Colorectal cancer remains a pressing challenge in global health, necessitating advanced biological models and analytical methodologies. Tumor organoids (tumoroids) have emerged as a compelling platform for cancer research, owing to their capacity to replicate the genetic and structural complexity of human tissues. However, extracting meaningful gene regulatory insights from bulk ribonucleic acid (RNA) sequencing data derived from tumoroids remains nontrivial due to cellular heterogeneity and temporal variation. We propose, for the first time, a comprehensive Bayesian framework to model gene expression dynamics throughout the developmental trajectory of colorectal tumoroids. We introduce a nonparametric Dirichlet process mixture model (DPMM) to cluster genes based on temporal expression patterns and a sparse regression scheme, incorporating Horseshoe+ priors, to construct gene regulatory networks (GRNs) among identified clusters. The proposed approach demonstrates robust performance in capturing high-dimensional relationships, enabling elucidation of key regulatory mechanisms in tumor progression. Our results offer valuable insights for personalized treatment and underscore the utility of Bayesian methods in complex biological systems.

In real-world graphs, node attributes are often incomplete due to acquisition costs or privacy restrictions, reducing representation quality and harming downstream predictions in graph neural networks (GNNs). A common remedy is feature-propagation-based imputation. However, cold-start effects arising from attribute resetting and low-degree nodes impede effective propagation and convergence in these methods. To address these challenges, we propose AttriReBoost (ARB), a propagation-based method that mitigates cold-start issues in attribute-missing graphs. ARB enhances global feature propagation (FP) by redefining initial boundary conditions and strategically integrating virtual edges, thereby improving node connectivity and ensuring stable and efficient convergence. The method supports gradient-free attribute reconstruction with low computational overhead, and we provide a rigorous convergence analysis. Extensive experiments on several real-world benchmark datasets demonstrate the effectiveness of ARB, achieving an average accuracy improvement of 5.11% over state-of-the-art methods. In addition, ARB exhibits remarkable computational efficiency, processing a large-scale graph with 2.44 million nodes in just 16 s on a single GPU. Our code is available at https://github.com/limengran98/ARB.

Single-cell RNA sequencing (scRNA-seq) has emerged as a transformative omics technology for cell type identification in cancer diagnostics, enabling high-throughput parallel generation of cellular-resolution data that revolutionizes precision medicine. However, scRNA-seq data are frequently compromised by prevalent drop-out events due to limitations in sample quality and technical bottlenecks, which cause a large number of the results lost. To address this challenge, we propose a nonlinear imputation via state transition process (NISP) method for the diffusion and imputation of missing values in single-cell sequencing data. Our results demonstrate that the NISP framework effectively preserves nonlinear characteristics inherent to biological state transitions, enabling to recover more than 50% of missing values and remove more than 98% of noise. Therefore, NISP exhibits superior sensitivity in missing value imputation and significantly enhances the structural clarity of post-imputation datasets. Finally, validation using spatiotemporal transcriptomic arrays derived from colorectal cancer organoids further corroborates the capability of NISP to accurately capture the intrinsic manifold structure of cellular states. The result shows the significant potential of NISP in biological applications, notably its pivotal role in elucidating the mechanisms driving tumorigenesis and cancer progression.

Engineered assembloids fabricated from tissue spheroids hold immense promise for developmental biology, disease modeling, and regenerative medicine. However, fabricating heterogeneous assembloids with precise spatial patterning remains a critical bottleneck, often reliant on manual pipetting that lacks scalability and reproducibility. While magnetic microrobotics, which transforms spheroids into controllable robots, offers a non-invasive alternative, it faces a fundamental challenge: the global input of magnetic actuation. A single command moves all robots simultaneously, leading to coupled motion and frequent assembly failures. Here, we present a collaborative control framework that overcomes this limitation by leveraging local constraints and a novel motion decoupling strategy. We reformulate the high-dimensional, coupled multi-robot planning problem into a low-dimensional aggregate space, effectively transforming the assembly task into a dynamic sequential decision problem. This framework, coupled with a graph-based dynamic path optimization algorithm, enables deterministic, collision-free assembly. Experimental validation demonstrates a 78.57% higher success rate and a 33.20% higher assembly efficiency compared to conventional strategies. This work establishes a foundational engineering principle for assembloid fabrication, transitioning the process from a qualitative experiments to a controllable and programmable engineering discipline, thereby unlocking the potential for deterministic construction of complex biological structures.

Control Barrier Functions (CBFs) have emerged as a prominent approach to designing safe navigation systems of robots. Despite their popularity, current CBF-based methods exhibit some limitations: optimization-based safe control techniques tend to be either myopic or computationally intensive, and they rely on simplified system models; conversely, the learning-based methods suffer from the lack of quantitative indication in terms of navigation performance and safety. In this paper, we present a new model-free reinforcement learning algorithm called Certificated Actor-Critic (CAC), which introduces a hierarchical reinforcement learning framework and well-defined reward functions derived from CBFs. We carry out theoretical analysis and proof of our algorithm, and propose several improvements in algorithm implementation. Our analysis is validated by two simulation experiments, showing the effectiveness of our proposed CAC algorithm.

Time-optimal path generation is critical for maximizing throughput in high-speed PCB assembly, yet existing approaches predominantly focus on geometric distance minimization, overlooking the fundamental impact of acceleration dynamics and multi-axis coordination on temporal efficiency. This study addresses this gap by introducing a physics-based time estimator that explicitly models trapezoidal acceleration profiles for synchronized X/Y/Z/R-axis motions, enabling precise performance evaluation under realistic kinematic constraints. Experimental validation on production PCBs demonstrates that the proposed estimator achieves much higher estimation accuracy, outperforming conventional methods. When integrated as the objective function in multi-chromosome genetic algorithm optimization, time-optimal solutions effectively reduce actual movement times compared to distance-optimal baselines, despite requiring longer travel paths. These findings confirm the time-optimal estimator’s superiority over pure distance minimization, proving that peak efficiency is achieved by balancing travel distance with movement speed.

In surface mount technology (SMT) assembly, the increasing complexity and miniaturization of electronic components pose critical challenges to balancing placement precision and production efficiency. This paper presents a quality-efficiency driven scheduling and process co-optimization (SPCO) methodology for the pick-and-place (PAP) process in SMT production lines. Leveraging a cyber-physical system framework integrated with automated optical inspection, the proposed approach dynamically couples offline scheduling with online process capability feedback to achieve adaptive allocation of components. A precision-aware SPCO model is formulated to assign components to placement heads based on real-time process capability indices, ensuring compliance with stringent precision constraints. To enable real-time deployment, a precision-prioritized allocation heuristic (PPAH) is introduced, supporting component-head assignments under heterogeneous head capabilities. Experiments on industrial datasets demonstrate that PPAH completely eliminates precision violations while improving the overall process capability margin by 2.6-fold compared to state-of-the-art benchmarks, with only a moderate increase in total PAP time. These results validate the effectiveness of the proposed co-optimization strategy in improving first-pass yield and robustness in high-mix SMT environments.

As an important part of the motion system in high-end manufacturing equipment, dual-drive gantry stage urgently requires higher performance. Planar rapid positioning is a classical application of the gantry stage, while unmodeled dynamics, coupled dynamics of mechanism, and conflict between rapidity and precision bring many difficulties into controller design. In this article, a coupled dynamic model is established and transformed for positioning to provide better guidance for controller design under the rotation mode of the cross beam. A composite positioning control method consisting of a high-speed moving section and a high-precision positioning section is proposed for linearmotors on the gantry to achieve both fast response and precise control simultaneously. In addition, a synchronization scheme is proposed for the dual-driven cross beam with coupled dynamics to enhance the positioning precision of the workbench and balance of the beam. Experiments are conducted on the$X$-axis with one linearmotor for the composite positioning method and$Y$-axis with two linearmotors for the synchronization scheme combined with the composite positioning control separately, through which the effectiveness and validity of the proposed method are verified.Note to Practitioners—Planar positioning of dual-drive gantry stage is a significant technical problem in industrial applications, which pursues the ultimate speed and precision performance. In this paper, a dynamic model with rotational mode is established and transformed for positioning in an intuitive way. In addition, a composite positioning method is proposed for linearmotors on the stage to achieve high-speed and high-precision performance in the case of common nonlinear dynamics and disturbances. And then, to achieve high positioning performance of the cross beam while maintaining the balance, a synchronous scheme incorporated with the composite positioning method is presented, based on the transformed model. In industrial scenarios, the proposed method is very practical since a precise, rapid and stable point-to-point planar motion can be obtained only with conventional dynamic models and parameters, and it is crucial for production efficiency and quality.

Organoids are cell clusters cultured in vitro that maintain the structure and function of the donor organs. They have found important applications in biomedicine, such as drug screening and personalized therapy. However, conventional organoid culture methods lack control of physical properties like size and distribution, leading to increased heterogeneity and very low batch-to-batch reproducibility, which significantly limits their widespread use. Controlling these properties at the microscale is challenging, particularly for fragile fragments, which are the main source for culturing organoids. To address this issue, we present a robotic micromanipulation system that allows operators to select fragments of particular sizes and automatically transfer them into a customized in-situ organoid chip (IOC) for culture. The chip was designed with microwell arrays to uniform the culture environment and facilitate imaging analysis. The transfer of fragments is modeled based on computational fluid dynamics (CFD) and is enabled by designing a robust model predictive control (RMPC) framework. Simulation and experiment results demonstrated the effectiveness of the model and controller. In colorectal cancer organoid culture experiments, our system significantly improved the morphological homogeneity of organoids. Note to Practitioners—Organoids have been demonstrated to be one of the most promising in vitro models. Lacking control of its size and distribution results in significant heterogeneity and low batch-to-batch reproducibility, which limits its wide uses. Here, we report a robotic micromanipulation system that allows operators to select fragments of particular sizes and morphologies and automatically transfer them into a customized organoid chip for culture. The results of colorectal cancer organoids culture experiments verified the effectiveness of our system in reducing the morphological heterogeneity among organoids.

