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This study aimed to examine the structural alterations of the deep gray matter (DGM) in the basal ganglia circuitry of Parkinson's disease (PD) patients with freezing of gait (FOG) using quantitative susceptibility mapping (QSM) and neuromelanin-sensitive magnetic resonance imaging (NM-MRI). Twenty-five (25) PD patients with FOG (PD-FOG), 22 PD patients without FOG (PD-nFOG), and 30 age- and sex-matched healthy controls (HCs) underwent 3-dimensional multi-echo gradient recalled echo and NM-MRI scanning. The mean volume and susceptibility of the DGM on QSM data and the relative contrast (NM RC-SNpc) and volume (NM volume-SNpc) of the substantia nigra pars compacta on NM-MRI were analyzed among groups. A multiple linear regression analysis was performed to explore the associations of FOG severity with MRI measurements and disease stage. The PD-FOG group showed higher susceptibility in the bilateral caudal substantia nigra (SN) compared to the HC group. Both the PD-FOG and PD-nFOG groups showed lower volumes than the HC group in the bilateral caudate and putamen as determined from the QSM data. The NM volume-SNpc on NM-MRI in the PD-FOG group was significantly lower than in the HC and PD-nFOG groups. Both the PD-FOG and PD-nFOG groups showed significantly decreased NM RC-SNpc. The PD-FOG patients showed abnormal neostriatum atrophy, increases in iron deposition in the SN, and lower NM volume-SNpc. The structural alterations of the DGM in the basal ganglia circuits could lead to the abnormal output of the basal ganglia circuit to trigger the FOG in PD patients. [ABSTRACT FROM AUTHOR]

The visualization and identification of the deep cerebellar nuclei (DCN) (dentate [DN], interposed [IN] and fastigial nuclei [FN]) are particularly challenging. We aimed to visualize the DCN using quantitative susceptibility mapping (QSM), predict the contrast differences between QSM and T2* weighted imaging, and compare the DCN volume and susceptibility in movement disorder populations and healthy controls (HCs). Seventy‐one Parkinson's disease (PD) patients, 39 essential tremor patients, and 80 HCs were enrolled. The PD patients were subdivided into tremor dominant (TD) and postural instability/gait difficulty (PIGD) groups. A 3D strategically acquired gradient echo MR imaging protocol was used for each subject to obtain the QSM data. Regions of interest were drawn manually on the QSM data to calculate the volume and susceptibility. Correlation analysis between the susceptibility and either age or volume was performed and the intergroup differences of the volume and magnetic susceptibility in all the DCN structures were evaluated. For the most part, all the DCN structures were clearly visualized on the QSM data. The susceptibility increased as a function of volume for both the HC group and disease groups in the DN and IN (p <.001) but not the FN (p =.74). Only the volume of the FN in the TD‐PD group was higher than that in the HCs (p =.012), otherwise, the volume and susceptibility among these four groups did not differ significantly. In conclusion, QSM provides clear visualization of the DCN structures. The results for the volume and susceptibility of the DCN can be used as baseline references in future studies of movement disorders. [ABSTRACT FROM AUTHOR]

Mapping and monitoring initial burn severity is a critical aspect of forest management and landscape restoration. Recently, supervised learning has achieved great success in evaluating the post-fire forest condition, but plenty of labeled samples are needed to carry out training for supervised learning. However, due to the limitation of accessible labeled data, it is often arduous to put supervised learning into practice in the remote sensing field. In this paper, a novel semi-unsupervised image classification framework by using generative adversarial networks under the Google Earth Engine (GEE) platform is proposed to undertake the burn severity assessment with limited samples. The generative model can produce additional adversarial samples that look like the real samples; therefore, the discriminative model gains better classification capabilities in burn severity assessment with the extra training data provided by the generator. The detailed evaluation and comparative experiments are done in the context of post-fire assessment on eleven forest fires in northern California in the United States. The experimental results demonstrate that our approach significantly outperforms the two other most well-known methods. [ABSTRACT FROM AUTHOR]

This article proposes an adaptive trajectory tracking control scheme for low-speed car-like vehicles with less efforts in tuning of the control gains. An interesting way of integrating adaptive control gains with consideration of steering saturation by using the backstepping technique is designed to enhance trajectory tracking while ensuring the commanded inputs within the input boundaries. The design of such adaptive control gains is also based on enhancing the convergence rate of tracking errors, especially for lateral deviation from the reference trajectory. It is further theoretically proven that, even under the influence of steering saturation, the proposed controller can make the closed-loop system approximately globally asymptotically stable at zero errors. Comparative MATLAB/Simulink simulations and experimental tests based on Quanser latest self-driving car have been conducted to verify the effectiveness of the proposed control scheme in accurate tracking without violating the input constraints. [ABSTRACT FROM AUTHOR]

