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An adaptive sliding mode fault‐tolerant control strategy is proposed for a quadrotor unmanned aerial vehicle in this article to accommodate actuator faults and model uncertainties. First, a new reaching law is proposed, with which a sliding mode control (SMC) law is constructed. The proposed reaching law is made up of a sliding variable and the distance between it and a designated boundary layer, and it can effectively suppress the unexpected control chattering while preserving the necessary system tracking performance. Then, an adaptive SMC scheme is proposed to further solve the fault and uncertainty compensation problem. The proposed adaptation law helps to prevent overestimation of the adaptive control parameters, as well as avoiding control chattering. Finally, a number of comparative simulation tests are carried out to validate the effectiveness and superiority of the proposed control strategy. The demonstrated quantitative comparison results confirm its advantages. [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]

Parkinson's disease (PD) diagnosis based on magnetic resonance imaging (MRI) is still challenging clinically. Quantitative susceptibility maps (QSM) can potentially provide underlying pathophysiological information by detecting the iron distribution in deep gray matter (DGM) nuclei. We hypothesized that deep learning (DL) could be used to automatically segment all DGM nuclei and use relevant features for a better differentiation between PD and healthy controls (HC). In this study, we proposed a DL‐based pipeline for automatic PD diagnosis based on QSM and T1‐weighted (T1W) images. This consists of (1) a convolutional neural network model integrated with multiple attention mechanisms which simultaneously segments caudate nucleus, globus pallidus, putamen, red nucleus, and substantia nigra from QSM and T1W images, and (2) an SE‐ResNeXt50 model with an anatomical attention mechanism, which uses QSM data and the segmented nuclei to distinguish PD from HC. The mean dice values for segmentation of the five DGM nuclei are all >0.83 in the internal testing cohort, suggesting that the model could segment brain nuclei accurately. The proposed PD diagnosis model achieved area under the the receiver operating characteristic curve (AUCs) of 0.901 and 0.845 on independent internal and external testing cohorts, respectively. Gradient‐weighted class activation mapping (Grad‐CAM) heatmaps were used to identify contributing nuclei for PD diagnosis on patient level. In conclusion, the proposed approach can potentially be used as an automatic, explainable pipeline for PD diagnosis in a clinical setting. [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]

Parkinson disease (PD) is a chronic progressive neurodegenerative disorder characterized pathologically by early loss of neuromelanin (NM) in the substantia nigra pars compacta (SNpc) and increased iron deposition in the substantia nigra (SN). Degeneration of the SN presents as a 50 to 70% loss of pigmented neurons in the ventral lateral tier of the SNpc at the onset of symptoms. Also, using magnetic resonance imaging (MRI), iron deposition and volume changes of the red nucleus (RN), and subthalamic nucleus (STN) have been reported to be associated with disease status and rate of progression. Further, the STN serves as an important target for deep brain stimulation treatment in advanced PD patients. Therefore, an accurate in‐vivo delineation of the SN, its subregions and other midbrain structures such as the RN and STN could be useful to better study iron and NM changes in PD. Our goal was to use an MRI template to create an automatic midbrain deep gray matter nuclei segmentation approach based on iron and NM contrast derived from a single, multiecho magnetization transfer contrast gradient echo (MTC‐GRE) imaging sequence. The short echo TE = 7.5 ms data from a 3D MTC‐GRE sequence was used to find the NM‐rich region, while the second echo TE = 15 ms was used to calculate the quantitative susceptibility map for 87 healthy subjects (mean age ± SD: 63.4 ± 6.2 years old, range: 45–81 years). From these data, we created both NM and iron templates and calculated the boundaries of each midbrain nucleus in template space, mapped these boundaries back to the original space and then fine‐tuned the boundaries in the original space using a dynamic programming algorithm to match the details of each individual's NM and iron features. A dual mapping approach was used to improve the performance of the morphological mapping of the midbrain of any given individual to the template space. A threshold approach was used in the NM‐rich region and susceptibility maps to optimize the DICE similarity coefficients and the volume ratios. The results for the NM of the SN as well as the iron containing SN, STN, and RN all indicate a strong agreement with manually drawn structures. The DICE similarity coefficients and volume ratios for these structures were 0.85, 0.87, 0.75, and 0.92 and 0.93, 0.95, 0.89, 1.05, respectively, before applying any threshold on the data. Using this fully automatic template‐based deep gray matter mapping approach, it is possible to accurately measure the tissue properties such as volumes, iron content, and NM content of the midbrain nuclei. [ABSTRACT FROM AUTHOR]