In the field of smart surface mount technology (SMT) production, integrating machines through a cyber-physical system (CPS) architecture holds significant potential for improving assembly quality and efficiency. However, fully unifying inspection and production systems to effectively address assembly-related quality issues remains a challenge. This study seeks to close these gaps by introducing collaborative optimization methods to ensure seamless operations. The research is driven by the need for precise control of key assembly parameters, such as placement height, x-offset, y-offset, rotation angle deviations, and blowing durations, all of which are major contributors to defects. To address these challenges, we propose a self-adaptive collaborative optimization (SACO) framework that prioritizes enhancements based on their impact on both quality and efficiency. The SACO framework combines customized Bayesian optimization and particle swarm optimization techniques, allowing for dynamic adjustments to process parameters, guided by real-time data from automatic optical inspection (AOI) systems. The primary goal of this study is to reduce defects and improve efficiency in the SMT assembly process through these targeted improvements. Experimental results validate the effectiveness of the proposed methods, demonstrating significant advancements in placement accuracy and overall assembly efficiency. Our findings confirm that the SACO framework provides a robust solution to persistent challenges in SMT production, addressing critical gaps in quality control and process optimization.

Printed circuit board assembly line scheduling (PCBALS) is a difficult task in the electronic industry for assembly lines using surface mounters, which is critical for production efficiency. This is a special type of line optimization problem that uses different allocation techniques, resulting in wide differences in assembly times between machines. This article proposes a hyper-heuristic optimizer embedded with a multifeature fusion ensemble estimator (HHO-MFEE) for PCBALS using linear-aligned-heads surface mounters. The objective and constraints of the problem are discussed, and a min-max integer model for small-scale problems is built. At the hyper-heuristic low level, seven data- and target-driven heuristics are presented for allocating components to different machines. Strategies for duplicated conditions with component types and placement points allocation are proposed to improve the applicability of the algorithm and the quality of the solution. An ensemble assembly time estimator that incorporates the coding of multifeatures, including estimated subobjectives, is proposed for evaluating the quality of the solution. Experimental results show that: 1) the gaps between the solution from HHO-MFEE and the optimal solution of the model are 3.44%~7.28% for small-scale data; 2) the proposed time estimator has higher accuracy than regression and heuristic-based ones, with mean absolute error of 2.01% and 3.43% for training and testing data, respectively; and 3) HHO-MFEE is better than other state-of-the-art algorithms, with average improvement of 7.21%~9.47%.

This article proposes a novel safety-critical control (SCC) framework based on embedded control barrier functions (EMB-CBF-SCC) for high-order strict-feedback nonlinear affine control systems. It is aimed at reconciling the potential conflict between predesigned desired trajectory and multiple safety constraints that could have different high relative degrees. Compared with existing CBF-based SCCs, our method can significantly reduce differential order and computational burden. Specifically, we first propose a novel concept of embedded control barrier function (EMB-CBF), which can reduce an arbitrary high-relative-degree safety constraint to relative degree one, and ensure safety of high-relative-degree systems. Further, EMB-CBF-SCC divides the original system into a top-level and a bottom-level subsystem. Then, it embeds between the two subsystems a quadratic program based on EMB-CBF, and introduces command filters to smooth virtual control inputs and obtain differential signals. Coordination performance of safety and stability is analyzed, considering the impact of filter errors. Finally, we present two real safety-critical robotic application scenarios with different safety constraint settings, namely, multiple state constraints for a single-link manipulator numerical model and dynamic obstacle avoidance constraints for a self-developed micro mobile robot experimental platform, respectively. The effectiveness of the proposed framework is demonstrated in both scenarios.

Dual-gantry surface mount optimization effectively improves the productivity of printed circuit board assembly (PCBA), but also brings new challenges. Optimizing workload allocation to balance the front and rear gantry placement completion time is a significant challenge for improving PCBA productivity. This study proposes a finite potential game heuristic algorithm (FPGHA) to solve the workload allocation problem. The algorithm generates game agents by analyzing the feeding characteristics of the dual-gantry placement machine and using an improved bisection K-means clustering method. Agent utility is calculated based on metrics affecting productivity of the pick-and-place process, including the number of simultaneous pickups, nozzle changes, cycles, and mounting points. Nash equilibrium of FPGHA is obtained by a best-response dynamics and heuristic algorithm. Then, the effectiveness of FPGHA in solving the workload allocation problem is first demonstrated in simulated experiments with different nozzle and feeder configurations. Finally, FPGHA is compared with the hierarchical restricted balance algorithm, adaptive clustering algorithm, and the popular industrial optimizer software in actual placement experiments using real-world industrial printed circuit boards. The effectiveness and accuracy of FPGHA are verified by analyzing the correlation between three variables: The FPGHA estimated value, the actual assembly value, and the PCB assembly time.

Deep feature fusion plays a significant role in the strong learning ability of convolutional neural networks (CNNs) for computer vision tasks. Recently, works continually demonstrate the advantages of efficient aggregation strategy and some of them refer to multiscale representations. In this article, we describe a novel network architecture for high-level computer vision tasks where densely connected feature fusion provides multiscale representations for the residual network. We term our method the ResDNet which is a simple and efficient backbone made up of sequential ResDNet modules containing the variants of dense blocks named sliding dense blocks (SDBs). Compared with DenseNet, ResDNet enhances the feature fusion and reduces the redundancy by shallower densely connected architectures. Experimental results on three classification benchmarks including CIFAR-10, CIFAR-100, and ImageNet demonstrate the effectiveness of ResDNet. ResDNet always outperforms DenseNet using much less computation on CIFAR-100. On ImageNet, ResDNet-B-129 achieves 1.94% and 0.89% top-1 accuracy improvement over ResNet-50 and DenseNet-201 with similar complexity. Besides, ResDNet with more than 1000 layers achieves remarkable accuracy on CIFAR compared with other state-of-the-art results. Based on MMdetection implementation of RetinaNet, ResDNet-B-129 improves mAP from 36.3 to 39.5 compared with ResNet-50 on COCO dataset.

In this article, the twistors modeling method and high-order fully actuated (HOFA) system approach are utilized to investigate the attitude and orbit integrated control problem of spacecraft. Initially, a first-order state-space model is formulated to represent the attitude and orbit dynamics of spacecraft. This model is subsequently transformed into a HOFA system model. Based on this transformed model, a control strategy is meticulously designed. With the developed control strategy, a linear closed-loop system is obtained, whose poles can be arbitrarily configured. The effectiveness of the proposed control strategy is ultimately verified through detailed simulation results.

Accurate and robust geo-localization in all-weather conditions is essential for enabling autonomous vehicles and delivery robots to offer uninterrupted mobility services in the real world. In this paper, we propose the first camera and radar fusion based geo-localisation method that is robust to all-weather conditions. The core of the proposed method is to leverage the rich semantics information in images and sensing consistency in radars across all-weather. Our proposed method surpasses the state of the art camera-based and LiDAR-camera based methods in inclement weather conditions, shown by extensive comparative experiments. Notably, our approach requires only an open accessible map, eliminating the need for high-definition maps and offering a cost-effective solution for geo-localizing or globally localizing autonomous vehicles in any weather condition. Our code and trained model will be released publicly.

Recent problems in robotics can sometimes only be tackled using machine learning technologies, particularly those that utilize deep learning (DL) with transfer learning. Transfer learning takes advantage of pretrained models, which are later fine-tuned using smaller task-specific datasets. The fine-tuned models must be robust against changes in environmental factors such as illumination since, often, there is no guarantee for them to be constant. Although synthetic data for pretraining has been shown to enhance DL model generalization, there is limited research on its application for fine-tuning. One limiting factor is that the generation and annotation of synthetic datasets can be cumbersome and impractical for the purpose of fine-tuning. To address this issue, we propose two methods for automatically generating annotated image datasets for object segmentation, one for real-world and another for synthetic images. We also introduce a novel domain adaptation approach called filling the reality gap (FTRG), which can blend elements from real-world and synthetic scenes in a single image to achieve domain adaptation. We demonstrate through experimentation on a representative robot application that FTRG outperforms other domain adaptation techniques, such as domain randomization or photorealistic synthetic images, in creating robust models. Furthermore, we evaluate the benefits of using synthetic data for fine-tuning in transfer learning and continual learning with experience replay using our proposed methods and FTRG. Our findings indicate that fine-tuning with synthetic data can produce superior results compared to solely using real-world data.

The optimization of printed circuit board assembly (PCBA) for a beam head placement machine is a multivariable and multiconstraint combinatorial problem. Current techniques falter in solving a variety of PCBA problems since heuristic algorithms lack theoretical guarantees of optimality, and mathematical modeling methods have high computational complexity for the whole problem. This article proposes a novel two-phase optimization for PCBA, integrating the advantages of mathematical modeling with heuristic algorithms. We divide the problem into the head task assignment and the placement route schedule. For the former, an effective integer linear programming model with component partition is proposed, encompassing key efficiency-influencing factors. A recursive heuristic-based initial solution speeds up the solving convergence, while the reduction strategies enhance model solvability. For the placement route schedule, a tailored greedy algorithm yields high-quality solutions, leveraging the results of the model, and an aggregated route relink heuristic does further optimization. In addition, we propose a selection criterion for the solution pool of the model to pre-evaluate the placement movement, which builds the connection between the two phases. Finally, we validate the performance of the two-phase optimization, which provides an average efficiency improvement of 8.66%–21.83% compared to other mainstream research.

Surface mount technology is essential to the development of the electronic manufacturing industry. This article studies optimizing the surface mount process for the beam-head placement machine. A mixed-integer programming (MIP) model is proposed for this problem, which is decomposed into three interconnected hierarchical parts: feeder allocation; component assignment; and pick-and-place (PAP) sequence problems. This article proposes an efficient hierarchical framework with three elaborately designed heuristics to solve the above problem. The design of the scan-based algorithms optimizes the subobjectives of feeder allocation and component assignment. First, the allocation heuristic arranges the feeders into slots as a prerequisite for other problems. Then, the component assignment heuristic determines the component type for each head with a variety of criteria and long short-term objectives. Finally, the PAP sequence problem is solved using a modified beam search algorithm. The proposed algorithm offers advantages in terms of effectiveness, efficiency, and extension, which can satisfy various customization demands. Experiments are conducted on our self-designed placement machine using industrial and randomly generated data. Computational experiments show that the scan-based heuristic algorithm obtains near-optimal solutions with a gap of 9.93% averagely compared with the proposed MIP model and provides efficiency improvement over the mainstream studies.