This article considers the decentralized fractional-order fault-tolerant control problem for unmanned aerial vehicles (UAVs) against wind disturbances and actuator faults in a directed communication network. A new composite adaptive disturbance observer-based decentralized fractional-order fault-tolerant control (CADOB-DFO-FTC) scheme, which incorporates fractional-order (FO) sliding-mode surfaces, nonlinear disturbance observers (NDOs), fuzzy wavelet neural networks (FWNNs), and robust controllers, is developed to achieve the attitude tracking control of networked UAVs in a decentralized way. Based on the FO sliding-mode surfaces, the NDOs are first developed to estimate the lumped uncertainties due to the aerodynamic parameter perturbations, wind disturbances, and actuator faults. Then, adaptive FWNNs with updating weighting matrices, mean vectors, and deviation vectors are constructed to effectively attenuate the adverse effects induced by the NDO estimation errors. Furthermore, to compensate the FWNN approximation errors, robust controllers are integrated into the developed control scheme to enhance the approximation abilities. It is shown that by using Lyapunov methods, all UAVs can track their attitude references. Finally, comparative simulation results are presented to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In recent years, spectral unmixing is a hot issue in the hyperspectral remote sensing technology and applications. Within various methods, sparse regression is one of the most commonly used methods in the hyperspectral unmixing community. In order to overcome the limitation of spatial correlation of existing sparse unmixing methods and improve further the accuracy of sparsity representation, a novel hyperspectral linear unmixing method via fusion of collaborative sparsity and multi-band non-local total variation is proposed. This method analyses the sparsity and spatially geometrical structure of hyperspectral pixels by studying the linear spectral mixed model of hyperspectral images, and reveals the interior spatial correlation of hyperspectral images with the non-local variation framework. Specifically, the sparsity and spatial correlation of hyperspectral imagery are discussed, and a hyperspectral linear sparse unmixing model is afterwards constructed by combining the relaxation technique and non-local total variation framework. Second, the solution procedure of the above spectral unmixing model is divided into two steps under the variable separation method: fractional abundance estimation and abundance map restoration. The alternating direction method of multipliers (ADMM) and split Bregman operator are exploited to obtain the spectral unmixing results. Finally, the proposed hyperspectral unmixing algorithm is evaluated with synthetic and real hyperspectral datasets. In the experiments with a synthetic hyperspectral dataset, the feasibility and effectiveness of the method are analysed quantitatively and qualitatively. The quantitative metrics and visual examination of the estimated fractional abundance map are also better than the performance of the current mainstream hyperspectral sparse unmixing algorithms. Furthermore, two real hyperspectral datasets are applied to the algorithm of this work to prove its practicability. [ABSTRACT FROM AUTHOR]

Various therapies are used in patients with internet gaming disorder (IGD), but their comparative effectiveness in reducing IGD symptoms is unknown. The present systematic review and meta-analysis evaluated the effectiveness of pharmacotherapy, psychotherapy and pharmacological-psychological combined therapy on IGD related-outcomes and its comorbid mental disorders. Following PRISMA guidelines, a search of MEDLINE, EMBASE, Ovid Emcare, PsycINFO and the Cochrane Clinical Trials Library was conducted by Aug 04, 2023. We pooled effect sizes as within-group Hedges's g and performed meta-regression using random effects models. Systematic review identified 43 studies and 32 of them with 1738 participants were included for meta-analysis. Pharmaco-, psycho- and combined therapies showed large-to-moderate effect sizes for IGD symptoms and comorbid mental disorders. Combined therapies (Hedges's g = −2.11, 95% confidence interval: −2.87 to −1.35) showed larger effect size than pharmacotherapies (Hedges's g = −1.10, −1.31 to −0.89) or psychotherapies (Hedges's g = −0.99, −1.22, −0.76) on IGD reduction at post-intervention. The effects maintained at follow-up in psycho- and combined therapies. Bupropion and cognitive behavioral therapy showed advantages over their counterparts. The three types of therapies are promising to reduce IGD symptoms and comorbidities, but cautious interpretation is required given the limitations and high heterogeneity in the existing interventions. • Pharmaco-, psycho- and combined therapies were promising to reduce IGD symptoms and comorbid mental disorders. • Combined therapy was more effective than pharmacotherapies or psychotherapies alone on IGD reduction at post-intervention. • The effects on IGD reduction maintained at follow-up in psycho- and combined therapies. • Bupropion and cognitive behavioral therapy showed advantages over their counterparts. • Cautious interpretation is required given the limitations and high heterogeneity in the existing interventions. [ABSTRACT FROM AUTHOR]

The sizes of wind turbines have been steadily increasing over the past decade, especially on offshore sites. The greater structural flexibility of such machines necessitates the development of reliable load mitigation techniques to alleviate the effects of asymmetric wind loads and fatigue. Given the importance of such techniques, this article proposes a reliable load mitigation scheme, referred to as “fault-tolerant individual pitch control,” for individually adjusting the pitch angle of wind turbine blades in the presence of blade pitch actuator faults. The proposed scheme consists of a collective pitch control augmented with an individual pitch control, and a fault detection and diagnosis system. Simulation results using an offshore wind turbine illustrate the effectiveness as well as fault-tolerance capability of the developed scheme compared to controlling the pitch angles collectively. The developed scheme not only ensures an almost equal output power with reduced loads on the turbine, but also tolerates the effects of possible faults in pitch actuators of the blades. [ABSTRACT FROM AUTHOR]

The current maritime applications have yielded strong demands for the development of advanced unmanned surface vehicles (USVs) with more reliable path following capabilities to greatly extend mission durations and enhance accommodative capabilities of USVs to more hazardous and dynamic environments. This paper presents an adaptive path following control method using a retrofit adaptive tracking control technique with application to a USV with consideration of environmental disturbances (like winds, waves, and currents), while taking into account of the system constraints of USVs, including both turning features (turning rate limit and turning dynamics) and rudder operation constraints (rudder deflection and rate saturation, and its dynamics). In order to guarantee the satisfactory performance of the USV operating in a calm environment, a baseline state feedback tracking controller considering the characteristics of yaw rate and rudder operations, and USV steering and actuator dynamics is first designed. In the presence of time-varying environmental disturbances, a retrofit adaptive disturbance compensating control mechanism is then developed based on the disturbance amplitude estimated from an indirect adaptive disturbance estimator. Finally, a reconfigurable adaptive path following controller is synthesized by combining the baseline controller and the adaptive disturbance compensating mechanism for the proper operation of the USV in the presence of environmental disturbances, while the desired path is successfully followed by the USV within an acceptable deviation boundary and without violating constraints of turning rates as well as amplitude and rate of rudder deflections. To evaluate the effectiveness of the proposed path following control methodology, both numerical simulations on a nonlinear USV model and field experiments on a real-size USV are conducted. [ABSTRACT FROM AUTHOR]

