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Fine-Grained Visual Categorization (FGVC) is a challenging task due to the large intra-subcategory and small inter-subcategory variances. Recent studies tackle this task through a weakly supervised manner without using the part annotation from the experts. Of those, methods based on bilinear pooling are one of the main categories for computing the interaction between deep features and have shown high effectiveness. However, these methods mainly focus on the correlation within one specific layer but largely ignore the high interactions between multiple layers. In this study, we argue that considering the high interaction between the features from multiple layers can help to learn more distinguishing fine-grained features. To this end, we propose a High-Order-Interaction (HOI) method for FGVC. In our HOI, an efficient cross-layer trilinear pooling is introduced to calculate the third-order interaction between three different layers. Third-order interactions of different combinations are then fused to form the final representation. HOI can produce more discriminative representations and be readily integrated with the two popular techniques, attention mechanism and triplet loss, to obtain superposed improvement. Extensive experiments conducted on four FGVC datasets show the great superiority of our method over bilinear-based methods and demonstrate that the proposed method achieves the state of the art.

Purpose This paper aims to focus on the environmental perception of unmanned platform under complex street scenes. Unmanned platform has a strict requirement both on accuracy and inference speed. So how to make a trade-off between accuracy and inference speed during the extraction of environmental information becomes a challenge. Design/methodology/approach In this paper, a novel multi-scale depth-wise residual (MDR) module is proposed. This module makes full use of depth-wise separable convolution, dilated convolution and 1-dimensional (1-D) convolution, which is able to extract local information and contextual information jointly while keeping this module small-scale and shallow. Then, based on MDR module, a novel network named multi-scale depth-wise residual network (MDRNet) is designed for fast semantic segmentation. This network could extract multi-scale information and maintain feature maps with high spatial resolution to mitigate the existence of objects at multiple scales. Findings Experiments on Camvid data set and Cityscapes data set reveal that the proposed MDRNet produces competitive results both in terms of computational time and accuracy during inference. Specially, the authors got 67.47 and 68.7% Mean Intersection over Union (MIoU) on Camvid data set and Cityscapes data set, respectively, with only 0.84 million parameters and quicker speed on a single GTX 1070Ti card. Originality/value This research can provide the theoretical and engineering basis for environmental perception on the unmanned platform. In addition, it provides environmental information to support the subsequent works.

This paper proposed a skill transmission technique for the mobile robot via learning by demonstration. When the material is transported to the designated location, the robot can show the human-like capabilities: autonomous tracking target. In this case, a skill transmission framework is designed, which the Kinect sensor is utilized to distinguish human activity recognition to create a planned path. Moreover, the dynamic movement primitive method is implemented to represent the teaching data, and the Gaussian mixture regression is utilized to encode the learning trajectory. Furthermore, in order to realize the accurate position control of trajectory tracking, a model predictive tracking control is investigated, where the recurrent neural network is used to eliminate the uncertain interaction. Finally, some experimental tasks using the mobile robot (BIT-6NAZA) are carried out to demonstrate the effectiveness of the developed techniques in real-world scenarios.

Purpose When it comes to the high accuracy autonomous motion of the mobile robot, it is challenging to effectively control the robot to follow the desired trajectory and transport the payload simultaneously, especially for the cloud robot system. In this paper, a flexible trajectory tracking control scheme is developed via iterative learning control to manage a distributed cloud robot (BIT-6NAZA) under the payload delivery scenarios. Design/methodology/approach Considering the relationship of six-wheeled independent steering in the BIT-6NAZA robot, an iterative learning controller is implemented for reliable trajectory tracking with the payload transportation. Meanwhile, the stability analysis of the system ensures the effective convergence of the algorithm. Findings Finally, to evaluate the developed method, some demonstrations, including the different motion models and tracking control, are presented both in simulation and experiment. It can achieve flexible tracking performance of the designed composite algorithm. Originality/value This paper provides a feasible method for the trajectory tracking control in the cloud robot system and simultaneously promotes the robot application in practical engineering.

PurposeRenal fibrosis is a pathological state in the progression of chronic kidney disease. Early detection and treatment are vital to prolong the survival of patients. Renal puncture examination represents the gold standard examination method for renal fibrosis, but it has several limitations. This study aims to evaluate the diagnostic performance of a novel PET radiotracer, [68Ga]Ga-fibroblast activation protein inhibitor (FAPI)-04, which specifically images fibroblast activation protein (FAP) expression for renal fibrosis.MethodsAll patients underwent renal puncture before receiving [68Ga]Ga-FAPI-04 PET/CT imaging. They then underwent [68Ga]Ga-FAPI-04 PET/CT and immunochemistry examinations, and the data obtained were analyzed.ResultsThe [68Ga]Ga-FAPI-04 PET/CT examination results showed that almost all patients (12/13) exhibited increased radiotracer uptake. The maximum standardized uptake value (SUVmax) in patients with mild, moderate, and severe fibrosis was 3.92±1.50, 5.98±1.6, and 7.67±2.23, respectively.Conclusion Compared with renal puncture examination, non-invasive imaging of FAP expression through [68Ga]Ga-FAPI-04 PET/CT can quickly show the patient’s bilateral kidney condition with high sensitivity. This can facilitate the evaluation of the patient’s disease progression, diagnosis, and the development of a treatment plan.

AIM We aimed to use data-driven glucose pattern analysis to unveil the correlation between the metrics reflecting glucose fluctuation and beta-cell function, and to identify the possible role of this metric in diabetes classification. MATERIALS AND METHODS In total, 78 participants with type 1 diabetes and 59 with type 2 diabetes were enrolled in this study. All participants wore a flash glucose monitoring system, and glucose data were collected. A detrended fluctuation function (DFF) was utilized to extract glucose fluctuation information from flash glucose monitoring data and a DFF-based glucose fluctuation metric was proposed. RESULTS For the entire study population, a significant negative correlation between the DFF-based glucose fluctuation metric and fasting C-peptide was observed (r = -0.667; P

Reinforcement learning (RL) depends on carefully engineering environment rewards. However, rewards from environments are extremely sparse for many RL tasks, challenging for the agent to learn skills and interact with the environment. One solution to this problem is to create intrinsic rewards for agents and to make rewards dense and more suitable for learning. Recent algorithms, such as curiosity-driven exploration, usually estimate the novelty of the next state through the prediction error of dynamics models. However, these methods are typically limited by the capacity of their dynamics models. In this paper, a random curiosity-driven model using deep reinforcement learning is proposed, which uses a target network with fixed weights to maintain the stability of dynamics models and create more suitable intrinsic rewards. We integrate the parametric exploration method for further promoting sufficient exploration. Besides, a deeper and more closely connected network is utilized for encoding the pixel images for policy-gradient. By comparing our method against the previous approaches in several environments, the experiments show that our method achieves state-of-the-art performance on most but not all of the Atari games.