In this article, a velocity-free adaptive controller is proposed for the tracking control of servo mechanisms with friction compensation. A continuously differentiable friction model is employed to compensate for the dominant friction nonlinearity of servo mechanisms. Besides, a projection-type adaptive law is applied to handle parameter uncertainties in the system model. Since only the output position signal is directly measurable, an adaptive extended state observer (AESO) is constructed to estimate the indeterminate velocity state, which can also provide an estimation of unmodeled dynamics. Moreover, the dynamic gain switching of AESO can effectively suppress the peaking phenomenon at the motion beginning. Specifically, the parameter adaptation law utilizes the desired velocity state instead of the estimated value, avoiding the coupling problem between parameter and state estimation. The proposed control strategy theoretically demonstrates the transient performance and boundedness of the error in output tracking. Asymptotic stabilization of the system can also be implemented when only parameter uncertainty exists. Comparative experiments are conducted on a linear motor platform to demonstrate the effectiveness of the proposed control scheme.

Camera and LiDAR are indispensable perception units in autonomous driving, providing complementary environmental information for 3D semantic segmentation. It is the key point that fuses the information of two modalities to accurate and robust semantic segmentation. However, three major factors will restrict the performance of fusion-based methods, i.e., the reliability of image features, the contribution of different image features, and the trade-off between results of image and point cloud. This paper proposes a novel multi-phase fusion network for 3D semantic segmentation. For the first factor, this paper takes the lead in regarding the problem that image features may be wrong due to the lack of dense annotations in the common datasets as a weak supervision problem and introduces the weakly supervised loss. Second, the proposed attention based feature fusion module can filter and reweight the image features effectively. Third, the results of the two modalities are further fused by self-confidence based late fusion module at pixel-level to complement their advantages. The proposed scheme has been evaluated on nuScenes and SemanticKITTI benchmarks, and the results show the competitiveness with state-of-the-art methods. The ablation studies demonstrate the superiority of the method in sparse classes segmentation. In addition, the robustness is also evaluated, and the results of the proposed method can keep relatively accurate even when faults in one of the sensors.

Compared with traditional rigid objects' dynamic throwing and catching by the robot, the in-flight trajectory of nonrigid objects (incredibly variable centroid objects) throwing is more challenging to predict and track. This article proposes a variable centroid trajectory tracking network (VCTTN) with the fusion of vision and force information by introducing force data of throw processing to the vision neural network. The VCTTN-based model-free robot control system is developed to perform highly precise prediction and tracking with a part of the in-flight vision. The flight trajectories dataset of variable centroid objects generated by the robot arm is collected to train VCTTN. The experimental results show that trajectory prediction and tracking with the vision-force VCTTN is superior to the ones with the traditional vision perception and has an excellent tracking performance.

Advanced robotics and autonomous vehicles rely on filtering and sensor fusion techniques to a large extent. These mobile applications need to handle the computations onboard at high rates while the computing capacities are limited. Therefore, any improvement that lowers the CPU time of the filtering leads to more accurate control or longer battery operation. This article introduces a generic computational relaxation for the unscented transformation (UT) that is the key operation of the Unscented Kalman filter-based applications. The central idea behind the relaxation is to pull out the linear part of the filtering model and avoid the calculations for the kernel of the nonlinear part. The practical merit of the proposed relaxation is demonstrated through a simultaneous localization and mapping (SLAM) implementation that underpins the superior performance of the algorithm in the practically relevant cases, where the nonlinear dependencies influence only an affine subspace of the image space. The numerical examples show that the computational demand can be mitigated below 50% without decreasing the accuracy of the approximation. The method described in this article is implemented and published as an open-source C++ library RelaxedUnscentedTransformation on GitHub.

In this article, a hybrid visual-ranging servoing method is proposed to realize high-precision positioning tasks with a 6-degree of freedom (DOF) manipulator. This method utilizes the image and measurement features directly in the control loop. Without the need of complex image feature design and attitude estimation, this method realizes the 6-DOF control of a robot. A vital challenge in traditional vision-based systems is avoiding local minima and singularity problems. To tackle this issue, a full-rank interaction matrix hybrid visual servo (FRHVS) design criterion is proposed, which guarantees that the hybrid interaction matrix and its pseudoinverse matrix are both full rank. Moreover, the interaction matrix for these hybrid strategies, which combines image features with other sensors features, is derived in an analytical form. Experiments on a 6-DOF manipulator show that the proposed method is effective and has global asymptotic stability and high precision.

In this study, a data-augmentation method is proposed to narrow the significant difference between the distribution of training and test sets when small sample sizes are concerned. Two major obstacles exist in the process of defect detection on sanitary ceramics. The first results from the high cost of sample collection, namely, the difficulty in obtaining a large number of training images required by deep-learning algorithms, which limits the application of existing algorithms in sanitary-ceramic defect detection. Second, due to the limitation of production processes, the collected defect images are often marked, thereby resulting in great differences in distribution compared with the images of test sets, which further affects the performance of detect-detection algorithms. The lack of training data and the differences in distribution between training and test sets lead to the fact that existing deep learning-based algorithms cannot be used directly in the defect detection of sanitary ceramics. The method proposed in this study, which is based on a generative adversarial network and the Gaussian mixture model, can effectively increase the number of training samples and reduce distribution differences between training and test sets, and the features of the generated images can be controlled to a certain extent. By applying this method, the accuracy is improved from approximately 75% to nearly 90% in almost all experiments on different classification networks.

Dual-drive gantry stage has been widely applied to various industrial manufacturing fields with its unique structural advantages, and the synchronous control accuracy of the platform is crucial to the performance of the whole motion system. Therefore, an adaptive robust synchronous control scheme based on an improved master-slave structure is proposed, which is not only simple in structure but also easy to implement in engineering. The error dynamics model established in this article makes up for the lag of response of traditional master-slave control and improves the stability of closed-loop system. Online parameter adaptive algorithms deal with parameter uncertainties in the system, while robust control deals with unmodeled dynamics and external disturbances. In addition, nonlinear cogging force compensation is applied to the gantry biaxial system to further improve the control accuracy of tracking and synchronization. Finally, a dual-drive gantry stage system with good tracking and synchronization performance is obtained. The effectiveness and superiority of the proposed control strategy are verified by the comparison of several groups of experiments.

The capability to obtain detailed motility information of model organisms is fundamental to reveal their functional and social behavior characteristics. Zebrafish is a powerful vertebrate model organism. Despite recent success in the automatic quantification of adult zebrafish movement, it remains a laborious task for group zebrafish larval tracking due to their similar appearance, frequent occlusions, and highly discontinuous kinematics. This article presents DanioSense (DS), an automatic tracker for group larval zebrafish, to overcome these tracking challenges. The integration of a light convolutional neural network and a centerline extraction algorithm enables the tracker to localize individuals even in occlusion cases where objects’ identities are prone to switch. With reliable detections, an adaptive Kalman filter is designed to optimally estimate locomotive parameters, which is also used for object reidentification accomplished by a two-stage data association protocol. Experimental results demonstrated a tracking accuracy of over 97%, median errors of$102~{\mathrm{\mu m}}$, and 8.8° for the position and orientation measurement, and a processing speed of over 30 frames/s with a normal computer configuration. DS provides detailed quantitative data for a large-scale larvae group in nearly real time, highly boosting the efficiency of characterizing individual phenotypes and analyzing social interactions.Note to Practitioners—This article aimed to tackle the problem of automated tracking groups of zebrafish larvae, an ideal vertebrate model organism for large-scale chemical and genetic screens. The task of group tracking is to record each individual’s movement and calculate their position, velocity, direction, and other parameters for further analysis, where the correct identity of each individual must be maintained. Existing algorithms either switch larvae’ identities easily or are unable to achieve online tracking due to the limitations of their methods to address individuals’ intersections. DanioSense (DS) adopts a convolutional neural network to identify larval heads whenever they intersect and uses an adaptive Kalman filter to calculate the movement parameters optimally. Besides, a range of visualization options is designed to bring insight into underlying patterns through massive amounts of data. Theoretically, this algorithm’s approach to solving intersections and calculating movement statistics can also apply to other fish-like animals. Its visualization options are applicable to other tracking systems. The key advantage of Daniosense over existing trackers is the capability to track each larva within a group and output detailed quantitative data in nearly real time. The tracking performance of DS is based on the quality of image segmentation and the success rate of classifying samples. Many state-of-the-art image segmentation and classification neural networks can be adopted to extend this system’s applications to more complex environments but at a higher computation and time cost, which is a tradeoff between efficiency and capability. Some applications require a higher video sampling rate, so the system’s processing speed needs to be further improved with better hardware and software framework optimization. The next steps include improving the processing efficiency, providing more tracking modules and visualization options, and extending its application fields.

This article proposes a hierarchical multiobjective heuristic (HMOH) to optimize printed-circuit board assembly (PCBA) in a single beam-head surface mounter. The beam-head surface mounter is the core facility in a high-mix and low-volume PCBA line. However, as a large-scale, complex, and multiobjective combinatorial optimization problem, the PCBA optimization of the beam-head surface mounter is still a challenge. This article provides a framework for optimizing all the interrelated objectives, which has not been achieved in the existing studies. A novel decomposition strategy is applied. This helps to closely model the real-world problem as the head task assignment problem (HTAP) and the pickup-and-place sequencing problem (PAPSP). These two models consider all the factors affecting the assembly time, including the number of pickup-and-place (PAP) cycles, nozzle changes, simultaneous pickups, and the PAP distances. Specifically, HTAP consists of the nozzle assignment and component allocation, while PAPSP comprises place allocation, feeder set assignment, and place sequencing problems. Adhering strictly to the lexicographic method, the HMOH solves these subproblems in a descending order of importance of their involved objectives. Exploiting the expert knowledge, each subproblem is solved by an elaborately designed heuristic. Finally, the proposed HMOH realizes the complete and optimal PCBA decision making in real time. Using industrial PCB datasets, the superiority of HMOH is elucidated through comparison with the built-in optimizer of the widely used Samsung SM482.

In this article, an adaptive sensor fault accommodation scheme is proposed for uncertain vehicle active suspensions via output-feedback control where vehicle body displacement is the only measurable output signal corrupted by sensor bias. An adaptive observer with variable gains is constructed to obtain state estimates whose design procedure involves parameter adaption of the uncertain system parameters and sensor bias, and an output-feedback controller is designed to attenuate the vehicle body displacement based on the partial measurement information, estimates of the states, and unknown parameters. Compensation for measurement error is made both in the design process of the adaptive observer and output-feedback controller in order to weaken the influence brought about by sensor bias fault. In order to guarantee system stability, the variable observer gains are determined in real time using a switching strategy where their values can be modified in finite times by monitoring the state estimates generated by the observer itself. It is proved that the vehicle body displacement will converge to a small neighborhood around zero, and all the signals of the closed-loop system are ensured to be bounded through selecting suitable control parameters. Simulation is carried out to show the effectiveness of the proposed method and results indicate that better stabilization of the suspension vertical motion can be achieved through adaptive compensation for sensor bias.