This study presents a distributed fault‐tolerant cooperative control (FTCC) strategy to achieve the attitude synchronisation tracking control of networked unmanned aerial vehicles (UAVs) in the presence of actuator faults and model uncertainties. By utilising the fuzzy neural networks (FNNs), the unknown non‐linear terms induced by actuator faults and model uncertainties are estimated as lumped uncertainties. A set of distributed sliding‐mode estimators (DSMEs) is then employed to estimate the leader UAV's attitudes for the follower UAVs via a distributed communication network. Based on the estimated knowledge from FNNs and DSMEs, a group of distributed FTCC laws is developed for all follower UAVs by using the fractional‐order calculus. It is proven that with the proposed control scheme, all follower UAVs can track the attitudes of the leader UAV and the tracking errors are uniformly ultimately bounded even when a portion of networked UAVs encounters multiple actuator faults. Comparative simulation results are presented to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents an average dwell-time method for fault-tolerant control of a class of uncertain switched linear time-delay systems and also designs an algorithm to determine the parameters pertaining to the dwell-time condition. Compared to the literature results, this paper features the following: 1) the proposed method is independent of switching polices if switchings are slow enough in the average sense; 2) the proposed control design guarantees exponential stabilization of this class of time-delay linear systems with actuator faults; and 3) this method treats all actuators in a unified manner without a need to identify which are faulty or healthy actuators. The proposed method is applied to the water pollution control problem, for the first time, to deal with different ratios of stream flow rates to effluent flow rates. [ABSTRACT FROM AUTHOR]

In this paper, a prescribed performance-based distributed neural adaptive fault-tolerant cooperative control (FTCC) scheme is proposed for multiple unmanned aerial vehicles (multi-UAVs). A distributed sliding-mode observer (SMO) technique is first utilized to estimate the leader UAV's reference. Then, by transforming the tracking errors of follower UAVs with respect to the estimated references into a new set, a distributed neural adaptive FTCC protocol is developed based on the combination of dynamic surface control (DSC) and minimal learning parameters of neural network (MLPNN). Moreover, auxiliary dynamic systems are exploited to deal with input saturation. Furthermore, the proposed control scheme can guarantee that all signals of the closed-loop system are bounded, and tracking errors of follower UAVs with respect to the estimated references are confined within the prescribed bounds. Finally, comparative simulation results are presented to illustrate the effectiveness of the proposed distributed neural adaptive FTCC scheme. [ABSTRACT FROM AUTHOR]

This article investigates the fault‐tolerant containment control (FTCC) problem for a group of fixed‐wing unmanned aerial vehicles with simultaneous considerations of faults and collision avoidance. A fractional‐order (FO) FTCC scheme is established to steer all follower UAVs into the convex hull formed by the leader UAVs with the involvements of FO calculus, disturbance observers (DOs), and interval type‐2 fuzzy neural networks (IT2FNNs). In the proposed control protocol, FO sliding‐mode surfaces with artificial potential functions are first designed to revamp the filtered containment errors. Then, the DOs with FO calculus are constructed to estimate the FO lumped disturbances due to faults and external disturbances. Moreover, to compensate for the DO estimation errors, IT2FNN learning mechanisms are introduced to improve the FTCC capability. It is shown by Lyapunov stability analysis that all follower UAVs can successfully converge into the convex hull spanned by the leader UAVs without collisions even when a portion of UAVs is encountered by faults. Simulation results are presented to show the effectiveness of the developed control scheme. [ABSTRACT FROM AUTHOR]

This paper proposes a novel adaptive sliding-mode-based control allocation scheme for accommodating simultaneous actuator faults. The proposed control scheme includes two separate control modules with virtual control part and control allocation part, respectively. As a low-level control module, the control allocation/reallocation scheme is used to distribute/redistribute virtual control signals among the available actuators under fault-free or faulty conditions, respectively. In the case of simultaneous actuator faults, the control allocation and reallocation module may fail to meet the required virtual control signal, which will degrade the overall system stability. The proposed online adaptive scheme can seamlessly adjust the control gains for the high-level sliding mode control module and reconfigure the distribution of control signals to eliminate the effect of the virtual control error and maintain the stability of the closed-loop system. In addition, with the help of the boundary layer for constructing the adaptation law, the overestimation of control gains is avoided, and the adaptation ceases once the sliding variable is within the boundary layer. A significant feature of this study is that the stability of the closed-loop system is guaranteed theoretically in the presence of simultaneous actuator faults. The effectiveness of the proposed control scheme is demonstrated by experimental results based on a modified unmanned multirotor helicopter under both single and simultaneous actuator faults conditions with comparison to a conventional sliding mode controller and a linear quadratic regulator scheme. [ABSTRACT FROM PUBLISHER]