When considering the robot application of the complex scenarios, the traditional geometric maps are insufficient because of the lack of interactions with the environment. In this paper, a three-dimensional (3D) semantic map with large-scale and accurate integrating Lidar and camera information is presented to achieve real-time road scenes. Firstly, simultaneous localization and mapping (SLAM) is performed to locate the robot position with the multi-sensor fusion of the Lidar and inertial measurement unit (IMU), and the map of the surrounding scenes is constructed while the robot is moving. Moreover, a convolutional neural networks (CNNs)-based semantic segmentation of images is employed to develop the semantic map of the environment. Following the synchronization of the time and space, the sensor fusion of Lidar and camera are used to generate the semantic labeled frame of point clouds and then create a semantic map in term of the posture. Besides, improving the capacity of classification, a higher-order 3D full connection conditional random fields (CRFs) method is utilized to optimize the semantic map. Finally, extensive experiment results evaluated on the KITTI dataset have illustrated the effectiveness of the proposed method.

This paper proposes a neural approximation based model predictive control approach for tracking control of a nonholonomic wheel-legged robot in complex environments, which features mechanical model uncertainty and unknown disturbances. In order to guarantee the tracking performance of wheel-legged robots in an uncertain environment, effective approaches for reliable tracking control should be investigated with the consideration of the disturbances, including internal-robot friction and external physical interactions in the robot’s dynamical system. In this paper, a radial basis function neural network (RBFNN) approximation based model predictive controller (NMPC) is designed and employed to improve the tracking performance for nonholonomic wheel-legged robots. Some demonstrations using a BIT-NAZA robot are performed to illustrate the performance of the proposed hybrid control strategy. The results indicate that the proposed methodology can achieve promising tracking performance in terms of accuracy and stability.

In order to realise autonomous navigation of unmanned platforms in urban or off-road environments, it is crucial to study accurate, versatile and real-time road detection methods. This study proposes an adaptive road detection method that combines lane lines and obstacle boundaries, which can be applied to a variety of driving environments. Combining multi-channel threshold processing, it is robust to lane feature detection under various complex situations. Obstacle information extracted from the grid image constructed by 3D LIDAR point cloud is used for lane feature selection to avoid interference from pedestrians and vehicles. The proposed method makes use of adaptive sliding window for feature selection, and piecewise least squares method for road line fitting. Experimental results on dataset and in real-world environments show that the proposed method can overcome illumination changes, shadow occlusion, pedestrian, vehicle interference and so on in a variety of scenes. The proposed method has good enough efficiency, robustness and real-time performance.

Summary Background Stroke is a leading cause of death and disability worldwide. Despite considerable improvements in diagnosis and treatment, little is known about the short-term and long-term prognosis after a first stroke in low-income and middle-income countries, including China. We aimed to assess the short-term and long-term risk of recurrent stroke and mortality after a first stroke for each of the major pathological stroke types. Methods This population-based cohort study included adults aged 35–74 years without disability who were recruited to the China Kadoorie Biobank (CKB). A baseline survey was conducted in ten geographical areas (five urban, five rural) in China, and participants had clinical measurements recorded. Participants were followed up by monitoring death registries and by electronic linkage to health registries and health insurance claims databases, with follow-up until Jan 1, 2017. Participants were excluded from analyses if they had a previous history of stroke, transient ischaemic attack, or ischaemic heart disease at baseline. All incidences of fatal and non-fatal stroke during the study period were recorded by type (ischaemic stroke, intracerebral haemorrhage, subarachnoid haemorrhage, and unspecified type). Primary outcome measures were 28-day mortality, recurrent stroke, major vascular events (recurrent stroke, myocardial infarction, or vascular death), vascular mortality, and all-cause mortality. Findings Of 512 715 individuals in the CKB, 489 586 participants without previous ischaemic heart disease and stroke at recruitment were included, of whom 45 732 (42 073 [92%] confirmed by brain imaging) had a stroke during the study period. The mean age was 59·3 years (SD 9·8) for participants who had a stroke (54% women) and 50·8 years (10·3) for participants with no stroke (60% women). 36 588 (80%) of the incident cases of stroke were ischaemic stroke, 7440 (16%) were intracerebral haemorrhage, 702 (2%) were subarachnoid haemorrhage, and 1002 (2%) were an unspecified stroke type. 28-day mortality was 3% (95% CI 3–4) for ischaemic stroke, 47% (46–48)for intracerebral haemorrhage, 19% (17–22; 52% for rural areas and 32% for urban areas) subarachnoid haemorrhage, and 24% (22–27) for unspecified stroke. Among participants who survived stroke at 28 days, 41% (41–42) had recurrent stroke at 5 years (ischaemic stroke 41% [41–42], intracerebral haemorrhage 44% [42–46], subarachnoid haemorrhage 22% [18–27], unspecified stroke type 40% [35–44]) and mortality at 5 years was 17% ([17–18] ischaemic stroke 16% [15–16], intracerebral haemorrhage 28% [26–29], subarachnoid haemorrhage 16% [12–20], unspecified stroke type 15% [12–19]). After a first ischaemic stroke, 91% of recurrent strokes were also ischaemic stroke; after an intracerebral haemorrhage, 56% of recurrent strokes were intracerebral haemorrhage, and 41% of recurrent strokes were ischaemic stroke. Interpretation After a first stroke, the risk of recurrence or death within 5 years was high among this population of Chinese adults. Urgent improvements to secondary prevention of stroke in China are needed to reduce these risks. Funding Wellcome Trust, Medical Research Council, British Heart Foundation, Cancer Research UK, Kadoorie Charitable Foundation, Chinese Ministry of Science and Technology, National Natural Science Foundation of China. Copyright © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.