This article studies the disturbance observer-based adaptive fuzzy finite-time control issue of strict-feedback nonlinear systems. Specifically, to meet practical application requirement, the finite-time prescribed performance is considered, which can guarantee the tracking error enters into the prescribed bounded set in a known time. A disturbance observer is proposed to estimate the external disturbance. It is proved that the closed-loop system is semi-globally practically finite-time stable. Finally, simulation studies for a one-link manipulator are shown to verify the effectiveness of the proposed approach.

Optimizing all the objectives of the printed circuit board assembly (PCBA) optimization in a multifunctional placer remains a formidable challenge till now. This article converts the original PCBA optimization problem to a newly defined component allocation problem, which decides the component-type handled by each head per pickup-and-place (PAP) cycle. The component allocation problem is a quadratic 3-D assignment problem (Q3AP) and effectively combines the optimization of all the main objectives. It is possible that one head stays idle, so the assigning 2-D locations are uncertain. We propose the cell division genetic algorithm (CDGA) to solve such a complex Q3AP. The CDGA allocates a component cell as the basic unit. Each of the first-generation component cells contains the mounting points of the same type. A cell chromosome decoding heuristic is designed to determine the next assigning head. By doing so, the problem dimension is reduced, so the conventional GA can be used for searching the optimal component allocation formed by the current-generation cells. When a better allocation can no longer be found by allocating the current cells, the cell division operation is performed to divide each cell into two new cells. The new cells are used in the next round of GA searching, which further optimizes the allocation from two perspectives: better balancing the minimization of nozzle changes and PAP cycles, more flexibly maximizing the simultaneous pickups with the uncertain locations. The CDGA works continuously until the current cells cannot bring any improvement. In simulations and experiments using the industrial samples, the proposed algorithm significantly reduces the PCBA time compared to two recent studies and the built-in optimizer of the widely used multifunctional placer, Hanwha SM482 PLUS, which demonstrates its effectiveness and superiority.

Online multi-object tracking (MOT) is an active research topic in the domain of computer vision. Although many previously proposed algorithms have exhibited decent results, the issue of tracklet inactivation has not been sufficiently studied. Simple strategies such as using a fixed threshold on classification scores are adopted, yielding undesirable tracking mistakes and limiting the overall performance. In this paper, a conditional random field (CRF) based framework is put forward to tackle the tracklet inactivation issue in online MOT problems. A discrete CRF which exploits the intra-frame relationship between tracking hypotheses is developed to improve the robustness of tracklet inactivation. Separate sets of feature functions are designed for the unary and binary terms in the CRF, which take into account various tracking challenges in practical scenarios. To handle the problem of varying CRF nodes in the MOT context, two strategies named as hypothesis filtering and dummy nodes are employed. In the proposed framework, the inference stage is conducted by using the loopy belief propagation algorithm, and the CRF parameters are determined by utilizing the maximum likelihood estimation method followed by slight manual adjustment. Experimental results show that the tracker combined with the CRF-based framework outperforms the baseline on the MOT16 and MOT17 benchmarks. The extensibility of the proposed framework is further validated by an extensive experiment.

Predators in nature grip their prey in different ways, which give innovational ideas of gripping approaches in industrial applications. Octopus performs flexible gripping with the help of vacuum grippers, suction cups, which inspired a new type of microgripper for biological sample micromanipulation. The proposed gripper consists of a glass pipette and a pump driven by a step-motor. The step-motor is controlled with adaptive robust control to adjust the gripping pressure applied on the biological sample. A dynamic model is developed for the biological sample aiming for better deformation control performance. A visual detection algorithm is developed for data processing to identify the parameters in the dynamic model and the detection result of visual algorithm is also used as feedback of adaptive robust control, which diminishes the negative influence of parameter and model uncertainties. Zebrafish larva was used as the testing sample for experiment and the corresponding parameters were identified experimentally. The experimental results correlated well with the model predicted deformation curve and visual detection algorithm provided promising accuracy, which is less than [Formula: see text]. Adaptive robust control provides fast and accuracy response in point-to-point deformation testing, and the average responding time is less than 30 s and the average error is no larger than 1 pixel.

The main function of active suspension systems is to suppress vibration resulted from the road roughness and improve passengers’ ride comfort, and some time-domain constraints such as mechanical limitation should be taken into consideration when designing active controllers. In this article, a constrained adaptive backstepping control scheme is proposed for the quarter-car suspension with parameter uncertainties, in which the primary control objective is to stabilize the vertical motion of the vehicle body and the suspension mechanical structure constraint can be satisfied in the meanwhile, and energy analysis has been made to demonstrate the potential of energy regeneration and reducing energy consumption for the designed active suspension system. In terms of dealing with the hard constraint, a specific nonlinear filter is employed in order to integrate the main control objective and time-domain constraint into a single controlled variable. In addition, a barrier Lyapunov function is selected to make the defined controlled variable converge to zero and stay in the allowable limit, which means that the vertical motion of the vehicle body can be stabilized and the suspension deflection restriction will not be transgressed. Experiments are carried out on the active suspension test plant to verify the effectiveness of the designed control scheme. Since the high energy consumption of active actuators is one of the drawbacks to be overcome, the energy flow of the dc motors is analyzed in the latter part of this article so as to provide a theoretical basis for energy harvesting design in further study. Finally, the energy consumption of the active suspension plant in experiments is figured out and its potential of energy recovery is demonstrated in detail.

This article presents an automated strategy to touch the injection site on zebrafish larva skin with the injection pipette tip accurately in the presence of water-depth variation, which is a crucial problem to automate zebrafish larva microinjection. The presented method consists of two parts: adaptive coordinate transformation and curve evolution for edge detection. In the first part, the impact of refraction is taken into consideration. An adaptive calibration method is developed, which enables the coordinate transformation matrix to adapt to the changing water depth. In the second part, the abovementioned calibration result is used to keep the injection pipette tip descending along the desired route. A curve-evolution-based edge detection algorithm is introduced to detect the deformation of larva skin caused by contact with the injection pipette tip. Experimental results demonstrate that high accuracy and success rates are achieved. The effect of uncertainties caused by water-depth variation and the skill requirement in manual manipulation are eliminated. The proposed contact detection strategy can be extended to microinjection for other organisms.Note to Practitioners—As a typical multicellular model organism, the zebrafish has been increasingly used in biological research. For studying drug toxicity and disease models, exogenous substances need to be injected into zebrafish larvae. However, for both manual and automated injection, a fatal problem is that the camera on the microscope only provides 2-D positional information. It is laborious to align the pipette tip with the injection site along the$z$-axis. Moreover, due to the characteristic of stereomicroscopes, the impact of refraction at the water surface cannot be ignored. In order to address these issues, in this article, we present an adaptive calibration method and an edge detection algorithm for zebrafish larva heart injection to avoid contact failure in practical implementations.

Convolutional neural networks (CNN) have been proved to be an effective method in the field of artificial intelligence (AI), and large-scale deploying CNN to embedded devices, no doubt, will greatly promote the development and application of AI into the practical industry. However, mainly due to the space-time complexity of CNN, computing power, memory bandwidth and flexibility are performance bottlenecks. In this paper, a framework containing model compression and hardware acceleration is proposed to solve the above problems. This framework consists of a mixed pruning method, data storage optimization for efficient memory utilization and an accelerator for mapping CNN on field programmable gate array (FPGA). The mixed pruning method is used to compress the model, and data bit-width is reduced to 8-bit by data quantization. Accelerator based on FPGA makes it flexible, configurable and efficient for CNN implementation. The model compression is evaluated on NVIDIA RTX2080Ti, and the results illustrate that the VGG16 is compressed by 30× and the fully convolutional network (FCN) is compressed by 11× within 1% accuracy loss. The compressed model is deployed and accelerated on ZCU102, which is up to 1.7× and 24.5× better in energy efficiency compared with RTX2080Ti and Intel i7 7700.

This article studies the asynchronous sampled-data filtering design problem for Itô stochastic nonlinear systems via Takagi-Sugeno fuzzy-affine models. The sample-and-hold behavior of the measurement output is described by an input delay method. Based on a novel piecewise quadratic Lyapunov-Krasovskii functional, some new results on the asynchronous sampled-data filtering design are proposed through a linearization procedure by using some convexification techniques. Simulation studies are given to illustrate the effectiveness of the proposed method.

This article investigates the adaptive fuzzy fault-tolerant control problem for a class of strict-feedback stochastic nonlinear systems with quantized input signal. A hysteretic quantizer is utilized to avoid chattering caused by quantized input signals. The fuzzy-logic systems are utilized to approximate the unknown nonlinear functions and also to construct the fuzzy state observer, which is used to estimate the immeasurable state vector. The actuator faults considered in this article are loss of effectiveness and lock-in-place faults. By using the Lyapunov stability theory, the closed-loop stochastic nonlinear system is guaranteed to be stable in probability, and all the signals of the closed-loop system are bounded in probability in the presence of quantized input and actuator faults. Finally, a simulation example is given to verify the validity of the proposed control strategy.

This article deals with the zonotopic fault detection for fuzzy systems with event-triggered mechanism. An$\ell _1/h_{\infty }$fault detector design method is deduced with considering the asynchronous premise variables caused by event-triggered transmission, such that the residual signal is sensitive to system failure while robust to amplitude-bounded disturbance, and noise. Then, a novel zonotopic residual evaluation process is constructed by converting the asynchronization of premise variables into system uncertainties. Different from most published works on event-triggered fault detection where constant thresholds are utilized for residual evaluation, zonotopic thresholds for residual signal are designed with taking the influences of disturbance, noise, asynchronous premise variables, and event-based transmission into consideration. Finally, a numerical example is utilized to verify the effective performance of the developed strategy.

This article investigates the event-triggered adaptive tracking control for a class of pure-feedback stochastic nonlinear systems with full state constraints and input saturation. The saturated input is expressed as a smooth nonlinear function with bounded disturbance. The pure-feedback structure is transformed into strict-feedback case via mean value theorem, and a novel event-triggered adaptive fuzzy tracking control scheme with relative threshold is then proposed. The barrier Lyapunov function is introduced to analyze the system stability, and the state constraints are, thus, guaranteed. It is proved that the closed-loop stochastic nonlinear system is semiglobally uniformly ultimately bounded in probability, and the output tracking error converges to a small neighborhood of zero. Finally, the effectiveness of the proposed method is verified via simulation studies.