This paper presents the development of a nonlinear model predictive controller (NMPC) applied to a closed grinding circuit system in the cement industry. A Markov chain model is used to characterize the cement grinding circuit by modeling the ball mill and the centrifugal dust separator. The probability matrices of the Markovian model are obtained through a combination of comminution principles and experimental data obtained from the particle size distribution of cement samples at specific stages of the system. The NMPC is designed as a supervisory controller to manage distributed controllers installed in the process. Both the model and the controller are validated online through the implementation of the proposed approach in the supervisory control and data acquisition (SCADA) system of an industrial plant. The results show a significant improvement in the performance of the grinding circuit in comparison to the operation of the system without the proposed controller. [ABSTRACT FROM PUBLISHER]

Summary: Wind energy has shown a remarkable potential for fulfilling the increasing world's energy demand in a clean and sustainable way. The wind energy industry has installed increasingly sophisticated and larger wind turbines particularly in offshore regions to capture such energy as efficiently and cost effectively as possible. The rapid growth in size and capacity of wind turbines together with harsh climate conditions and limited accessibility in offshore regions all result in higher failure rates and increased maintenance requirements and costs. Such difficulties motivate the use of advanced fault detection and diagnosis and fault‐tolerant control schemes in wind farms to improve their reliability and availability. Given the importance of this issue, this paper uses a fuzzy model reference adaptive control approach in a cooperative framework that is oriented to the design and development of a novel fault‐tolerant cooperative control scheme in a wind farm. This scheme handles decreased power generation faults in a wind farm caused by turbine blade erosion and debris buildup on the blades over time. The effectiveness and performance of the proposed scheme is demonstrated by a series of simulations on an advanced large offshore wind farm benchmark model in the presence of wind turbulence, measurement noise, load variations, and realistic fault scenarios. [ABSTRACT FROM AUTHOR]

Given the importance of reliability and availability in wind farms, this paper focuses on the development of fault diagnosis and fault-tolerant control schemes in a cooperative framework (referred to as active fault-tolerant cooperative control) at the wind farm level against the decreasing power generation caused by turbine blade erosion and debris build-up on the blades over time. In more details, the paper presents a novel integrated fault detection and diagnosis and fault-tolerant control approach oriented to the design and development of two active fault-tolerant cooperative control schemes for an offshore wind farm. Each of the schemes employs a fault detection and diagnosis system to provide accurate and timely diagnosis information to be used in an appropriate automatic signal correction algorithm for accommodation of faults in the wind farm. The effectiveness and performance of the proposed schemes are evaluated and compared using different simulations on a high-fidelity offshore wind farm benchmark model in the presence of wind turbulences, measurement noises and realistic fault scenarios. [ABSTRACT FROM AUTHOR]

Experimental study on the ballistic behavior of mosaic Silicon Carbide (SiC) ceramics bonding with Ultra-High Molecular Weight Polyethylene (UHMWPE) plate is presented against 7.62 mm×51 mm Armor Piercing (AP) projectile. The effect of SiC ceramics on the ballistic performance of mosaic SiC/UHMWPE composite armors is systematically investigated, and the bulletproof process of the composite armor combining the hard/brittle SiC and soft/flexible UHMWPE is clarified in detail. The test results show that the ballistic performance of composite armors is significant influenced by the geometry of mosaic SiC ceramics and the impact zone of front layer, different impact zones are defined for the cylindrical mosaic SiC/UHMWPE composite armor. The residual velocities of AP projectile are varied from 648 m/s to 0 m/s for the cylindrical, hexagonal and square mosaic SiC ceramics, when the launch speeds of bullets are 780 m/s with a standard deviation of 10 m/s. The bulge lengths of UHMWPE plate are also changed from 16.5 mm to 43.2 mm for different structures of mosaic SiC ceramic front layer. The bulletproof property can be significantly enhanced by prolonging the interaction time between bullet and target. The conclusions of this paper are practically useful for designing the hard/flexible composite armor systems. [ABSTRACT FROM AUTHOR]

Automobiles automated perpendicular parking using Artificial Potential Field (APF) is discussed in this paper. The Unmanned Ground Vehicle (UGV) used for carrying out experiments is introduced first; UGV configuration, kinematics, and motion controller are included. Based on discretized form of the parking space, the APF is generated. Holonomic path for the vehicle parking is found first; path modification to satisfy minimum turning-radius constraint is performed based on Reeds-Shepp curve connections. Optimization efforts are included to remove extra maneuvers and to reduce length of the path. Afterwards waypoints are generated as reference for the vehicle to track. Perpendicular parking tests with several different start configurations are demonstrated; based on the test results the automated parking framework proposed in this paper is considered to be effective. [ABSTRACT FROM AUTHOR]

This paper studies a robust fault tolerant control of a class of nonlinear switched systems with strong structural uncertainties and actuator faults. This paper presents a new robust fault tolerant state feedback method, by using the average dwell time technique to stabilize the switched nonlinear systems exponentially under an arbitrary switching law. Comparing with the existing results, the method of this paper has three features: (1) this method is applicable to the switched nonlinear systems with strong structural uncertainties and faulty actuators and also the nominal case of the considered systems, there is no need to change any structures and/or parameters of the controller. (2) The switching law is arbitrary provided that the average switching is slow enough in the average sense. (3) This method treats all actuators in the same way, no need to separate faulty actuators from the healthy ones. The simulation result verifies the effectiveness of this method. [ABSTRACT FROM AUTHOR]