This article presents a single‐hidden‐layer feedforward network (SLFN) aided fault‐tolerant control (FTC) scheme for a class of nonlinear systems subject to actuator faults. First, the upper bounds of the norms of the unknown functions, which cover information of actuator faults and amplitude saturation degrees, are deployed. The upper bounds are approximated by SLFNs with adaptive techniques. Subsequently, a sliding mode control approach is presented to allow for prompt corrective reactions, with explicit consideration of multivariable conditions and control input constraints. Finally, simulation results based on a nonlinear Boeing 747 aircraft model are presented to demonstrate the effectiveness of the proposed FTC scheme. [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 a vector tracking loop (VTL) architecture, non‐linearities exist in discriminator functions and pseudo‐range/pseudo‐range rate measurement expressions. Generally, normalisation functions are used in discriminators to export the desired code phase or carrier frequency error and the extended Kalman filter is adopted to estimate receiver's states. This process could be accurate enough when the code phase or carrier frequency error approaches zero in the signal moderate environment but begins to distort due to non‐linearity when the tracking errors become large in harsh situations. This finally narrows the applicable range of VTL. To overcome this issue, a square‐root cubature Kalman filter (CKF)‐based VTL is designed in this study. The discriminator functions are employed directly as measurements of navigation filter, and the non‐linear expressions of discriminator functions in terms of the receiver's position, velocity, and time states are derived without normalisation. Then the CKF, which is competitive in high‐dimensional non‐linear systems, is employed in its square‐root version to estimate the position, velocity, acceleration, and time states of the receiver. Comparison trial results between traditional and proposed VTL illustrate that the proposed algorithm can not only keep a superior tracking accuracy but also improves the tracking stability of VTL in <20 dB‐Hz signal harsh circumstances. [ABSTRACT FROM AUTHOR]

In this study, three general formulations are presented for trajectory optimisation in patrolling problems. In the traditional patrolling problem, some basic assumptions are made (often implicitly). For example, it is known how many robots and how many starting depots exist. Furthermore, the starting depots are assumed to be pre‐specified. Each of the three formulations provided here relaxes some (or all) of these assumptions, hence generalising the patrolling problem. A group of robots are supposed to travel through a number of nodes (viewpoints) in such an order so that the total travel distance is minimised. This problem is, in fact, a variant of the Travelling Salesman Problem and is called Multidepot multiple Travelling Salesman Problem. The effectiveness of the approach is demonstrated by comparing the results with those in the literature. [ABSTRACT FROM AUTHOR]

Summary An adaptive sliding mode observer (SMO)–based fault‐tolerant control method taking into consideration of actuator saturation is proposed for a hypersonic scramjet vehicle (HSV) under a class of time‐varying actuator faults. The SMO is designed to robustly estimate the HSV states and reconstruct the fault signals. The adaptive technique is integrated into the SMO to approximate the unknown bounds of system uncertainties, actuator faults, and estimation errors. The robust SMO synthesis condition, which can be formulated as a set of linear matrix inequalities, is improved by relaxing structure constraints to the Lyapunov matrix. An anti‐windup feedback control law, which utilizes the estimated HSV states and the fault signals, is designed to counteract the negative effects of actuator saturation induced by actuator faults. Simulation results demonstrate that the proposed approach can guarantee stability and maintain L2 performance of the closed‐loop system in the presence of HSV actuator faults and saturation. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a retrofit fault‐tolerant tracking control (FTTC) design method with application to an unmanned quadrotor helicopter (UQH). The proposed retrofit fault‐tolerant tracking controller is developed to accommodate loss‐of‐effectiveness faults in the actuators of UQH. First, a state feedback tracking controller acting as the normal controller is designed to guarantee the stability and satisfactory performance of UQH in the absence of actuator faults, while actuator dynamics of UQH are also considered in the controller design. Then, a retrofit control mechanism with integration of an adaptive fault estimator and an adaptive fault compensator is devised against the adverse effects of actuator faults. Next, the proposed retrofit FTTC strategy, which is synthesized by the normal controller and an additional reconfigurable fault compensating mechanism, takes over the control of the faulty UQH to asymptotically stabilize the closed‐loop system with an acceptable performance degradation in the presence of actuator faults. Finally, both numerical simulations and practical experiments are conducted in order to demonstrate the effectiveness of the proposed FTTC methodology on the asymptotic convergence of tracking error for several combinations of loss‐of‐effectiveness faults in actuators. [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]

Summary: This paper investigates the attitude stabilization problem of spacecraft subject to external disturbances and actuator saturation. A novel attitude control scheme is technically proposed by incorporating the Nussbaum gain technique into backstepping design. The key idea behind this is to introduce a special Nussbaum‐type function in order to compensate for the time‐varying nonlinear terms arising from input saturation. By exploiting the dynamic surface control technique, the problem of “explosion of terms” inherent in traditional backstepping designs is effectively eliminated and the computational burden is significantly reduced. Additionally, based on the selected Nussbaum‐type function, a constructive analysis methodology is presented, which plays an important role in analyzing the stability properties of the closed‐loop system. It is then proved that the proposed control scheme can guarantee the boundedness of all closed‐loop signals. Furthermore, the unwinding phenomenon is given a simple and effective remedy by resorting to suitable choices of the attitude error variable and the virtual control law. Finally, simulation experiments are carried out to assess the effectiveness and demonstrate the advantages of the proposed control scheme. [ABSTRACT FROM AUTHOR]