Introduction ‘Repurposing’ existing drugs to treat COVID-19 is vital to reducing mortality and controlling the pandemic. Several promising drugs have been identified and are in various stages of clinical trials globally. If efficacy of these drugs is demonstrated, rapid, mass availability at an affordable cost would be essential to ensuring equity and access especially amongst low- and middle-income economies. Methods Minimum costs of production were estimated from the costs of active pharmaceutical ingredients using established methodology, which had good predictive accuracy for medicines for hepatitis C and HIV amongst others. Data were extracted from global export shipment records or analysis of the route of chemical synthesis. The estimated costs were compared with list prices from a range of countries where pricing data were available. Results Minimum estimated costs of production were US $0.93/day for remdesivir, $1.45/day for favipiravir, $0.08/day for hydroxychloroquine, $0.02/day for chloroquine, $0.10/day for azithromycin, $0.28/day for lopinavir/ritonavir, $0.39/day for sofosbuvir/daclatasvir and $1.09/day for pirfenidone. Costs of production ranged between $0.30 and $31 per treatment course (10–28 days). Current prices of these drugs were far higher than the costs of production, particularly in the US. Conclusions Should repurposed drugs demonstrate efficacy against COVID-19, they could be manufactured profitably at very low costs, for much less than current list prices. Estimations for the minimum production costs can strengthen price negotiations and help ensure affordable access to vital treatment for COVID-19 at low prices globally.

Background: Cooking practice has transitioned from use of solid fuels to use of clean fuels, with addition of better ventilation facilities. However, the change in mortality risk associated with such a transition remains unclear. Methods: The China Kadoorie Biobank (CKB) Study enrolled participants (aged 30–79 years) from ten areas across China; we chose to study participants from five urban areas where transition from use of solid fuels to clean fuels for cooking was prevalent. Participants who reported regular cooking (weekly or more frequently) at baseline were categorised as persistent clean fuel users, previous solid fuel users, or persistent solid fuel users, according to self-reported fuel use histories. All-cause and cardiopulmonary mortality were identified through linkage to China's Disease Surveillance Point system and local mortality records. Findings: Between June 24, 2004, and July 15, 2008, 226 186 participants living in five urban areas of China were enrolled in the CKB Study. Among 171 677 participants who reported cooking regularly (weekly or more frequently), 75 785 (44%) were persistent clean fuel users, 80 511 (47%) were previous solid fuel users, and 15 381 (9%) were persistent solid fuel users. During a mean of 9·8 (SD 1·7) years of follow-up, 10 831 deaths were documented, including 3819 cardiovascular deaths and 761 respiratory deaths. Compared with persistent clean fuel users, persistent solid fuel users had significantly higher risks of all-cause mortality (hazard ratio [HR] 1·19, 95% CI 1·10–1·28), cardiovascular mortality (1·24, 1·10–1·39), and respiratory mortality (1·43, 1·10–1·85). The excess risk of all-cause and cardiopulmonary mortality fell by more than 60% in 5 years after cessation of solid fuel use and continued to decrease afterwards. Use of ventilation was associated with lower all-cause mortality risk, even among persistent clean fuel users (HR 0·78, 0·69–0·89). Interpretation: Solid fuel use for cooking is associated with a higher risk of mortality, and cessation of solid fuel use cuts excess mortality risks swiftly and substantially within 5 years. Ventilation use also lowers the risk of mortality, even among people who persistently use clean fuels. It is of prime importance for both policy makers and the public to accelerate the transition from solid fuels to clean fuels and promote efficient ventilation to minimise further adverse health effects.

For the velocity tracking control of electric vehicle, the classical control algorithm is difficult to obtain high precision. Modern control algorithms can improve the control accuracy but they require accurate vehicle dynamics modeling. For this situation,a hierarchical model predictive control(MPC) strategy is proposed. Compared with traditional vehicles, electric vehicles are driven by motors, which are more suitable for model predictive control.In the proposed strategy, the controller can adaptively adjust the acceleration to track the expected velocity without accurate vehicle model.An upper MPC controller is designed to uses the expected velocity and the actual velocity to calculate the expected acceleration. The lower controller establishes the vehicle inverse dynamics model to calculate the expected opening degree of the accelerator pedal and the braking pressure through the Inverse dynamics model of vehicle. Simulation results demonstrate that the the controller has a fast response and accurate tracking performance without overshoot.

Ivermectin is an antiparasitic drug being investigated for repurposing against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Ivermectin showed in vitro activity against SARS-COV-2, but only at high concentrations. This meta-analysis investigated ivermectin in 23 randomized clinical trials (3349 patients) identified through systematic searches of PUBMED, EMBASE, MedRxiv, and trial registries. The primary meta-analysis was carried out by excluding studies at a high risk of bias. Ivermectin did not show a statistically significant effect on survival (risk ratio [RR], 0.90; 95% CI, 0.57 to 1.42; P = .66) or hospitalizations (RR, 0.63; 95% CI, 0.36 to 1.11; P = .11). Ivermectin displayed a borderline significant effect on duration of hospitalization in comparison with standard of care (mean difference, –1.14 days; 95% CI, –2.27 to –0.00; P = .05). There was no significant effect of ivermectin on time to clinical recovery (mean difference, –0.57 days; 95% CI, –1.31 to 0.17; P = .13) or binary clinical recovery (RR, 1.19; 95% CI, 0.94 to 1.50; P = .15). Currently, the World Health Organization recommends the use of ivermectin only inside clinical trials. A network of large clinical trials is in progress to validate the results seen to date.

To realize real-time road scene understanding, jointly detecting objects and segmenting road areas, an improved multi-task network was proposed. Based on SE-ResNeXt, dilated convolution was added to expand the image receptive field and improve the performance of the encoder. In terms of object detection, a coarse-fine optimization network was proposed, using high-level features to further refine the low-level rough estimated results, and the self-attention module was used to adaptively adjust the detection results of different scales from a global perspective view. For road detection, a pyramid pooling model was added to obtain global information, and a jump connection mode was used to combine multi-level features. A channel adjustment module was added to adjust the relationship between different channels. Experiments show that these strategies can significantly improve the detection results while increasing a small amount of reasoning time. And generalization experiment proves the effectiveness of the method. Two tasks were trained together, resulting in mutual promotion.