Motion planning methodologies for parallel parking have been well developed in the last decade. In contrast to the prevailing and emerging parking motion planners, this work provides a precise and objective description of the parking scenario and vehicle kinematics/dynamics. This is achieved by formulating a unified optimal control problem that is free of subjective knowledge (e.g., human experiences). The concerned optimal control problem, when parameterized into a large-scale nonlinear programming (NLP) problem, is extremely difficult to solve. This bottleneck has hindered many research efforts previously. Although the feasible regions of NLP problems are clearly defined, the majority of NLP-solving processes still require high-quality initial guesses, which accelerate the convergence process. In this work, we propose a spatio-temporal decomposition based initialization strategy to generate reliable initial guesses, so as to facilitate the NLP-solving process. In contrast to the typical facilitation strategies in robotic motion/path planning, our spatio-temporal decomposition strategy considers only objective kowledge, further breaking the limitation of subjective knowledge and making full use of a vehicle's maneuver potential. A series of comparative simulations verifies that the proposed initialization strategy is advantageous over its prevailing competitors, and that the proposed motion planner is promising for on-line planning missions. Theoretical analysis that supports our initialization strategy is given as well. [ABSTRACT FROM AUTHOR]

This paper aims to address the dual-input problem for civil aircraft equipped with side-sticks. A concept of full-altitude attitude angles envelope is firstly proposed; at low altitude this envelope protects the aircraft from striking ground, while at high altitude it restricts attitude angles within a threshold to guarantee passenger-ride qualities. Model Predictive Control (MPC) strategy is utilized in this paper; at each operation point, given linearized dynamics of the aircraft, cost function and inequalities constraints for MPC are specified, associated control laws are generated. Flight simulation investigates the performance of MPC protection laws; two flight records at high altitude and a record simulating the take-off and landing process are presented. Based on the test records, the proposed MPC-based protection framework is considered to be applicable. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive reconfiguration control for linear systems with both unknown actuator failures and disturbances. The model uncertainties are included to present additive disturbances which are unknown but bounded. By using set-membership estimation technique for such a problem, two interval observers are developed to estimate the system states subject to model uncertainties. Based on these state estimates, an adaptive state feedback reconfiguration controller is proposed to compensate actuator failures for achieving system states tracking to those of reference model under the framework of model reference adaptive control approach. The effectiveness of the proposed method is demonstrated through simulation results. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

With growing worldwide interest in commercial, scientific, and military issues associated with both oceans and shallow waters, there has been a corresponding growth in demand for the development of unmanned surface vehicles (USVs) with advanced guidance, navigation and control (GNC) capabilities. This paper presents a comprehensive literature review of recent progress in USVs development. The paper first provides an overview of both historical and recent USVs development, along with some fundamental definitions. Next, existing USVs GNC approaches are outlined and classified according to various criteria, such as their applications, methodologies, and challenges. Finally, more general challenges and future directions of USVs towards more practical GNC capabilities are highlighted. [ABSTRACT FROM AUTHOR]

Among renewable energy sources, wind power is expected to contribute a larger and rapidly growing portion of the world's energy portfolio. However, the increased penetration of wind power into the power grid has challenged the reliable and stable operation of the grid. This motivates new opportunities in the design and development of novel control schemes capable of actively maintaining the necessary balance between power generation and load, which in turn regulates the grid frequency when plenty of winds are available. This paper presents two active power control schemes that are developed based on adaptive pole placement control and fuzzy gain-scheduled proportional-integral control approaches. The active power control is conducted collectively across a wind farm to provide rapid power response while maintaining safe structural loading on turbines’ components. The proposed active power control schemes are evaluated and compared by a series of simulations on an advanced wind farm benchmark model in the presence of wind turbulences, measurement noises, and grid load variations. It is further demonstrated that the mentioned schemes are able to tolerate probable occurrence of sudden imbalance between generation and load due to relevant faults/failures in the wind farm or electric grid. [ABSTRACT FROM AUTHOR]

Because of their rapid maneuverability, extended operational range, and improved personnel safety, unmanned aerial vehicles (UAVs) with vision-based systems have great potential for monitoring, detecting, and fighting forest fires. Over the last decade, UAV-based forest fire fighting technology has shown increasing promise. This paper presents a systematic overview of current progress in this field. First, a brief review of the development and system architecture of UAV systems for forest fire monitoring, detection, and fighting is provided. Next, technologies related to UAV forest fire monitoring, detection, and fighting are briefly reviewed, including those associated with fire detection, diagnosis, and prognosis, image vibration elimination, and cooperative control of UAVs. The final section outlines existing challenges and potential solutions in the application of UAVs to forest firefighting. [ABSTRACT FROM AUTHOR]

Unmanned Aircraft Systems (UASs) are becoming ever more promising over the last decade. The Sense and Avoid (S&A) system plays a profoundly important role in integrating UASs into the National Airspace System (NAS) with reliable and safe operations. After analyzing the manner of S&A system, this paper systematically presents an overview on the recent progress in S&A technologies in the sequence of fundamental functions/components of S&A in sensing techniques, decision making, path planning, and path following. The approaches to these four aspects are outlined and summarized, based on which the existing challenges and potential solutions are highlighted for facilitating the development of S&A systems. [ABSTRACT FROM AUTHOR]