The visual inspection algorithms are the core of automatic optical inspection system on surface mounting machines. This article is concerned with the development of precise positioning algorithms for circular mark points and transistor (TR) components on surface mounting devices. To handle nonuniform illumination or occlusion of other components, a polar coordinate transform and smoothness selection based circular mark point location method is proposed. The TR components are fundamental chips in electronic products and have various package types. The illumination changes, background disturbance, and the diversity of package types have imposed great challenges on the development of the uniform algorithm for detection and location of TR components. To deal with these issues, the 1-D integral image based TR component detection and location algorithm is proposed and the coordinates and orientation of the component are calculated simultaneously. The efficiency of the proposed methods is tested on real images and compared with classical Hough transform method, commercial algorithms on SMT482 device, and two methods of Halcon software.

Modern autonomous vehicles are required to perform various visual perception tasks for scene construction and motion decision. The multiobject tracking and instance segmentation (MOTS) are the main tasks since they directly influence the steering and braking of the car. Implementing both tasks using a multitask learning neural network presents significant challenges in performance and complexity. Current work on MOTS devotes to improve the precision of the network with a two-stage tracking by detection model, which is difficult to satisfy the real-time requirement of autonomous vehicles. In this article, a real-time multitask network named YolTrack based on one-stage instance segmentation model is proposed to perform the MOTS task, achieving an inference speed of 29.5 frames per second (fps) with slight accuracy and precision drop. The YolTrack uses ShuffleNet V2 with feature pyramid network (FPN) as a backbone, from which two decoders are extended to generate instance segments and embedding vectors. Segmentation masks are used to improve the tracking performance by performing logic AND operation with feature maps, proving that foreground segmentation plays an important role in object tracking. The different scales of multiple tasks are balanced by the optimized geometric mean loss during the training phase. Experimental results on the KITTI MOTS data set show that YolTrack outperforms other state-of-the-art MOTS architectures in real-time aspect and is appropriate for deployment in autonomous vehicles.

This article investigates the adaptive learning control problem for a class of nonlinear autonomous underwater vehicles (AUVs) with unknown uncertainties. The unknown nonlinear functions in the AUVs are approximated by radial basis function neural networks (RBFNNs), in which the weight updating laws are designed via gradient descent algorithm. The proposed gradient descent-based control scheme guarantees the semiglobal uniform ultimate boundedness (SUUB) of the system and the fast convergence of the weight updating laws. In order to reduce the computational burden during the backstepping control design process, the command-filter-based design technique is incorporated into the adaptive learning control strategy. Finally, simulation studies are given to demonstrate the effectiveness of the proposed method.

To reduce the labor effort in hazardous environments like spray painting, an automatic car part spray painting machine has been set up. This article presents an intelligent location procedure for this machine. The location procedure assumes that the car part is placed with a tiny arbitrary pose, furthermore some kinds of parts under inspection undergoes through the deformation and thus it contains a nonrigid model. To tackle this problem, a workpiece positioning system works though multicamera is established first. Subsequently, a novel modified iterative closest point (ICP) algorithm is proposed which registers the nonrigid shape to the undeformed source shape in the training library. The modified ICP combines the ideas from traditional ICP and deformation estimation from bounded biharmonic weights. It solves a nonlinear cost function by using Levenberg–Marquardt algorithm. As a result, it estimates the transformation of the target point cloud with regards to the source point cloud. Additionally, it improves the accuracy of traditional ICP and increases its scope to nonrigid shapes. By employing these results in our location procedure, it can estimate the 6-DOF pose of the car part to be painted in addition to that it also estimates the deformation compared to the source cloud in the training library. This information is subsequently used to modify the guidance trajectory of the spray gun. In this article, a test case of front bumper is given, as it is commonly made of soft plastic materials and undergoes deformation under the influence of the external force. All the stated results support the efficacy of our algorithm.

This article investigates the neural network-based finite-time control issue for a class of nonstrict feedback nonlinear systems, which contain unknown smooth functions, input saturation, and error constraint. Radial basis function neural networks and an auxiliary control signal are adopted to identify unknown smooth functions and deal with input saturation, respectively. The issue of error constraint is solved by combining the performance function and error transformation. Based on the backstepping recursive technique, a neural network-based finite-time control scheme is developed. The developed control scheme can ensure that the closed-loop system is semi-globally practically finite-time stable. Finally, the validity of theoretical results is verified via simulation studies.

In this paper, we propose a modified CenterNet to complete the defect detection of Sanitary Ceramics. Generally, visual quality inspection is rather important during the productive process of Sanitary Ceramics and it is nearly impossible to inspect the massive images by hand. Consequently, it is necessary to devise an accurate and real-time system to process the data. However, due to the varied shapes and backgrounds of ceramics, conventional computer vision methods are usually not robust to all those variables. Detectors based on Deep Learning start to be adopted in recent years, but most algorithms require some carefully devised anchor boxes and post-processing methods, which also bring more computational costs. Here we decide to take advantage of the anchor-free model, CenterNet. We change the main structure to fit our own data and introduce an extra branch with shallow layers to strengthen the feature representation. The results have shown the great power of this model. Without even any post-processing methods, our model achieves a result of 96.16 AP on the established dataset.

In this paper, we propose four offline data augmentation methods to improve the performance of convolutional neural network(CNN) on defect detection of sanitary ceramics. In recent years, based on big data, deep learning has begun to become a popular way for sanitary ceramics defect detection. Comparing with traditional vision inspection system, deep learning method is more robust and convenient without manual design of feature extraction. As a data-driven detection way, data plays a vital roll, however, sometimes we could not obtain a high-quality and large dataset. Consequently, we consider data augmentation to improve the quality of original dataset. Here, we use image generation, image mosaic, image fusion and image rotation mosaic. According to the experiment results, with these methods, the enhanced datasets perform well compared with the original one.

Aerial manipulator is a kind of robot with broad application prospects, which is suitable for high altitude operation and other dangerous application scenarios. This paper presents a trajectory tracking control algorithm for the aerial parallel manipulator based on Stewart platform. By modeling the overall dynamics of the aerial parallel manipulator, the expression of the influence of Stewart platform is given, and it is proved that this type of influence can be combined with the unmodeled error and external disturbance into the comprehensive disturbance of the flight platform. The flight platform trajectory tracking control is carried out by using the backstepping method, and the nonlinear disturbance observer is used for disturbance estimation and compensation in the control output. Numerical experiments show that the proposed control method can realize the trajectory tracking control of the flight platform of the aerial parallel manipulator.

This article studies the issue of adaptive neural network (NN) control for strict-feedback multi-input and multioutput (MIMO) nonlinear systems with full-state constraints and actuator hysteresis. Radial basis function NNs (RBFNNs) are introduced to approximate unknown nonlinear functions. The command filter is adopted to solve the issue of "explosion of complexity." By applying a one-to-one nonlinear mapping, the strict-feedback system with full-state constraints is converted into a new pure-feedback system without state constraints, and a novel NN control method is proposed. The stability of the closed-loop system is proved via the Lyapunov stability theory, and the tracking errors converge to small residual sets. The simulation results are given to confirm the validity of the proposed method.

The papers in this special section examine smart process manufacturing that is driven by artificial intelligence (AI). As a fundamental industry, process industry mainly involves elementary raw material industries, such as petroleum, chemical, steel, nonferrous metal, and building. However, there are a series of problems existing in process industry such as inaccurate perception of industrial data, low production efficiency, high materials consumption and limitations in safety and environment protection. In order to solve these restriction problems, we must pursue the goal of efficient, green, and smart processes in manufacturing and marketing. On the other hand, artificial intelligence (AI) has powerful strengths in perception, knowledge representation, learning, reasoning and planning, so that it has been successfully utilized in diverse areas, such as autonomous vehicles and so on. It is promising to have deep and tight integration between artificial intelligence and process industry, to achieve “smart process industry”.

This article deals with the template matching problem, and a weighted smallest deformation similarity measure, which is robust to occlusions, background outliers, and complex deformations. The appearance-based nearest neighbor (NN) matching of points is constructed and the smallest location distance between each point in the template and its matching points is employed to penalize the deformation explicitly. Then, the weights are added to points in the template relied on their likelihood of belonging to the background through NN matching with the points around the target window. Experiments show that the proposed method improves the state-of-the-art performance on real-world scenario benchmarks and can be applied in rough positioning of surface mount technology components.

The problem of H∞filtering to estimate the unmeasurable states is investigated in this paper and the most general multirate measurements condition is taken into account. The main result of this paper is to give a method to design a filter, which can guarantee the stability of the resultant filtering error system with H∞performance. For a given system with multirate measurements, this paper first provides a method to convert this multirate design problem into an equivalent single-rate design problem. Then, it can be proved that the design approach of a required filter based on a linear matrix inequality is a sufficient and necessary condition. Lastly, two examples are utilized to demonstrate that this design approach is effective and applicable to estimate the unmeasurable states for the given system with multirate measurements.

Multiplicative noise removal problems have attracted much attention in recent years. Unlike additive noise removal problems, multiplicative noise destroys almost all information of the original image, especially for texture images. In this paper, a fractional-order nonlinear diffusion model is proposed to denoise the texture images corrupted by multiplicative noise. In the model, a gray level indicator is introduced to remove multiplicative noise and preserve structure details for texture images. By virtue of the discrete Fourier transform, the model is solved by an iterative scheme in the frequency domain. Then an algorithm in the spatial domain is developed based on the definition of the Grünwald--Letnikov fractional-order derivative. Inspired by the discrepancy principle used for additive noise, we develop a new stopping criterion based on the mean and variance of the noise. Numerical examples are presented to demonstrate the effectiveness and efficiency of the proposed method. Experimental results show that the proposed model can handle multiplicative noise removal and texture preservation quite well.

This paper studies the problem of fuzzy adaptive event-triggered control for a class of pure-feedback nonlinear systems, which contain unknown smooth functions and unmeasured states. Fuzzy logic systems are adopted to approximate unknown smooth functions and a fuzzy state observer is designed to estimate unmeasured states. Via the event-triggered control technique, the control signal of the fixed threshold strategy is obtained. By converting the tracking error into a new virtual error variable, an observer-based fuzzy adaptive event-triggered prescribed performance control strategy is designed. The key advantage is that the proposed method does not require a priori knowledge of partial derivatives of system functions, i.e., it relaxes the restrictive condition that the partial derivatives of system functions need to be known for pure-feedback nonlinear systems. Simulation results confirm the efficiency of the proposed method.