BackgroundCurrently, only dexamethasone, tocilizumab and sarilumab have conclusively been shown to reduce mortality of COVID-19. Safe and effective treatments will need to be both affordable and widely available globally to be used alongside vaccination programmes. This analysis will estimate and compare potential generic minimum costs of a selection of approved COVID-19 drug candidates with available international list prices.MethodsWe searched for repurposed drugs that have been approved by at least one of the WHO, FDA or NICE, or at least given emergency use authorisation or recommended for off-label prescription. Drug prices were searched for, for dexamethasone, budesonide, baricitinib, tocilizumab, casirivimab and imdevimab, and sarilumab using active pharmaceutical ingredients (API) data extracted from global shipping records. This was compared with national pricing data from a range of low, medium, and high-income countries. Annual API export volumes from India were used to estimate the current availability of each drug.ResultsRepurposed therapies can be generically manufactured for some treatments at very low per-course costs, ranging from $2.58 for IV dexamethasone (or $0.19 orally) and $4.34 for inhaled budesonide. No export price data was available for baricitinib, tocilizumab, casirivimab and imdevimab or sarilumab, but courses of these treatments are priced highly, ranging from $6.67 for baricitinib to $875.5 for sarilumab. When comparing international list prices, we found wide variations between countries.ConclusionsSuccessful management of COVID-19 will require equitable access to treatment for all populations, not just those able to pay high prices. Dexamethasone and budesonide are widely available and affordable, whilst monoclonal antibodies and IV treatment courses are more expensive.Key PointsRe-purposed drugs must be affordable worldwide to compliment COVID-19 vaccine programmes.Estimated costs/course were: dexamethasone (Oral $0.22, IV $2.58), budesonide ($4.34), baricitnib ($6.67), tocilizumab ($410.59), sarilumab ($875.70). Casirivimab and imdevimab = no data available.High drug prices will limit access.

Background and aims Observational studies have associated resting heart rate with incident diabetes. Whether the associations are causal remains unclear. We aimed to examine the shape and strength of the associations and assessed the causal relevance of such associations in Chinese adults. Methods and results The China Kadoorie Biobank enrolled 512,891 adults in China. Cox proportional hazard regression models was conducted to estimate hazard ratios (HRs) for the associations of resting heart rate with type 2 diabetes and total diabetes. Among 92,724 participants, 36 single-nucleotide polymorphisms (SNPs) related to resting heart rate were used to construct genetic risk score. We used Mendelian randomization analyses to make the causal inferences. During a median follow-up of 9 years, 7872 incident type 2 diabetes and 13,349 incident total diabetes were documented. After regression dilution bias adjustment, each 10 bpm higher heart rate was associated with about a 26% higher risk of type 2 diabetes (HR, 1.26 [95% CI, 1.23, 1.29]) and 23% higher risk of total diabetes (HR, 1.23 [95% CI, 1.20, 1.26]). Instrumental variable analyses showed participants at top quintile compared with those at bottom quintile had 30% higher risk for type 2 diabetes (HR, 1.30 [95% CI, 1.17, 1.43]), and 10% higher risk for total diabetes (HR, 1.10 [95% CI, 1.02, 1.20]). Conclusions This study provides evidence that resting heart rate is an important risk factor for diabetes risk. The results suggest that novel treatment approaches targeting reduction of high heart rate for incidence of diabetes may be worth further investigation.

Although new diagnostic techniques and treatments are increasingly updated, the clinical outcomes of CRC patients are still not encouraging with a low survival rate. N6-methyladenosine as a popular modification on mRNA is associated with multiple types of cancers. Our purpose is to evaluate gene signature and prognostic ability of N6-methyladenosine in CRC. We used the gene expression, copy number variation, simple nucleotide variation and clinical messages from The Cancer Genome Atlas database. We first identified mutation and copy number variations of N6-methyladenosine regulatory genes in both colon adenocarcinoma and rectum adenocarcinoma. Fourteen of all 17 N6-methyladenosine regulatory genes were related with higher mRNA expression, whereas deletion leads to reduced expression. Using univariate Cox regression analysis, RBM15, YTHDC2, and METTL14 genes in the rectum adenocarcinoma samples were conspicuously associated with the prognosis of patients. Based on the least absolute shrinkage and selection operator regression models, we built a 2-gene (YTHDC2 and IGF2BP3) signature of N6-methyladenosine regulators with prognostic ability. The 1-, 3-, and 5-year AUCs of this signature were all greater than 0.6, and the P-value for risk prediction for patients was also less than 0.0001. Moreover, high IGF2BP3 gene expression was significantly associated with IFN-γ in colon adenocarcinoma , and related to the azurophil granule membrane pathway in rectum adenocarcinoma. High YTHDC2 expression in colon adenocarcinoma is closely related to cell energy metabolism. In the rectum adenocarcinoma, high YTHDC2 gene expression is related to the cell centrosome pathway. In conclusion, for the first time, we identified genetic changes of N6-methyladenosine modulators and built a prognostic gene signature in CRC. Impact statement Although new diagnostic techniques and treatments are increasingly updated for CRC, the clinical outcomes of CRC patients are still not encouraging with a low survival rate. N6-methyladenosine (m6A) as a popular modification on mRNA is associated with multiple types of cancers. Our purpose is to identify gene signature and prognostic ability of m6A modulators in CRC. For the first time, we identified genetic changes of m6A modulators and built prognostic gene signature in CRC, which may provide effective targets for the diagnosis and management of CRC.

The local map can update the local environment information in real time, which provides the environment information for the local dynamic planning of the robot. In this paper, a local cost map construction method based on 3D-LIDAR and camera is proposed. We use camera to detect lane lines in structured road and 3D-LIDAR to detect road boundaries in unstructured environment, and then use DS evidence reasoning to determine the current local road information. Dynamic obstacle information in the environment is obtained through 3D point cloud data segmentation, which is fused with the road information to get 3D point cloud information in the local range and generate local cost map according to it. Experiments show that the method in this paper can accurately extract the current road information whether in structured roads or unstructured roads. The fused local cost map can enable the robot to perform reasonable local planning and complete navigation on the current road.