Color is a crucial feature to consider when breeding and improving strains of Auricularia cornea. To uncover the mechanism of white strain formation in A. cornea, this study selected parental strains that were homozygous for the color trait and analyzed the genetic laws of A. cornea color through genetic population construction, such as test-cross, back-cross, and self-cross populations, and the statistical analysis of color trait segregation. Moreover, the study developed SSR molecular markers to construct a genetic linkage map, perform the fine mapping the color-controlling genetic locus, and verify candidate genes using yeast two-hybrid, transcriptome analysis, and different light treatments. The results of the study indicated that the color trait of A. cornea is controlled by two pairs of alleles. When both pairs of loci are dominant, the fruiting body is purple, while when both pairs of loci are recessive or one pair of loci is recessive, the fruiting body is white. Based on the linkage map, the study finely mapped the color locus within Contig9_29,619bp-53,463bp in the A. cornea genome and successfully predicted the color-controlling locus gene A18078 (AcveA), which belongs to the Velvet factor family protein and has a conserved structure domain of the VeA protein. It can form a dimer with the VelB protein to inhibit pigment synthesis in filamentous fungi. Lastly, the study validated the interaction between AcVeA and VelB (AcVelB) in A. cornea at the gene, protein, and phenotype levels, revealing the mechanism of inhibition of pigment synthesis in A. cornea. Under dark conditions, dimerization occurs, allowing it to enter the nucleus and inhibit pigment synthesis, leading to a lighter fruiting body color. However, under light conditions, the dimer content is low and cannot enter the nucleus to inhibit pigment synthesis. In summary, this study clarified the mechanism of white strain formation in A. cornea, which could aid in improving white strains of A. cornea and studying the genetic basis of color in other fungi. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative integrated path planning and trajectory tracking control framework for a quadrotor unmanned aerial vehicle (UAV) in the presence of environmental and systematic uncertainties to achieve integrated guidance and control. Firstly, in order to perform real-time path planning, a computationally cost-effective planning algorithm is designed to find an optimal and smooth path while avoiding both static and dynamic obstacles. Then, by employing the pure-pursuit path following approach, the generated geometric path is converted to a trajectory profile related to time, which serves as the reference commands for the low-level trajectory tracking controller. Finally, a novel adaptive sliding mode trajectory tracking controller is proposed to compensate model uncertainties and maintain the desired tracking performance for the studied quadrotor UAV. With the proposed adaptive schemes, overestimation of uncertain parameters can be avoided, which further contributes to avoiding control chattering of the system. The performance of the proposed framework is validated through comparative simulation and experimental tests based on a quadrotor UAV subject to model uncertainties and environmental obstacles, which confirms the effectiveness and superiority of the proposed approach for practical applications. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a bibliographical review on reconfigurable (active) fault-tolerant control systems (FTCS) is presented. The existing approaches to fault detection and diagnosis (FDD) and fault-tolerant control (FTC) in a general framework of active fault-tolerant control systems (AFTCS) are considered and classified according to different criteria such as design methodologies and applications. A comparison of different approaches is briefly carried out. Focuses in the field on the current research are also addressed with emphasis on the practical application of the techniques. In total, 376 references in the open literature, dating back to 1971, are compiled to provide an overall picture of historical, current, and future developments in this area. [Copyright &y& Elsevier]

This paper proposes an adaptive fault-tolerant cooperative control (FTCC) scheme for networked unmanned aerial vehicles (UAVs) in the presence of actuator faults and wind effects by artfully introducing fractional order calculus, proportional–integral–derivative (PID), and recurrent neural networks. Fractional order sliding-mode surface and PID-type error mapping are first utilized to transform the synchronization tracking errors of all UAVs into a new set of errors. Then, based on these newly constructed errors, an FTCC scheme is developed to synchronously track their references. Moreover, Butterworth low-pass filter (BLF) and recurrent neural network (RNN) learning strategies are assimilated to handle the unknown terms induced by the actuator faults and wind effects. Finally, theoretical analysis and comparative hardware-in-the-loop experimental demonstrations have shown the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

In this paper, the classical least squares (LS) and recursive least squares (RLS) for parameter estimation have been re-examined in the light of the present day computing capabilities. It has been demonstrated that for linear time-invariant systems, the performance of blockwise least squares (BLS) is always superior to that of RLS. In the context of parameter estimation for dynamic systems, the current computational capability of personal computers are more than adequate for BLS. However, for time-varying systems with abrupt parameter changes, standard blockwise LS may no longer be suitable due to its inefficiency in discarding “old” data. To deal with this limitation, a novel sliding window blockwise least squares approach with automatically adjustable window length triggered by a change detection scheme is proposed. Two types of sliding windows, rectangular and exponential, have been investigated. The performance of the proposed algorithm has been illustrated by comparing with the standard RLS and an exponentially weighted RLS (EWRLS) using two examples. The simulation results have conclusively shown that: (1) BLS has better performance than RLS; (2) the proposed variable-length sliding window blockwise least squares (VLSWBLS) algorithm can outperform RLS with forgetting factors; (3) the scheme has both good tracking ability for abrupt parameter changes and can ensure the high accuracy of parameter estimate at the steady-state; and (4) the computational burden of VLSWBLS is completely manageable with the current computer technology. Even though the idea presented here is straightforward, it has significant implications to virtually all areas of application where RLS schemes are used. [Copyright &y& Elsevier]

Presents a study which discussed a multiple-model (MM) estimator with a variable structure called likely-model set algorithm applied to hybrid estimation problems. Impact of (M) estimation to adaptive estimation; Conclusions.