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]

This paper deals with the task-space trajectory tracking control problem of robot manipulators. An improved adaptive backstepping controller is proposed to deal with the uncertainties in kinematics, dynamics, and actuator modeling. To avoid the explosion of computation in conventional backstepping techniques, a modified dynamic surface control algorithm is proposed, which guarantees the asymptotic convergence rather than the uniformly ultimately boundedness of tracking errors in conventional dynamic surface control methods. Furthermore, the expression of the [ABSTRACT FROM AUTHOR]

In order to counteract actuator faults in formation flight of multiple unmanned aerial vehicles (UAVs), this paper presents a fault-tolerant formation control (FTFC) design methodology, in which the reference generator and the finite-time convergence of FTFC gains are explicitly considered. Feasible references in response to actuator faults can be generated by considering the health and mission conditions of an overall team of UAVs. Moreover, by applying an auxiliary integrated regressor matrix and vector method, FTFC gains can converge within a finite amount of time to facilitate the fault accommodation process. Thus, the negative effects resulting from failed actuators can be compensated by the healthy/redundant actuators in UAVs. Simulation studies of UAV formation flight are carried out to exemplify the effectiveness of this design approach. [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]

Proprotein convertase subtilisin/kexin type 9 ( PCSK9) promotes atherosclerosis by increasing low-density lipoprotein ( LDL) cholesterol levels through degradation of hepatic LDL receptor ( LDLR). Studies have described the systemic effects of PCSK9 on atherosclerosis, but whether PCSK9 has local and direct effects on the plaque is unknown. To study the local effect of human PCSK9 ( hPCSK9) on atherosclerotic lesion composition, independently of changes in serum cholesterol levels, we generated chimeric mice expressing h PCSK9 exclusively from macrophages, using marrow from h PCSK9 transgenic (h PCSK9tg) mice transplanted into apoE−/− and LDLR−/− mice, which were then placed on a high-fat diet (HFD) for 8 weeks. We further characterized the effect of h PCSK9 expression on the inflammatory responses in the spleen and by mouse peritoneal macrophages (MPM) in vitro. We found that MPMs from transgenic mice express both murine (m) Pcsk9 and h PCSK9 and that the latter reduces macrophage LDLR and LRP1 surface levels. We detected hPCSK9 in the serum of mice transplanted with h PCSK9tg marrow, but did not influence lipid levels or atherosclerotic lesion size. However, marrow-derived PCSK9 progressively accumulated in lesions of apoE−/− recipient mice, while increasing the infiltration of Ly6Chi inflammatory monocytes by 32% compared with controls. Expression of h PCSK9 also increased CD11b- and Ly6Chi-positive cell numbers in spleens of apoE−/− mice. In vitro, expression of h PCSK9 in LPS-stimulated macrophages increased mRNA levels of the pro-inflammatory markers Tnf and Il1b (40% and 45%, respectively) and suppressed those of the anti-inflammatory markers Il10 and Arg1 (30% and 44%, respectively). All PCSK9 effects were LDLR-dependent, as PCSK9 protein was not detected in lesions of LDLR−/− recipient mice and did not affect macrophage or splenocyte inflammation. In conclusion, PCSK9 directly increases atherosclerotic lesion inflammation in an LDLR-dependent but cholesterol-independent mechanism, suggesting that therapeutic PCSK9 inhibition may have vascular benefits secondary to LDL reduction. Copyright © 2015 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for designing an effective fault-tolerant controller (FTC) through the combination of three control techniques: linear parameter varying (LPV), model reference adaptive control (MRAC), and a proportional-integral-derivative (PID) controller. The proposed FTC is tested in a diesel engine generator (DEG), operating as a master generation unit in an autonomous hybrid wind-diesel power system with a battery storage system (BSS). The control objectives are to regulate voltage and frequency of the DEG and to ensure covering the demand load. Frequency regulation is achieved with the help of an MRAC-LPV scheme combining a PID controller tuned by a genetic algorithm (GA) for maintaining the speed of the diesel engine (DE) in a constant value, and in consequence the frequency of the grid. Voltage magnitude control is performed through a constrained variation of the field voltage of the synchronous generator through a classic MRAC. Different operating conditions of the hybrid power system are applied in order to test the controller's robustness: (i) steady-state operation; (ii) sudden connection of a load of 0.5 MW; (iii) a three-phase fault with duration of 0.5 s; and (iv) DE's actuator fault with six different magnitudes. An improved performance is achieved by the proposed scheme over a baseline controller, IEEE type 1 AVR for voltage regulation and a governor with PI controller for frequency regulation. Dynamic models of the microgrid components are presented, and the proposed microgrid and its FTC are implemented and tested in the Simpower Systems of MATLAB/Simulink simulation environment. The simulation results showed that the use of an LPV methodology for designing the MRAC allows the online accommodation of different fault magnitudes in the DE actuator and improves the FTC system performance in comparison with the baseline controller. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]