Background Many treatments are being assessed for repurposing to treat coronavirus disease 2019 (COVID-19). One drug that has shown promising results in vitro is nitazoxanide. Unlike other postulated drugs, nitazoxanide shows a high ratio of maximum plasma concentration (Cmax), after 1 day of 500 mg twice daily (BD), to the concentration required to inhibit 50% replication (EC50) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (Cmax : EC50 roughly equal to 14:1). As such, it is important to investigate the safety of nitazoxanide for further trials. Furthermore, treatments for COVID-19 should be cheap to promote global access, but prices of many drugs are far higher than the costs of production. We aimed to conduct a review of the safety of nitazoxanide for any prior indication and calculate its minimum costs of production. Methods A review of nitazoxanide clinical research was conducted using EMBASE and MEDLINE databases, supplemented by ClinicalTrials.gov. We searched for phase 2 or 3 randomised controlled trials (RCTs) comparing nitazoxanide with placebo or active control for 5–14 days in participants experiencing acute infections of any kind. Data extracted were grade 1–4 and serious adverse events (AEs). Data were also extracted on gastrointestinal (GI) AEs, as well as hepatorenal and cardiovascular effects. Active pharmaceutical ingredient cost data from 2016 to 2019 were extracted from the Panjiva database and adjusted for 5% loss during production, costs of excipients, formulation, a 10% profit margin and tax. Two dosages, at 500 mg BD and a higher dose of 1100 mg three times daily (TDS), were considered. Our estimated costs were compared with publicly available list prices from a selection of countries. Results Nine RCTs of nitazoxanide were identified for inclusion. These RCTs accounted for 1514 participants and an estimated 95.3 person-years-of-follow-up. No significant differences were found in any of the AE endpoints assessed, across all trials or on subgroup analyses of active- or placebo-controlled trials. Mild GI AEs increased with dose. No hepatorenal or cardiovascular concerns were raised, but few appropriate metrics were reported. There were no teratogenic concerns, but the evidence base was very limited. Based on a weighted-mean cost of US $61/kg, a 14-day course of treatment with nitazoxanide 500 mg BD would cost $1.41. The daily cost would therefore be $0.10. The same 14-day course could cost $3944 in US commercial pharmacies, and $3 per course in Pakistan, India and Bangladesh. At a higher dose of 1100 mg TDS, our estimated cost was $4.08 per 14-day course, equivalent to $0.29 per day. Conclusion Nitazoxanide demonstrates a good safety profile at approved doses. However, further evidence is required regarding hepatorenal and cardiovascular effects, as well as teratogenicity. We estimate that it would be possible to manufacture nitazoxanide as generic for $1.41 for a 14-day treatment course at 500 mg BD, up to $4.08 at 1100 mg TDS. Further trials in COVID-19 patients should be initiated. If efficacy against SARS-CoV-2 is demonstrated in clinical studies, nitazoxanide may represent a safe and affordable treatment in the ongoing pandemic.

Detection of traversable road area is one of the key tasks for unmanned platforms to achieve autonomous navigation. We propose a road detection method using lidar data based on plane assumption and graph model in this paper. Two detection algorithms for different speeds are provided to ensure the detection accuracy and to meet the real-time requirements, respectively. First, the point cloud is projected into a real image or an imaginary image for triangulation. The road points are then extended outward from the near platform according to the plane assumption. Then the dual Voronoi diagram of the triangulation is filled according to the attribute of the central element node, and the upper part of the horizon is removed. Finally, image post-processing yields the final detection results. This method perfectly combines the classification of points and the determination of road areas and uses the mature image algorithms in the processing of point cloud data. Experimental tests have been carried out on the public KITTI-Road benchmark, obtaining positive results.

Physical servo systems are affected by disturbances, uncertainties, and resource restrictions. In this paper, an event-triggered active disturbance rejection control approach is proposed to achieve position tracking of dc torque motors. An event-based sampler together with an extended state observer is introduced, which allows the joint observation of the system state, and the total disturbance induced by model uncertainty and intermittent sampling. Based on the observation results, closed-loop control is achieved with guaranteed stable tracking performance. To quantify the effect of event-based sampling, a quantitative relationship between the asymptotic upper bound of tracking error and the parameters in the event-based sampler is developed. The control strategy is applied to a dc torque motor system, and comparative experimental results indicate that for different reference signals, the proposed event-based control strategy can achieve satisfactory tracking performance with reduced sampling cost.

In this brief, an experimental and comparative study of event-based state estimation is performed for some typical event-triggering conditions. The experiments are performed on a magnetic-brake loaded permanent-magnet dc torque motor system. The comparisons are performed from three aspects: 1) performance under different average sensor-to-estimator communication rates; 2) effect of inaccurate estimates of the noise covariance matrices; and 3) computation complexity. The comparative results show that the innovation-based schedules are relatively superior to the “send-on-delta” schedules in terms of estimation quality, and the deterministic schedules have enhanced estimation performance compared with stochastic counterparts. The estimators considered are sensitive to the estimates of the noise covariance matrices, and retain a similar level of computation complexity as that of the standard Kalman filter.

With the demand for energy efficiency in electrohydraulic servo systems (EHSS), the separate meter-in and separate meter-out (SMISMO) control system draws massive attention. In this paper, the SMISMO control system is decoupled completely into two subsystems by the proposed indirect adaptive robust dynamic surface control (IARDSC) method. Indirect adaptive robust control (IARC) is proposed to address the internal parameter uncertainties and external disturbances. Dynamic surface control (DSC) is utilized in the design procedure of IARC to deal with the inherent ‘explosion of terms’ problem. The proposed IARDSC simplifies the design procedure and decreases the computational cost of the controller. Besides, a faster parameter estimation scheme is proposed to adapt to the parameter change for a better estimation performance. Finally, experimental results show that the proposed IARDSC can achieve a good parameter estimation and trajectory tracking performance. Meanwhile, two energy saving techniques are discussed.

This study aims to investigate the problem of attitude control for a spacecraft with inertial uncertainties, external disturbances, and communication restrictions. An event-triggered active disturbance rejection control approach is proposed for attitude tracking of the spacecraft. An event-triggered mechanism is introduced together with an extended state observer to jointly monitor the system states and total disturbances. The observation error is proved to be uniformly bounded. Based on the proposed control scheme, the integrated tracking system is shown to be asymptotically stable, implying successful attitude tracking of the spacecraft for the desired motion. Numerical results illustrate the effectiveness of the control strategy in achieving satisfactory tracking performance with a reduced data-transmission cost.

Background Lean body mass has been identified as a key determinant of left ventricular mass and wall thickness. However, the importance of lean body mass or other body‐size measures as normative determinants of carotid intima‐media thickness ( cIMT ), a widely used early indicator of atherosclerosis, has not been well established. Methods and Results Carotid artery ultrasound measurements of cIMT and carotid artery plaque burden (derived from plaque number and maximum size) and measurements of body size, including height, body mass index, weight, body fat proportion, and lean body mass ([1−body fat proportion]×weight), were recorded in 25 020 participants from 10 regions of China. Analyses were restricted to a healthy younger subset (n=6617) defined as never or long‐term ex‐regular smokers aged cIMT , but was not associated with plaque burden: overall, each 10 kg higher lean body mass was associated with a 0.03 (95% CI , 0.03–0.04) mm higher cIMT ( P =5×10 −33 ). Fat mass, height, and other body‐size measures were more weakly associated with cIMT . Conclusions The strong association of lean body mass with cIMT, but not with plaque burden, in healthy adults suggests a normative relationship rather than reflecting atherosclerotic pathology. Common mechanisms may underlie the associations of lean body mass with cIMT and with nonatherosclerotic vascular traits.