This paper investigates a new finite-time fault-tolerant control (FTC) using a fractional-order backstepping iterative design strategy for a fixed-wing unmanned aerial vehicle (UAV) in the presence of actuator faults and input saturation. To compensate for the lumped disturbance induced by the actuator faults, a neural network disturbance observer (NNDO) with finite-time observation capability is first developed as the fault diagnostic unit. Then, based on the diagnosed fault information, fractional-order (FO) calculus is artfully utilized to enhance the FTC performance within the backstepping design architecture. The salient feature of the developed control scheme is that the finite-time NNDO and FO calculus are simultaneously used to significantly increase the FTC performance against unexpected actuator faults. Moreover, to address the input saturation problem, the faulty UAV dynamics is augmented by a new auxiliary system. Furthermore, a Nussbaum function is incorporated into the FTC scheme to further avoid the calculation of the inverse gain matrix involved within the auxiliary system. It is shown by Lyapunov analysis that the tracking errors are convergent in finite time. Finally, comparative simulations are conducted to show the effectiveness of the developed FTC scheme. Some hardware-in-the-loop (HIL) experimental results are illustrated to further demonstrate the feasibility of the proposed finite-time fractional-order fault-tolerant control (FTFOFTC) method. [ABSTRACT FROM AUTHOR]

• PENCIL imaging can visualize NM with high quality compared to conventional MTC images. • PENCIL imaging showed similar diagnostic performance for differentiating PDs from HCs compared with MTC. • PENCIL has rapid whole-brain coverage using STAGE imaging, offering a one-stop quantitative assessment. Parkinson's disease (PD) is associated with the loss of neuromelanin (NM) and increased iron in the substantia nigra (SN). Magnetization transfer contrast (MTC) is widely used for NM visualization but has limitations in brain coverage and scan time. This study aimed to develop a new approach called Proton-density Enhanced Neuromelanin Contrast in Low flip angle gradient echo (PENCIL) imaging to visualize NM in the SN. This study included 30 PD subjects and 50 healthy controls (HCs) scanned at 3T. PENCIL and MTC images were acquired. NM volume in the SN pars compacta (SNpc), normalized image contrast (Cnorm), and contrast-to-noise ratio (CNR) were calculated. The change of NM volume in the SNpc with age was analyzed using the HC data. A group analysis compared differences between PD subjects and HCs. Receiver operating characteristic (ROC) analysis and area under the curve (AUC) calculations were used to evaluate the diagnostic performance of NM volume and CNR in the SNpc. PENCIL provided similar visualization and structural information of NM compared to MTC. In HCs, PENCIL showed higher NM volume in the SNpc than MTC, but this difference was not observed in PD subjects. PENCIL had higher CNR, while MTC had higher Cnorm. Both methods revealed a similar pattern of NM volume in SNpc changes with age. There were no significant differences in AUCs between NM volume in SNpc measured by PENCIL and MTC. Both methods exhibited comparable diagnostic performance in this regard. PENCIL imaging provided improved CNR compared to MTC and showed similar diagnostic performance for differentiating PD subjects from HCs. The major advantage is PENCIL has rapid whole-brain coverage and, when using STAGE imaging, offers a one-stop quantitative assessment of tissue properties. [ABSTRACT FROM AUTHOR]

This paper investigates a problem of autonomous aerial refueling point parallel rendezvous guidance and flight control subject to exogenous wind field disturbances. The tanker and uncrewed receiver aircraft approach the common refueling rendezvous point according to their respective flight plans, ultimately maintaining a fixed altitude and the same speed. According to the relative position of refueling target point and uncrewed receiver, a new continuous terminal sliding-mode guidance law combined with the prescribed performance control theory is designed under the end angle and speed constraints. Meanwhile, a finite-time observer is proposed to estimate the parameter uncertainties and wake vortex disturbances during refueling rendezvous. Based on the estimated information, a backstepping double-integral sliding-mode controller is developed for the uncrewed receiver flight control system. In the process of the guidance and flight control system, the integral variable is introduced to improve the steady-state performance. The designed guidance and control system can enable the uncrewed receiver to achieve the refueling rendezvous task within finite time and improve the efficiency of uncrewed aerial refueling. Comparative numerical simulations are provided to demonstrate the effectiveness of the proposed guidance and control scheme. [ABSTRACT FROM AUTHOR]

Investigates the importance in the application of a variable-structure multiple-model (VSMM) estimator, called model-group switching (MGS) algorithm. Design of model-group switching algorithm; Design of test scenarios between MGS algorithm with the Interacting Multiple-Model estimator; Performance measures of MM estimators.

Presents information on the detection and diagnosis of sensor and actuator failures, using an approach which is based on the interacting multiple-model (IMM) estimation algorithm. Examination of modern engineering systems; Details on the integrated framework of this approach; Conclusions of this research.

Presents a fast learning algorithm for training multilayer feedforward neural networks (FNN) by using a fading memory extended Kalman filter (FMEKF) along with a technique using self-adjusting time-varying forgetting factor. Overview of the FEMKF-based learning algorithm; U-D factorization-based FMEKF learning algorithm; Applications to nonlinear systems modeling.

Concurrent occurrences of actuator faults and wind effects can significantly threaten the flight safety of multiple unmanned aerial vehicles (multi-UAVs). To address this difficult control problem against actuator faults and wind effects, a composite decentralized fractional-order (FO) backstepping adaptive neural fault-tolerant control (FTC) method is presented for the attitude synchronization tracking of multi-UAVs, which is integrated with neural networks (NNs), disturbance observers (DOs), FO calculus, and high-order sliding-mode differentiators (HOSMDs). The distinctive feature of this work is addressing the attitude synchronization tracking control problem with actuator faults and wind effects in a decentralized framework and proposing a composite approximation method for multi-UAVs. It is shown that by using Lyapunov methods the synchronization tracking control is achieved even when multi-UAVs simultaneously encounter wind effects and actuator faults. Comparative simulation results illustrate the theoretical feasibility. [ABSTRACT FROM AUTHOR]