The problem of adaptive fuzzy control is investigated for a class of nontriangular structural stochastic switched nonlinear systems with full state constraints in this paper. A remarkable feature of the nontriangular structural nonlinear system is the so-called algebraic loop problem in the existing backstepping-based analysis and design. Properties of fuzzy basis functions are utilized to circumvent this algebraic loop problem. Based on the Barrier Lyapunov function, an adaptive fuzzy stochastic switched control scheme is designed. It is proven that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded with full state constraints. The effectiveness of the proposed control scheme is verified via simulation studies.

In this work, the fuzzy observer-based control problem is investigated for a class of nonlinear coupled systems, which consists of a hyperbolic partial differential equation (PDE) containing nonlinearities and a nonlinear ordinary differential equation (ODE). The nonlinear coupled system is represented as a Takagi-Sugeno (T-S) fuzzy coupled hyperbolic PDE-ODE model. Based on the T-S fuzzy model, a novel Lyapunov functional approach is proposed to design a fuzzy observer based control strategy. More specifically, a fuzzy observer is presented to estimate the state variables of the fuzzy coupled PDE-ODE system with the measurements of the PDE, and the exponential convergence of the observer error is proved. Then, a fuzzy controller is given utilizing the estimated states as feedback variables, and it is proved that the evolution profiles of the PDE and the trajectory of the ODE in the closed-loop fuzzy system converge exponentially to the desired values, respectively. The sufficient existence conditions of the fuzzy observer based controller are formulated in terms of a set of space differential linear matrix inequalities (SDLMIs). A recursive algorithm based on the finite-difference approximation and the linear matrix inequality techniques are provided to solve the SDLMIs. Finally, the results are applied to case study of a predator-prey system, and the simulations are performed to illustrate the effectiveness of the proposed observer based control law.

Computer vision based detection approaches are widely employed to detect or calibrate different types of meters nowadays. However, traditional detection algorithms suffer drawbacks in accuracy and adaptability upon detecting various types of automobile dashboards. Plenty of parameters of these algorithms need to be tuned to suit certain types of dashboards. Besides, theses algorithms cannot automatically read the speed value, which requires manual setting operations. In this paper, a novel approach is presented to adaptively detect different types of automobile dashboards. The contour analysis based method is first implemented to extract the connected component of the pointer. A robust character segmentation classifier, which is designed by cascading histogram of oriented gradients (HOG)/support vector machine (SVM) binary classifier, character filter as well as HOG/multiclass SVM digit classifier, is then proposed to recognize digit characters on the dashboard. Simultaneously, tick marks are then extracted based on recognition results. Finally, Newton interpolation linear relationship is established to diagnose the potential responding errors of the pointer. The experimental results show that the pointer extraction method is robust to interferences caused by connected components of digits and also that the established character segmentation classifier has a more accurate detection result. Furthermore, compared with similar algorithms, it has a significant advantage in detecting a vast majority of different dashboards without manual tuning of the parameters.

The autonomous operation of industrial robots with minimal human supervision has always been in high demand. To prepare the autonomous operation of a car part spray painting robot, novel object detection, and pose estimation algorithms have been developed in this paper. The object detection part used principal components analysis (PCA) to reduce the dimension of three-dimensional (3-D) point cloud to 2-D binary image. Distance measure between the auto and cross correlation of the binary features was established to find out the similarity between them. Resultantly, the type of auto part was successfully obtained. Furthermore, iterative closest point (ICP) algorithm was used to estimate the pose difference of the auto part with respect to the camera reference frame, which was mounted on the robot. An issue with ICP's lack of robustness to local minimum was solved by the combination of ICP and genetic algorithm (GA). This allowed the optimization of pose error and addressed the problem of local minimum entrapment in ICP. For experimental validation: the proposed object recognition pipeline was implemented in both serial and parallel programming paradigms. The results were obtained for the acquired point clouds of side body car parts and compared with the major 3-D object detection systems in terms of computational cost. Pose estimation error was calculated with both ICP and the modified point set registration schemes, and it was shown to be decreasing in the case of later. All shown results supported the research claims.

In this paper, sampled-data adaptive robust control is proposed for a class of uncertain cascaded nonlinear system with states and inputs constraints. The systematic design procedure can be divided into two steps: i) design a sampled-data adaptive robust controller for the plant to not only stabilize the closed-loop system but also track the desired command although there are a variety of uncertainties and disturbances in the system; ii) design a reference governor for the control system to avoid the states and inputs violating their limits. Finally, the proposed method is employed in Motor-servo system to demonstrate the effectiveness.

This paper is concerned with the event-triggered fault detection filter (FDF) design for an unmanned surface vehicle (USV) under the network environment. A framework of fault detection is established for a USV subject to wave-induced disturbance and actuator failures, in which an FDF is utilized to construct a residual model and an integral-based event generator is introduced to save communication resources. Compared with the traditional instantaneous value based event-triggering scheme and periodic sampling, the proposed event-triggering mechanism can not only reduce bandwidth utilization of the network more significantly, but also get rid of the Zeno phenomenon fundamentally. The event-triggering scheme and the FDF are co-designed. Finally, the efficient performance of the proposed fault detection method based on integral-based event-triggering scheme is illustrated by simulation.

In network systems, a group of nodes may evolve into several subgroups and coordinate with each other in the same subgroup, i.e., reach cluster synchronization, to cope with the unanticipated situations. To this end, the leader-following practical cluster synchronization problem of networks of generic linear systems is studied in this paper. An event-based control algorithm that can largely reduce the amount of communication is first proposed over directed communication topologies. In the proposed algorithm, each node decides itself when to transmit its current state to its neighbors and how to update its controller according to the estimations of the states of it and its neighbors. Then, the Lyapunov method is utilized to perform the convergence analysis. It shows that the practical cluster synchronization can be ensured by choosing appropriate parameters no matter what kind of estimation for the state is applied. Furthermore, the Zeno behavior is also excluded for each node under some mild assumptions. Besides, three kinds of common estimations for the states including zero-order hold model, first-order approximate model, and high-order model-based estimations are, respectively, analyzed from the perspective of the exclusion of Zeno behavior. Finally, the validity of the proposed algorithm is demonstrated, the effects of the concerned parameters are simply presented, and the effects of the three estimations are also compared through several simulations.

This paper is concerned with the nonrigid point set registration problem and a probability-based registration algorithm with local connectivity preservation is proposed. A unified formulation for point set registration problem is introduced and the derived energy function is composed of three parts, distance measurement item, transformation constraint item, and correspondence constraint item. In order to preserve the local structure of point set, the definitions of -connected neighbors and connectivity matrix are given and the local connectivity constraint is constructed as a weighted least square error item. The point set registration problem is formulated in the expectation-maximization algorithm scheme and the optimal spatial transformation and correspondence matrix are estimated simultaneously. The effectiveness of the proposed method is verified by applying the method to synthetic point sets and real scenarios of hand shapes and surface-mount technology components.

Component pick-and-place technology has been widely used to improve production efficiency and reduce common defects. The vision-driven measurement system of a component pick-and-place machine requires an appropriate positioning algorithm with low computational complexity, high accuracy, and high generalizability. To satisfy these attributes is rather challenging. This paper focuses on the online component positioning problem based on corner points. Thus, we propose a robust, accurate, and efficient universal algorithm that incorporates preprocessing, coarse positioning, and fine positioning stages. Two types of model key points are introduced for interpreting the model component. To enhance positioning accuracy and robustness against illumination changes, the Harris corners and subpixel corner points are extracted from the images of real components. In the coarse positioning step, distance and shape feature matching methods are introduced to, respectively, compute the coarse and correct correspondences between type I model key points and Harris corner points. After the corresponding point pairs have been obtained, the coarse and fine positioning problems are formulated as least squares error problems. The effectiveness of the proposed method was verified by applying the method in several real component positioning experiments.

This paper is concerned with the exponential synchronization for master-slave chaotic delayed neural network with event trigger control scheme. The model is established on a network control framework, where both external disturbance and network-induced delay are taken into consideration. The desired aim is to synchronize the master and slave systems with limited communication capacity and network bandwidth. In order to save the network resource, we adopt a hybrid event trigger approach, which not only reduces the data package sending out, but also gets rid of the Zeno phenomenon. By using an appropriate Lyapunov functional, a sufficient criterion for the stability is proposed for the error system with extended ( , , )-dissipativity performance index. Moreover, hybrid event trigger scheme and controller are codesigned for network-based delayed neural network to guarantee the exponential synchronization between the master and slave systems. The effectiveness and potential of the proposed results are demonstrated through a numerical example.

An intelligent data-driven predictive control strategy is proposed in this paper. The predictive controller is designed by combining predictive control and local weighted projection regression. The presented control strategy needs less prior knowledge and has fewer parameters that are hard to determine compared to other data-driven predictive controller, e.g., the one in dynamic partial least square (PLS) framework. Furthermore, the proposed predictive controller performs better in the control of nonlinear processes and is able to update its parameters based on the online data. The predictive model validity and intelligence of the control strategy are guaranteed by the online updating strategy to a certain degree. The control performance of the proposed predictive controller against the model predictive control (MPC) in dynamic PLS framework is illustrated through the simulation of a typical numerical example and the benchmark of a continuous stirred tank heater system. It can be observed from the simulation that the proposed MPC strategy has higher prediction precision and stronger ability in coping with nonlinear dynamic processes which are quite common in practical applications, for instance, the industrial process.

In image classification, feature combination is often used to combine the merits of multiple complementary features and improve the classification accuracy compared with one single feature. Existing feature combination algorithms, e.g., multiple kernel learning, usually determine the weights of features based on the optimization with respect to some classifier-dependent objective function. These algorithms are often computationally expensive, and in some cases are found to perform no better than simple baselines. In this paper, we solve the feature combination problem from a totally different perspective. Our algorithm is based on the simple idea of combining only base kernels suitable to be combined. Since the very aim of feature combination is to obtain the highest possible classification accuracy, we measure the combination suitableness of two base kernels by the maximum possible cross-validation accuracy of their combined kernel. By regarding the pairwise suitableness as the kernel adjacency, we obtain a weighted graph of all base kernels and find that the base kernels suitable to be combined correspond to a cluster in the graph. We then use the dominant sets algorithm to find the cluster and determine the weights of base kernels automatically. In this way, we transform the kernel combination problem into a clustering one. Our algorithm can be implemented in parallel easily and the running time can be adjusted based on available memory to a large extent. In experiments on several data sets, our algorithm generates comparable classification accuracy with the state of the art.