Road detection is the basic component of many intelligent vehicle systems. In this paper, a robust multi-lane detection method based on binocular vision is proposed. First, a fast estimation technique of the Stixel World, which is the outside environment representation of stereo-vision, is developed. Taking advantage of the estimate for free space under the plane road hypothesis, the proposed method is robust against on-road obstacles when detecting lane markings. Then, the bird-view of the transitable area is obtained through Inverse Perspective Mapping (IPM) Transform; Steerable filter, parallel parabolas modal and RANdom SAmple Consensus (RANSAC) technique are introduced for multi-lane fitting. Experimental results in real urban driving situations indicate that our algorithm is robust under various conditions.

e14510 Background: CD4 is highly and uniformly expressed T-cell lymphomas (TCL) including PTCL and CTCL, suggesting its potential as a surface target for CAR-T therapy. However, there is a significant risk of potential antigen masking by CAR introduced into tumor cells, which thereby leads to escape of tumor cells from recognition by CAR-T cells. For example, Ruella M et al ( Nat Med, 2018) reported that contaminating malignant B cells transduced with anti-CD19 CAR (called CAR-B cells) in the manufacturing process led to the CD19 antigen masking by CAR molecules. Hence, CAR-B tumor cells could not be recognized by CAR-T cells, which resulted in relapse of CAR expressing B lymphoma cells. A similar antigen masking effect might occur for the anti-CD4 CAR-T product. Methods: To identify single-chain variable fragment (scFv) without antigen-masking effect, large panels of fully human monoclonal antibodies were converted to format of CAR modality with the scFv-4-1BB-CD3z structure and introduced into CD4+ and CD8+ T cells. After lentiviral transduction, residual CD4+ T cells were quantified and the CARs with complete elimination of CD4+ T cells were selected for further validation. The selected CAR constructs were then introduced into CD4+ TCL cells HH (CAR-HH cells) to mimic the potential risk of introducing CAR into contaminating malignant T cells in the manufacturing process. Thereafter, CAR-HH cells were subject to in vitro killing assay by LB1901. Last, identified CAR constructs were further tested for in vitro and in vivo anti-tumor efficacy and off-target binding and killing. Results: Introduction of LB1901 CAR into CD4+ and CD8+ T cells led to complete elimination of CD4+ T cells, suggesting no masking effect of CAR on CD4 antigen. Furthermore, the introduction of CAR into CD4+ HH cells did not protect HH cells from being recognized and eliminated by LB1901, further confirming that the CAR modality of LB1901 does not mask CD4 antigen. An in vivo anti-tumor efficacy study showed that LB1901 exhibited dose-dependent anti-tumor activity without significant adverse effect. As low as 0.3 million CAR+ cells completely suppressed tumor growth, suggesting the potent anti-tumor activity by LB1901. Immunohistochemical analysis of normal tissues with LB1901 scFV binder showed no off-target binding. Furthermore, no killing toward CD4- cell lines and primary cells derived from vital organs or antigen-independent cytokine release was observed in vitro. Conclusions: Altogether, the in vitro and in vivo studies showed that LB1901 did not “mask” the CD4 antigen but exhibited potent anti-tumor activity without off-target effects. A phase 1 study of LB1901 CAR-T in patients with relapsed or refractory PTCL or CTCL is ongoing in the US to assess the safety and tolerability of LB1901 CAR-T (NCT04712864).

This paper presents an observer-based robust controller equipped with a fast friction estimator for a 6-DOF parallel electrical manipulator (PEM) in the joint-task space. First, the joint-task space framework for a 6-DOF PEM is constructed, which not only avoids time-consuming forward kinematics, but also contains the dynamic model. Second, a nonlinear disturbance observer is designed to estimate the deviation uncertainties which are modeled as disturbances in the proposed joint-task space. In addition, a fast friction estimator based on Lyapunov design method is proposed to estimate the uncertain friction caused by the spiral drive way of the electrical cylinder fast and effectively. The sufficient condition for the convergence of the estimation errors is given. The robust control scheme by employing the disturbance observer and the fast friction estimator is proposed based on the Lyapunov design method to guarantee a practical stability under uncertainties. Finally, the friction property identification method and comparative experimental results are presented to show the effectiveness of the proposed scheme.

In this article, an electro-hydraulic-based fatigue testing system is considered. Electro-hydraulic system suffers from internal parameter uncertainties and external disturbances. Indirect adaptive robust control has been proposed to address these problems; however, since the mathematical model of this electro-hydraulic system has an order of four, the inherent “explosion of terms” problem makes the controller hard to design, and a large computational cost is needed. Thus, dynamic surface control is utilized in the design procedure of indirect adaptive robust control. The proposed indirect adaptive robust dynamic surface controller simplified the design procedure and decreased the computational cost of the controller. Furthermore, due to the fatigue of the elastic load, fast internal parameter change is encountered in this system. A fast parameter estimation scheme is proposed to adapt to the parameter change for a better estimation performance. For the mechanical system, a change in the hydraulic circuit is made to address the problem of chattering and impact caused by huge elastic load and to simplify the system model at the same time. Simulations and experiments show that the proposed controller achieves a better parameter estimation and trajectory tracking performance.

The wheel-legged hybrid robot (WLHR) is capable of adapting height and wheelbase configuration to traverse obstacles or rolling in confined space. Compared with legged and wheeled machines, it can be applied for more challenging mobile robotic exercises using the enhanced environment adapting performance. To make full use of the deformability and traversability of WHLR with parallel Stewart mechanism, this paper presents an optimization-driven planning framework for WHLR with parallel Stewart mechanism by abstracting the robot as a deformable bounding box. It will improve the obstacle negotiation ability of the high degree-of-freedoms robot, resulting in a shorter path through adjusting wheelbase of support polygon or trunk height instead of using a fixed configuration for wheeled robots. In the planning framework, we firstly proposed a pre-calculated signed distance field (SDF) mapping method based on point cloud data collected from a lidar sensor and a KD -tree-based point cloud fusion approach. Then, a covariant gradient optimization method is presented, which generates smooth, deformable-configuration, as well as collision-free trajectories in confined narrow spaces. Finally, with the user-defined driving velocity and position as motion inputs, obstacle-avoidancing actions including expanding or shrinking foothold polygon and lifting trunk were effectively testified in realistic conditions, demonstrating the practicability of our methodology. We analyzed the success rate of proposed framework in four different terrain scenarios through deforming configuration rather than bypassing obstacles.