This article details the development and evaluation of a practical solution for path-constrained proximity maneuvers of spacecraft. Whereas no velocity measurement is utilized within the feedback structure, the controller rigorously enforces actuator magnitude constraints. Specifically, the control algorithm is constructed from a potential function method repelling the spacecraft from possible collisions. The proposed controller can guarantee potential functions to be navigated to the origin and thus overcome the stubborn local minima problem. Moreover, the control capability under any given control limit can be estimated and adjusted by changing the feedback gains. The specific performance with guaranteed safety can be also explicitly calculated by designers. The results are obtained through a Lyapunov-based stability analysis to prove uniformly ultimate boundedness. Numerical simulation results illustrate the performance and features of the developed control method. [ABSTRACT FROM AUTHOR]

In a dynamic environment, moving to the destination safely and effectively is of paramount importance for an unmanned ground vehicle (UGV). This article presents a strategy of trajectory planning and tracking that aims to ensure the UGV’s safety in an uncertain environment. Specifically, based on the initial environment information, a global optimal trajectory connecting the start and the destination is predefined by an artificial fish swarm algorithm (AFSA). In the presence of unforeseen obstacles, a trial-based forward search (TFS) algorithm based on the Markov chain is proposed in the local trajectory planning module, while collision prediction is integrated as heuristic information. The vehicle’s current state is updated accordingly for the sake of avoiding entire state spaces involved in the computation. Therefore, the storage efficiency and convergence rate in local path planning are sufficiently enhanced in comparison to dynamic programming. Moreover, command signals can be calculated with the proposed multiconstrained model predictive controller (MMPC), ensuring the vehicle to track the reference trajectory and smoothen the motion. Finally, the results in both simulations and experiments reveal the effectiveness of the proposed algorithm in the presence of both static and dynamic obstacles. Note to Practitioners—This article is motivated by the unmanned ground vehicle (UGV) collision avoidance problem in practical missions, such as farming and emergency response. In recent years, various trajectory planning and tracking algorithms have been widely developed. However, the environmental complexity and the intruders’ unexpected movement pose difficulties in trajectory planning, especially in ensuring the computation time under the allowable threshold. Moreover, the UGV practical trajectory tracking is a challenging task which demands a desired response within various physical constraints. In this article, a two-stage conflict resolution system is proposed. First, a trial-based forward search (TFS) is used to generate a new trajectory deviating the UGV from the initially generated trajectory by the artificial fish swarm algorithm (AFSA), aiming to avoid the unforeseen intruders (unknown in prior) in real-time. Using these two trajectory planning algorithms alternatively, both global trajectory optimality in a cluttered environment and appropriate maneuvers with respect to unexpected intruders can be achieved. Subsequently, the UGV is modeled according to its kinematic characteristics, and thus a multiconstrained model predictive controller (MMPC) is designed to follow the reference trajectory. The physical constraints are respected by integrating them into the controller. Simulations and experimental results demonstrate that the proposed strategy can guide and control a UGV from the start to the destination safely and smoothly, even in the case of multiple obstacles with constant or varying velocities. Furthermore, the proposed collision avoidance strategy can be extended to other unmanned systems, including unmanned aerial vehicles and unmanned surface vehicles. [ABSTRACT FROM AUTHOR]

Underwater image processing has always been a very challenging problem. Under the influence of environmental factors, underwater images are prone to some problems, such as colour cast, low visibility, and few edge details. Here, an image enhancement algorithm is proposed to improve image degradation mainly caused by the absorption of light. First, colour compensation and white balance algorithm are used to restore the natural appearance of the image. Then the improved dark channel prior (DCP) is used to improve the visibility and avoid blocking artifacts which appear in traditional DCP. Unsharp masking (USM) is applied to enhance the texture features of the DCP image. Finally, wavelet fusion is used to fuse the DCP image and DCP+USM image. The fusion algorithm not only further improves the visibility and texture features, but also reduces the noise of DCP+USM. Compared with other methods, quantitative analysis results show that the enhanced images have higher visibility, more details and edge information. [ABSTRACT FROM AUTHOR]

In recent decades, deep learning (DL) has become a rapidly growing research direction, redefining the state-of-the-art performances in a wide range of techniques, such as object detection and speech recognition. In the aircraft design, dynamics, and control field, many works hinge on the information-rich data-driven approach, which includes the fusion-based prognostic and health management, the airliner's flight safety monitoring, intelligent sensing, and flight control systems development. While DL provides great potentials to solve these data-driven problems, a systematic review and discussion as to how the DL has been/can be used for these problems are still missing in relation to the rapidly developing and widely used DL techniques. In this article, we aim to address this urgent issue to provide a timely overview of the state-of-the-art for applying DL to the aircraft design, dynamics, and control field. In particular, we briefly introduce five representative DL methods, i.e., deep neural network, deep autoencoder, deep belief network, convolutional neural network, and recurrent neural network. Mathematical definitions for each method are presented, and illustrative applications are also discussed. We then review the existing DL-based works that have appeared in the aircraft design, dynamics, and control field. The review efforts are divided into two major groups, i.e., the own-ship aircraft modeling, wherein the works have been/can be implemented online for the aircraft design/dynamics/control, and other airplanes research works, wherein DL-based schemes provide offline monitoring of the aircraft operation. We then summarize the data sources and DL architectures. Referring to the experiences of DL research works/techniques development in other related fields, future opportunities, challenges, and potential solutions for implementing DL in the aircraft design, dynamics, and control field are also discussed. [ABSTRACT FROM AUTHOR]