The cluster synchronization problem is investigated using intermittent pinning control for the interacting clusters of nonidentical nodes that may represent either general linear systems or nonlinear oscillators. These nodes communicate over general network topology, and the nodes from different clusters are governed by different self-dynamics. A unified convergence analysis is provided to analyze the synchronization via intermittent pinning controllers. It is observed that the nodes in different clusters synchronize to the given patterns if a directed spanning tree exists in the underlying topology of every extended cluster (which consists of the original cluster of nodes as well as their pinning node) and one algebraic condition holds. Structural conditions are then derived to guarantee such an algebraic condition. That is: 1) if the intracluster couplings are with sufficiently strong strength and the pinning controller is with sufficiently long execution time in every period, then the algebraic condition for general linear systems is warranted and 2) if every cluster is with the sufficiently strong intracluster coupling strength, then the pinning controller for nonlinear oscillators can have its execution time to be arbitrarily short. The lower bounds are explicitly derived both for these coupling strengths and the execution time of the pinning controller in every period. In addition, in regard to the above-mentioned structural conditions for nonlinear systems, an adaptive law is further introduced to adapt the intracluster coupling strength, such that the cluster synchronization for nonlinear systems is achieved.

In this paper, a stochastic gradient descent (SGD)-based adaptive neural network (NN) control scheme is presented for a class of uncertain nonlinear systems. The introduction of the SGD algorithm results in a better tracking performance compared with some other adaptive NN methods without using SGD. This is because the proposed SGD-based adaptive NN control strategy provides optimization algorithms for the weights, the widths, and the centers of the NNs, which can achieve a good function approximation performance. In order to implement the proposed method, extended differentiators are introduced to get the differential estimations of error signals, such that the loss function of the optimization algorithm can be constructed approximatively. Moreover, adaptive laws are designed to reduce the overall approximation errors, such that the tracking performance is further improved. By using the Lyapunov stability theory, it can be proved that the target signal is tracked by the system output within a small error. Finally, simulation and comparison results are given to show the effectiveness and advantages of the proposed method.

This paper addresses the problem of delay-dependent robust and reliable, H∞static output feedback (SOF) control for uncertain discrete-time piecewise-affine (PWA) systems with time-delay and actuator failure in a singular system setup. The Markov chain is applied to describe the actuator faults behaviors. In particular, by utilizing a system augmentation approach, the conventional closed-loop system is converted into a singular PWA system. By constructing a mode-dependent piecewise Lyapunov-Krasovskii functional, a new H∞performance analysis criterion is then presented, where a novel summation inequality and S-procedure are succeedingly employed. Subsequently, thanks to the special structure of the singular system formulation, the PWA SOF controller design is proposed via a convex program. Illustrative examples are finally given to show the efficacy and less conservatism of the presented approach.

A data-driven model predictive control (MPC) that combines modified partial least squares (PLSs) and MPC is proposed in this paper. A theoretical comparison among traditional MPC, MPC in PLS framework and in modified PLS framework is presented, which demonstrates that the proposed MPC approach has high prediction precision and the ability in coping with dynamics in the process compared to MPC in traditional PLS framework. Furthermore, the proposed MPC requires no prior knowledge, and the simplicity in computation makes it possible to update the prediction model online. The model validity and intelligence of the control strategy are guaranteed by the model updating strategy to a certain degree. Steady-state performance and dynamic response of the proposed MPC is testified through a tracking control simulation of the benchmark of a continuous stirred tank heater system, which illustrates that the advantages of the proposed MPC.

This paper is concerned with the design of fault detection for sampled-data systems, which are based on multirate sampling, with frequency specifications. A general multirate system is considered in this paper, where not only the inputs and outputs but also their different channels have different sampling rates. The purpose of this paper is to make this residual system with multirate sampling satisfy a given disturbance attenuation level over a restricted frequency range. With the use of the lifting technique, this paper reformulates a single-rate linear time-invariant system, which is equivalent to the multirate time-varying system. For a given restricted frequency range, convex conditions are obtained in designing a required fault detection filter. Then, the restricted frequency ranges problem are also solved specifically via the generalized Kalman-Yakubovic̆-Popov lemma. Finally, this paper uses a continuous-stirred tank reactor system to illustrate the effectiveness and advantages of the fault detection filter design method.

In this paper, problem of identifying and tracking the power port of remote radio unit (RRU) is addressed. The testing of RRU requires the inspection robot to insert the probes into its power and network ports. In order to solve this problem, an experimental setup of visual servoing with 6 degrees of freedom (DoF) manipulator has been established. The initial problem of recognizing and tracking the power port of RRU has been resolved using template matching and camshift tracking algorithms. Furthermore, camshift tracking algorithm has been improved to work more accurately in this application. Modified algorithm addresses the problem of swapping of major and minor axes of camshift and enhances its application to 6 DoF from 4 DoF. Experimental results have been presented to support the research claims, and computational comparison of modified tracking algorithm with camshift has been shown.

Three-dimensional (3D) morphological information of Zebrafish larvae is important for investigating the development of the vertebrate model. Some existing automated handling systems have already been developed to reconstruct 3D models of micro-objects, but many commercial devices are generally costly and complicated to assemble, which limits their wide usage. In this paper, we present an automated visual servo platform to carve 3D model of Zebrafish larva in a simple and controllable manner. The proposed 3D carving strategy only involves a 4-DOF manipulator, a glass capillary and a micropump. The Zebrafish larva is first captured by the capillary mounted at the end of the manipulator. Then, the manipulator rotates larva body to desired orientations in order to obtain 2D images from different views. A structure-from-motion algorithm finally carves the 3D model of the larva body. Experimental results verify the validity of proposed methods, and a guideline of selecting the number of views is also given. As a high-cost-performance system, it has a considerable reference for reconstructing other microobjects.

Structure extraction is important for human perception. However, for various textured images, computers can hardly achieve this goal. Despite a plethora of studies to address the challenge, results from most previous methods contain unwanted artifacts and over-smoothed structures. Therefore, to address the weaknesses, we have proposed a variational model with end-to-end contour learning capability. Our formulation dwells in two observations: likelihood for representation of residual textures may be well abstracted using super Gaussian distribution, and edge metrics with semantic meaning may benefit structure preservation. The augmented Lagrangian method is adopted for optimal computation. Compared with classical approaches, our method offers a higher performance in structure extraction, including situations where the images have significant nonuniformity of the scale features.

Wireless bilateral control based haptic teleoperation is a potential and challenging technique that extends human beings' sensing to a remote environment. However, communication delay in a bilateral control system directly influences the performance and even produces instability. This paper presents a stable and high performance bilateral control with a switching algorithm. This proposal uses the reaction force from the slave robot as a beacon to determine whether the robot touches an environment object. Accordingly, the bilateral control algorithm is switched between a force-type three-channel control and a position-type three-channel control to improve the transparency. The stability is guaranteed by designing a passive system. In the proposal, a new idea of trade-off is proposed that guarantees the system stability and sacrifices the operational experience (transparency) only in the moment of contact. During the other operation time, the transparency in the states of free motion and contact is effectively improved. The proposed method is verified by experiments.

The purpose of this paper is to design an adaptive reliable H∞controller for multirate sampled-data systems. Sampled-data systems are widely adopted in the industrial process and multirate sampling is abundant in such systems. Sensor failure is one of the faults exist in the control systems, which can result in the instability. A reliable controller is proposed in this article to guarantee the stability and H∞performance of the multirate sampled-data systems when sensor failures happen. The lifting technique is used to convert a multirate system to an equivalent discrete system and the idea of the substitution is utilized to address the controller design problem based on adaptive mechanism. With the use of the mathematical model of the practical F-404 engine, an example is illustrated to prove the applicability of the proposed method.

Zebrafish has been demonstrated to be an important model organism in the study of genetics, diseases and drugs. For investigating drug toxicity, we need to inject foreign substances into specific organs within zebrafish larvae. Traditionally, zebrafish larva microinjection is conducted manually and requires operators to control larva's orientation with flexible ends, which is time-consuming, labor-intensive, and inaccurate. In this paper, we present an integrated microfluidic system to facilitate zebrafish larva organs microinjection, which is capable of adjusting larvae to appropriate orientation conveniently. The head's direction of zebrafish is adjusted through a microfluidic chip and a series of pumps so that the larva moves tail-first at the exit of the microfluidic channel, where it is rotated around its body axis to a desired orientation. Finally, the binary image of the larva is analysed to locate the organs and injection is executed. Experimental results are presented to verify the efficiency of the proposed method.

This study is concerned with the surface mount component positioning problem and an improved chamfer matching method based on iterative position and rotation angle estimation approach is proposed. Instead of performing matching of the image with different pre‐specified angle templates pixel by pixel, as does in traditional chamfer matching methods, the gradient information of the distance transform image is incorporated into the chamfer matching method and reduced computational cost of the method is achieved. The iterative formulas to update the translation and rotation angle are derived by reference to the characteristic of rigid body motion. The criterions of search region establishment, seed point selection and terminal conditions are given. The effectiveness of the proposed method is verified by applying the method to component positioning with actual captured images.

This paper is concerned with developing a distributed k-means algorithm and a distributed fuzzy c-means algorithm for wireless sensor networks (WSNs) where each node is equipped with sensors. The underlying topology of the WSN is supposed to be strongly connected. The consensus algorithm in multiagent consensus theory is utilized to exchange the measurement information of the sensors in WSN. To obtain a faster convergence speed as well as a higher possibility of having the global optimum, a distributed k-means++ algorithm is first proposed to find the initial centroids before executing the distributed k-means algorithm and the distributed fuzzy c-means algorithm. The proposed distributed k-means algorithm is capable of partitioning the data observed by the nodes into measure-dependent groups which have small in-group and large out-group distances, while the proposed distributed fuzzy c-means algorithm is capable of partitioning the data observed by the nodes into different measure-dependent groups with degrees of membership values ranging from 0 to 1. Simulation results show that the proposed distributed algorithms can achieve almost the same results as that given by the centralized clustering algorithms.