Feline parvovirus virus (FPV) causes severe diarrhea and leukopenia in felines, and threatening the health of wild and domestic felines. Currently, specific drugs to treat FPV have not yet been developed. In this study, IgG was extracted from inactivated FPV-immunized dog sera. Canine F(ab')2 fragments were obtained from pepsin-digested IgG and then purified by protein-G column chromatography. The results showed that canine immunoglobulin F(ab')2 fragments showed efficient neutralizing activity in vitro against FPV and had therapeutic and prophylactic effects in FPV infected cats. The anti-FPV-specific F(ab')2 fragment can significantly alleviate the clinical symptoms of FPV infected cats and reduce the viral loads of the intestinal tract. These results indicated that the F(ab')2 fragment prepared from inactivated FPV-immunized felines may be used as a prophylactic and therapeutic agent for diseases caused by FPV.

Rationale Little evidence from large-scale cohort studies exists about the relationship of solid fuel use with hospitalization and mortality from major respiratory diseases. Objectives To examine the associations of solid fuel use and risks of acute and chronic respiratory diseases. Methods A cohort study of 277,838 Chinese never-smokers with no prior major chronic diseases at baseline. During 9 years of follow-up, 19,823 first hospitalization episodes or deaths from major respiratory diseases, including 10,553 chronic lower respiratory disease (CLRD), 4,398 chronic obstructive pulmonary disease (COPD), and 7,324 acute lower respiratory infection (ALRI), were recorded. Cox regression yielded adjusted hazard ratios (HRs) for disease risks associated with self-reported primary cooking fuel use. Measurements and Main Results Overall, 91% of participants reported regular cooking, with 52% using solid fuels. Compared with clean fuel users, solid fuel users had an adjusted HR of 1.36 (95% confidence interval, 1.32–1.40) for major respiratory diseases, whereas those who switched from solid to clean fuels had a weaker HR (1.14, 1.10–1.17). The HRs were higher in wood (1.37, 1.33–1.41) than coal users (1.22, 1.15–1.29) and in those with prolonged use (≥40 yr, 1.54, 1.48–1.60; Conclusions Among Chinese adults, solid fuel use for cooking was associated with higher risks of major respiratory disease admissions and death, and switching to clean fuels or use of ventilated cookstoves had lower risk than not switching.

In this paper, a fault-tolerant control method based on deep learning is proposed for multi displacement sensor fault of a wheel-legged robot with new structure. Unlike most methods that only detect a single sensor, the proposed method can detect a large number of sensors simultaneously and rapidly. The residual error is generated by sensor values and the prediction model which is established by deep belief network(DBN) in deep learning, to detect faults and locate faulty sensors. Then, by using other non-faulty sensor information to reconstruct the signal through the neural network and combining with the coupling relationship of the 6-DOF platform, the fault sensor signal can be estimated accurately and the error accumulation problem can be also solved. Comparing the two algorithms of neural network and support vector machine(SVM), the reconstruction signal of neural network has higher accuracy. So, the performance of the wheel-legged robot can be guaranteed within a safety range. It is proved that the proposed method has high reliability and stability.

As an important part of environmental perception, maps guarantee the accuracy of intelligent robots in navigation, localization and path planning. The traditional 3D maps mainly focus on the spatial structure of the objects, which lacks the semantic information. A method is proposed in the paper, this method combines convolutional neural networks (CNNs) and Simultaneous Localization and Mapping (SLAM) to create global dense 3D semantic maps for indoor scenes. The deep neural network that includes convolution and deconvolution is designed to predict semantic category of every pixel. RGB-D camera is used to obtain scene information, accomplish localization and build 3D maps simultaneously. the semantic information is integrated into the 3D scene, we present an octree map method to replace traditional point clouds method, which can reduce the error from pose estimation and single frame labeling. By this method, the accuracy of semantic information is greatly improved.

A highly efficient Synchronous Dynamic Random Access Memory (SDRAM) controller supporting variable-length burst access and batch process for discrete reads is proposed in this paper. Based on the Principle of Locality, command First In First Out (FIFO) and address range detector are designed within this controller to accelerate its responses to discrete read requests, which dramatically improves the average Effective Bus Utilization Ratio (EBUR) of SDRAM. Our controller is finally verified by driving the Micron 256-Mb SDRAM MT48LC16M16A2. Successful simulation and verification results show that our controller exhibits much higher EBUR than do most existing designs in case of discrete reads.

In the continuous casting of steel billets, mould oscillation is a key process. The mould driven by a hydraulic cylinder is a big inertia system. Its trajectory is required to track a specific oscillation rule, but the tracking performance will be influenced by the molten steel weight. In this paper, dynamic pressure feedback (DPF) is proposed as compensation for the PID controller, to eliminate the adverse effect caused by the variation of the molten steel weight. The iterative feedback tuning (IFT) method is applied to tune the PID controller parameters and the DPF coefficient. In contrast to common IFT tuning structure, this paper designs a new scheme to estimate the cost function gradient with respect to the controller parameters. Numerical simulations and experimental results show the effectiveness of the DPF compensation strategy and the proposed parameter tuning method.

To reduce the computation and to improve the performance of object detection and tracking algorithm with object appearance variation, a tracker based on sparse eigenbasis is proposed. According to the compressive sensing theory, the objects are described in a low-dimensional sub-space representation based on Karhunen–Loeve transform learned online. Meanwhile, combining the Bayesian inference, an adaptive object tracker is presented. First, the authors represent the appearance of the object in a low-dimensional sub-space, then the authors obtain the optimal estimation of the state parameters by Bayesian inference. Finally, the authors update the eigenbasis space using the optimal observations. Experimental results show that the proposed method is able to track the objects effectively and robustly under temporary occlusion and large illumination changes.

This paper proposes a new unsupervised image segmentation method by using Bhattacharyya distance-based irregular pyramid, termed as ‘BDIP’ algorithm. The proposed BDIP algorithm obtains a suboptimal labelling solution under the condition that the number of segments is not manually given. It hierarchically builds each level of the irregular pyramid, with the result that the final segments emerge as they are represented by single nodes at certain levels. The BDIP algorithm employs Bhattacharyya distance to estimate the intra-level similarity at higher pyramidal levels so as to improve the accuracy and robustness to noise. Furthermore, an adaptive neighbour search method is proposed such that the BDIP algorithm can self-determine the number of segments. This method considers not only the graphic constraint, but also the similarity constraint in the sense that a candidate node is selected as a neighbour of the centre node if there is no boundary evidence between these two nodes. With the pyramidal accumulation, this evaluation is aggregated into the approximately global evidence, based on which the number of segments can be self-determined. Experimental results have shown that this proposed BDIP algorithm outperforms other benchmark segmentation algorithms in terms of segmentation accuracy, labelling cost and robustness to noise.

