generating the sinusoidal reference current and separating the fundamental current from the harmonics. When compared to conventional and existing adaptive filtering techniques such as least mean square (LMS), least mean fourth (LMF), and variable step size LMS (VSS-LMS), the proposed EFLN-NAF method excels in terms of speedy convergence, adaptability in noise-specific environments and reduced steady-state coefficient error. MATLAB/Simulink software is utilized to perform the simulations to examine the suggested strategy for the chosen SHAPF topology both in static and dynamic scenarios. For a 15 kW and 3kVAr requirement of the nonlinear load, simulation results proved that the designed PPF for 2kVAr is able to share the reactive power with the APF, thereby reducing its rating and cost. The proposed method of filtering has been proven to be fast converging with 0.049 s, and the THD in steady state is brought to 1.32 % in steady-state and to 3.77 % during transient conditions, which are under standard limits. A hardware prototype of the experimental setup is constructed at the laboratory scale with OPAL-RT (OP4510) as the controller. With an active and reactive power demand of 1.1 kW and 210VAr, the designed PPF supplies 110 VAr, whereas the rest is supplied by the APF. The practical THD in source current is observed to be 2.081 %, which meets the standards. The results from both simulations and experiments are validated, and the efficacy of the proposed technique in mitigating the aforementioned power quality issues is proved.

In this paper, a novel joint unknown input observer (JUIO) is proposed for a class of descriptor systems. The unknown input (UI) to be estimated injects additively into both the state and output equations in a state space model. To the best of our knowledge, only a few contributions in existing work address this problem directly. To begin with, by introducing an auxiliary UI, the original system is transformed into a normal form in which the output is no longer affected by UI. In this way, the negative effect brought by the UI occurring in the output measurement is removed. An interval observer is developed to obtain upper and lower boundary estimates of the output of the reformulated system. After that, an algebraic relationship between the auxiliary UI and the states is established, and a UI reconstruction (UIR) method is developed. Based on the UIR, a JUIO comprising the UIR and a Luenberger-like state observer is developed to achieve asymptotic estimations of the UI and state simultaneously. Verifiable conditions for the existence of the proposed JUIO are given with respect to the original descriptor system. Finally, a simulation example is presented to verify the effectiveness of the proposed method.

This paper proposed a prediction algorithm for the degraded actuator taking into account the impact of estimation error of hidden index in the closed-loop system. To this end, a unified prediction framework is established to evaluate the hidden degradation information and recursively update the degradation model parameters simultaneously. The advantage is that the prediction framework can comprehensively compensate the estimation error of hidden degradation index caused by system uncertainty. To jointly estimate the degradation information in avoidance of the impact of system uncertainty, a modified adaptive Kalman filter is designed, and the proof of stability is provided. With the priori estimate from the filter, the degradation model parameters are updated by the inverse filtering probability based on Bayes’ theorem. It is followed by the computation of the remaining useful life (RUL) prediction utilizing aforementioned hidden degradation information and the latest degradation model. The effectiveness of the proposed RUL prediction algorithm is demonstrated by the degraded actuator in the continuous casting process.

The missile integrated guidance and control (IGC) problem with seeker’s asymmetric field-of-view (FOV) angle constraints is addressed. In the introduced model, the fin deflections controller is used to drive the body line-of-sight angle rate, which avoids the solving and tracking of aerodynamic angles in traditional IGC method. A novel fixed-time convergence virtual input based on the integral barrier Lyapunov function is designed to ensure the asymmetric FOV angle constraints are never violated. The virtual input is tracked by a new proposed pre-defined fixed time controller with adjustable initial convergence speed. The lumped uncertainty including aerodynamic coefficient and target maneuvering is coped by the fixed-time disturbance observer. It is proved that the closed-loop system states are converged to the bounded region in a fixed-time and the asymmetric FOV angle constraints are satisfied. The 6-degree of freedom flight simulations and comparisons verified the advantages of the proposed algorithm.

Fault detection and diagnosis of nonstationary processes are crucial for ensuring the safety of industrial production systems. However, the nonstationarity of process data poses multifaceted challenges to them. First, conventional stationary fault detection methods encounter difficulties in discerning evolving trends within nonstationary data. Secondly, the majority of current nonstationary fault detection methods directly extract features from all variables, rendering them susceptible to redundant interference. Moreover, nonstationary trends possess the capacity to conceal and modify the correlations among variables. Coupled with the smearing effect of faults, it is challenging to achieve accurate fault diagnosis. To address these challenges, this paper proposes sparse Wasserstein stationary subspace analysis (SWSSA). Specifically, a ${\ell }_{2,p}$-norm constraint is introduced to endow the stationary subspace model with excellent sparse representation capability. Furthermore, recognizing that fault variables within the sparse stationary subspace influence only a limited subset of stationary sources, this paper proposes a novel contribution analysis method based on local dynamic preserving projection (LDPP), termed LDPPBC, which can effectively mitigate the smearing effect on nonstationary fault diagnosis. LDPPBC establishes a LDPP matrix by extracting the latent positional information of fault variables within the stationary subspace. This allows LDPPBC to selectively analyze the contributions of variables within the latent fault subspace to achieve precise fault diagnosis while avoiding the interference of variable contributions from the fault-free subspace. Finally, the superiority of the proposed method is thoroughly validated through a numerical simulation, a continuous stirred tank reactor, and a real industrial roaster.

In this paper, a novel fixed-time non-singular terminal sliding mode control (NFNTSMC) method with an adaptive neural network (ANN) is proposed for permanent magnet synchronous motor (PMSM) system to improve PMSM performance. For nominal PMSM system without disturbance, a novel fixed-time non-singular terminal sliding mode control is designed to achieve fixed-time convergence property to improve the dynamic performance of the system. However, parameters mismatch and external load disturbances generally exist in PMSM system, the controller designed by NFNTSMC requires a large switching gain to ensure the robustness of the system, which will cause high-frequency sliding mode chattering. Therefore, an adaptive radial basis function (RBF) neural network is designed to approximate the unknown nonlinear lumped disturbance including parameters mismatch and external load disturbances online, and then the output of the neural network can be compensated to the NFNTSMC controller to reduce the switching gain and sliding mode chattering. Finally, the fixed-time convergence property and stability of the system are proved by Lyapunov method. The simulation and experimental results show that the presented strategy possesses satisfactory dynamic performance and strong robustness for PMSM system. And the proposed control scheme also provides an effective and systematic idea of the controller design for PMSM.