The hydraulically driven shaking table has been developed for damping characteristic experiments on shock absorbers in this paper. The damping characteristics and corresponding experiments on the double-tube hydraulic absorber are analyzed firstly, and its damping force model is also built. Then the working principle is introduced, and the structure and components of the physical shaking table are described, especially the hydraulic actuator, composed of servo valve controlled asymmetric cylinder with damping load, is analyzed. Considering parameter uncertainty, nonlinearity, asymmetry and time-variation of such hydraulic system, as well as repeatability of desired trajectory, an open-closed-loop ILC scheme is designed to solve the challenging control issue. The simulation and experiments are developed to verify the feasibility of working principle and the superiority of proposed control method. The hydraulically driven shaking table developed by discussed electro-hydraulic control technology has been applied in actual damping characteristic experiments for a serial of shock absorbers, and achieved satisfactory testing performance.

Background Self‐rated health ( SRH ) is a strong predictor of mortality in different populations. However, the associations between SRH measures and risk of ischemic heart disease ( IHD ) have not been extensively explored, especially in a Chinese population. Methods and Results More than 500 000 adults from 10 cities in China were followed from baseline (2004–2008) through December 31, 2013. Global and age‐comparative SRH were reported from baseline questionnaires. Incident IHD cases were identified through links to well‐established disease registry systems and the national health insurance system. During 3 423 542 person‐years of follow‐up, we identified 24 705 incident cases of IHD . In multivariable‐adjusted models, both global and age‐comparative SRH was significantly associated with incident IHD . Compared with excellent SRH , the hazard ratios for good, fair, and poor SRH were 1.02 (95% confidence interval [CI], 0.98–1.07), 1.32 (95% CI, 1.27–1.37), and 1.76 (95% CI, 1.68–1.85), respectively. Compared with better age‐comparative SRH , the hazard ratios for same and worse age‐comparative SRH were 1.23 (95% CI, 1.19–1.27) and 1.78 (95% CI, 1.70–1.86), respectively. The associations persisted in all subgroup analyses, although they were slightly modified by study location, education, and income levels. Conclusions A simple questionnaire for self‐assessment of health status was significantly associated with incident IHD in Chinese adults. Individuals and healthcare providers can use SRH measures as a convenient tool for assessing future IHD risk.

BACKGROUND: Adherence to a combination of healthy lifestyle factors has been related to a considerable reduction of cardiovascular risk in white populations; however, little is known whether such associations persist in nonwhite populations like the Asian population. OBJECTIVES: This study aimed to examine the associations of a combination of modifiable, healthy lifestyle factors with the risks of ischemic cardiovascular diseases and estimate the proportion of diseases that could potentially be prevented by adherence to these healthy lifestyle patterns. METHODS: This study examined the associations of 6 lifestyle factors with ischemic heart disease and ischemic stroke (IS) in the China Kadoorie Biobank of 461,211 participants 30 to 79 years of age who did not have cardiovascular diseases, cancer, or diabetes at baseline. Low-risk lifestyle factors were defined as nonsmoking status or having stopped smoking for reasons other than illness, alcohol consumption of

For the control method based on SLIP model and the trisection method of quadruped robot can not be applied in the parallel quadruped wheel-foot robot, a new walking control method which can control body posture according to ZMP theory is put forward. The problem that robot can easily tip during trotting is solved by selecting landing points. Mechanical structure of the parallel quadruped wheel-foot robot is presented. Gait planning algorithm is shown in detail. Virtual prototyping based on Matlab and Adams integrated simulation platform is built. The simulation results show the effectiveness of the new control algorithm.

In this work, an event-triggered adaptive robust controller (ET-ARC) design is considered for a continuous-time nonlinear system combined with structural uncertainties and parameter uncertainties. In the event-trigger scheme, the controller only can obtain the sampled date of the output measurement at some certain instants determined by the designed triggering condition and therefore the performance of the ET-ARC depends on the triggering condition directly. Thus, in this work, for the adaptive robust control scheme, an event-triggered transmission strategy is proposed, which relies on the output measurement of the nonlinear system. Then, based on this strategy, the design idea of the adaptive robust controller is presented such that the tracking performance is guaranteed. Furthermore, based on a more general assumption, we provide a possible formation of control signal which satisfies the requirement proposed.

Obstacle information detection is a prerequisite for safety and stability driving of unmanned platforms in the unknown environments. Joint detection of the obstacle using the 3D laser radar (3D LADAR) and camera is proposed in this paper. Firstly, joint calibration between 3D LADAR and camera based on the planar feature method is done. Secondly, laser point cloud is clustered to identify the number of obstacles, and then detected image is divided into the corresponding small regions according to the number of clusters. Finally, we use the image processing technology to detect the obstacle areas. The experiments in the real world are implemented to show that the proposed approach can effectively detect the size of the obstacles.

The position tracking control problem of electrical cylinder in the presence of nonlinear friction load, inertia load and nonlinear disturbance due to its transmission process is addressed in this paper. To compensate all of these disturbances in the case of servo motion, active disturbance rejection control is used to make a continuous and precise transition. Tracking differentiator is used for arranging the transition process to avoid overshoot. Extended state observer is built to estimate the unmeasurable internal disturbance, parametric uncertainties and external uncertainty for a guaranteed robust performance. Nonlinear state error feedback is designed to combine the error feedbacks more reasonable. Simulation and experimental results show that active disturbance rejection controller shows very strong robustness and adaptability compared with traditional PID control algorithm.

In this paper, a fault tolerant control method based on active disturbance rejection control (ADRC) and radial basis function neural network (RBFNN) is proposed for a class of multi-input-multi-output nonlinear system with actuator faults, components faults and sensor faults. The proposed method does not rely on the plant model. By regarding the faults and plant uncertainties as the disturbance, through the observation of extended state observer and the compensation of feedback control signal, this method achieves the fault tolerance control of the plant with component fault and actuator fault. For sensor faults, in this work, radial basis function neural network is applied to estimate the real output of the system. Then this output estimation is utilized by active disturbance rejection control to achieve the fault tolerance of sensor. Finally, the effectiveness of the proposed method is validated by the simulation results of the three-tank system.