The bipartite consensus problem for a group of homogeneous generic linear agents with input saturation under directed interaction topology is examined. It is established that if each agent is asymptotically null controllable with bounded controls and the interaction topology described by a signed digraph is structurally balanced and contains a spanning tree, then the semi-global bipartite consensus can be achieved for the linear multiagent system by a linear feedback controller with the control gain being designed via the low gain feedback technique. The convergence analysis of the proposed control strategy is performed by means of the Lyapunov method which can also specify the convergence rate. At last, the validity of the theoretical findings is demonstrated by two simulation examples.

This paper investigates group synchronization for multiple interacting clusters of nonidentical systems that are linearly or nonlinearly coupled. By observing the structure of the coupling topology, a Lyapunov function-based approach is proposed to deal with the case of linear systems which are linearly coupled in the framework of directed topology. Such an analysis is then further extended to tackle the case of nonlinear systems in a similar framework. Moreover, the case of nonlinear systems which are nonlinearly coupled is also addressed, however, in the framework of undirected coupling topology. For all these cases, a consistent conclusion is made that group synchronization can be achieved if the coupling topology for each cluster satisfies certain connectivity condition and further, the intra-cluster coupling strengths are sufficiently strong. Both the lower bound for the intra-cluster coupling strength as well as the convergence rate are explicitly specified.

Data-driven fault detection plays an important role in industrial systems due to its applicability in case of unknown physical models. In fault detection, disturbances must be taken into account as an inherent characteristic of processes. Nevertheless, fault detection for nonlinear processes with deterministic disturbances still receive little attention, especially in data-driven field. To solve this problem, a just-in-time learning-based data-driven (JITL-DD) fault detection method for nonlinear processes with deterministic disturbances is proposed in this paper. JITL-DD employs JITL scheme for process description with local model structures to cope with processes dynamics and nonlinearity. The proposed method provides a data-driven fault detection solution for nonlinear processes with deterministic disturbances, and owns inherent online adaptation and high accuracy of fault detection. Two nonlinear systems, i.e., a numerical example and a sewage treatment process benchmark, are employed to show the effectiveness of the proposed method.

This paper is concerned with the distributed ℋ∞ consensus control problem for a discrete time-varying multiagent system with the stochastic communication protocol (SCP). A directed graph is used to characterize the communication topology of the multiagent network. The data transmission between each agent and the neighboring ones is implemented via a constrained communication channel where only one neighboring agent is allowed to transmit data at each time instant. The SCP is applied to schedule the signal transmission of the multiagent system. A sequence of random variables is utilized to capture the scheduling behavior of the SCP. By using the mapping technology combined with the Hadamard product, the closed-loop multiagent system is modeled as a time-varying system with a stochastic parameter matrix. The purpose of the addressed problem is to design a cooperative controller for each agent such that, for all probabilistic scheduling behaviors, the ℋ∞ consensus performance is achieved over a given finite horizon for the closed-loop multiagent system. A necessary and sufficient condition is derived to ensure the ℋ∞ consensus performance based on the completing squares approach and the stochastic analysis technique. Then, the controller parameters are obtained by solving two coupled backward recursive Riccati difference equations. Finally, a numerical example is given to illustrate the effectiveness of the proposed controller design scheme.

In the deployment of interval type-2 fuzzy systems, one of the most important steps is the type reduction. The commonly used center of sets type reducer requires the solution of two nonlinear constrained optimization problems. Frequently used approaches to solve them are the Karnik-Mendel algorithms and their variants. However, these algorithms suffer from the need for sorting, which is known to be computationally very expensive. Using the reformulations proposed in this paper for center of sets type reducer, it is possible to eliminate the need for sorting. This makes interval type-2 fuzzy systems more appropriate for cost-sensitive real-time applications. Extensive simulations are presented to illustrate the faster convergence speed of the proposed method over six other enhanced variants of the Karnik-Mendel algorithm as applied to center of sets type reduction of interval type-2 fuzzy systems.

In this paper, we propose a novel sparsity-based image error concealment (EC) algorithm through adaptive dual dictionary learning and regularization. We define two feature spaces: the observed space and the latent space, corresponding to the available regions and the missing regions of image under test, respectively. We learn adaptive and complete dictionaries individually for each space, where the training data are collected via an adaptive template matching mechanism. Based on the piecewise stationarity of natural images, a local correlation model is learned to bridge the sparse representations of the aforementioned dual spaces, allowing us to transfer the knowledge of the available regions to the missing regions for EC purpose. Eventually, the EC task is formulated as a unified optimization problem, where the sparsity of both spaces and the learned correlation model are incorporated. Experimental results show that the proposed method outperforms the state-of-the-art techniques in terms of both objective and perceptual metrics.

The containment control is studied for the second-order multiagent systems over a heterogeneous network where the position and velocity interactions are different. We consider three cases that multiple leaders are stationary, moving at the same constant speed, and moving at the same time-varying speed, and develop different containment control algorithms for each case. In particular, for the former two cases, we first propose the containment algorithms based on the well-established ones for the homogeneous network, for which the position interaction topology is required to be undirected. Then, we extend the results to the general setting with the directed position and velocity interaction topologies by developing a novel algorithm. For the last case with time-varying velocities, we introduce two algorithms to address the containment control problem under, respectively, the directed and undirected interaction topologies. For most cases, sufficient conditions with regard to the interaction topologies are derived for guaranteeing the containment behavior and, thus, are easy to verify. Finally, six simulation examples are presented to illustrate the validity of the theoretical findings.

This paper presents an adaptive neural network (NN)-based fault-tolerant control approach for the compensation of actuator failures in nonlinear systems with time-varying delay. The novelty of this paper lies in the fact that both the lock in place and loss of effectiveness faults, unmodeled dynamics, and dynamic disturbances are catered for simultaneously. Furthermore, this is achieved by the adaptation of only one parameter, which simplifies the computation of the control effort, and therefore extends its applicability. In the approach, the Razumikhin lemma and a dynamic signal are employed. It is shown that the output of the system converges to a neighborhood of the reference signal and the semiglobal boundedness of all signals is guaranteed. A simulation example is used to illustrate the validity and efficacy of the approach.

This paper is concerned with the state estimation problem for a class of nonlinear dynamical networks with time-varying delays subject to the round-robin protocol. The communication between the state estimator and the nodes of the dynamical networks is implemented through a shared constrained network, in which only one node is allowed to send data at each time instant. The round-robin protocol is utilized to orchestrate the transmission order of nodes. By using a switch-based approach, the dynamics of the estimation error is modeled by a periodic parameter-switching system with time-varying delays. The purpose of the problem addressed is to design an estimator, such that the estimation error is exponentially ultimately bounded with a certain asymptotic upper bound in mean square subject to the process noise and exogenous disturbance. Furthermore, such a bound is subsequently minimized by the designed estimator parameters. A novel Lyapunov-like functional is employed to deal with the dynamics analysis issue of the estimation error. Sufficient conditions are established to guarantee the ultimate boundedness of the estimation error in mean square by applying the stochastic analysis approach. Then, the desired estimator gains are characterized by solving a convex problem. Finally, a numerical example is given to illustrate the effectiveness of the estimator design scheme.

For a class of stochastic nonlinear time-delay systems with multiple constraints-predefined tracking constraint, input saturation, and output dead zone-the output tracking control problem is addressed in this paper. By expressing the saturated actuator as a smooth nonlinear function and employing the Nussbaum function technique, the input and output constraints problems are solved. The tracking performance is achieved under the predefined tracking constraint by utilizing the backstepping recursive design technique and the approximation property of neural networks. Then, based on the utilization of the Lyapunov-Krasovskii functional, the stochastic stability of the closed-loop system is achieved. Finally, the proposed control method is verified through a simulation example.

In this paper, the output feedback control problem is investigated for general nonlinear industrial processes. At the device layer, the nonlinear industrial processes with disturbances are modeled by utilizing Takagi-Sugeno modeling approach, and the corresponding local controllers are then designed to guarantee that the outputs for the local subsystems can track the decomposed setpoints. At the operation layer, considering the effect of radial basis function performance index and packet dropout phenomenon, a setpoint compensator is constructed to dynamically regulate the setpoints and track the given operation index. Finally, a network-based continuous stirred tank reactor system is considered to verify the validity of the proposed strategy in the simulation part.

This paper focuses mainly on the approximation-based fuzzy adaptive fault-tolerant control problem for nonlinear systems with unmodeled dynamics and unknown control directions. With the Nussbaum gain technique and a dynamic signal introduced, the difficulties from the unknown control directions and unmodeled dynamics are successfully overcome. Then, by taking advantage of the adaptive fuzzy control method and backstepping technology, we develop a fuzzy adaptive failure compensation control strategy and guarantee the semi-global boundedness for all signals. A simulation example is carried out to demonstrate the validity of the theoretical findings.

Signal filters are the foundation of many industrial fields, especially in high-performance motion control. However, commonly used low-pass filters exhibit serious phase lag, which degrades the performance of the whole system. This paper proposes a filter method that uses information from higher-order sensors. The proposal is an interfusion of several sensors. This method is based on the idea of pole-zero cancellation. Statistical data of the noise are not required, which improves the feasibility of the proposed method. Moreover, the basic idea can be extended to nonlinear situations. The proposed method achieves satisfactory noise suppression without significant phase lag. This characteristic guarantees the realization of robust motion control with good performance. Simulation and application experiments in motion control system confirm its validity.

This work aims to study the fault classification problem in complicated industrial processes. Two modified multi-classification methods of Support Vector Machine (SVM), i.e., Principal Component Analysis based Support Vector Machine (PCA-SVM) as well as Kernel Principal Component Analysis based Support Vector Machine (KPCA-SVM), are respectively proposed to classify multi-fault for the underlying process. The continuous stirred tank heater (CSTH) benchmark is adopted in simulation to validate the effectiveness of the proposed approaches. Simulation results indicate that compared with the original PCA-SVM, KPCA-SVM generates a higher classification rate for the underlying process at the cost of larger computation loads.

The target of the article is to solve the problem of fault detection for a multirate sampled-data system with frequency specifications. And the multirate condition considered in this article is that different channels of both the inputs and the outputs have different sampling rates. This paper aims to solve this fault detection problem with using a fault detection filter to make the residual system satisfy a given disturbance attenuation level over a restricted frequency range. The problem is solved via reformulating the multirate sampled-data system with extended inputs and outputs and utilizing the generalized Kalman-Yakubovič-Popov lemma to deal with the restricted frequency specifications. Finally, we use an example to illustrate the design procedure and show the advantages of the proposed method.

Modern smart grid, as a typical cyber-physical system (CPS), allows plug-in hybrid electric vehicles (PHEVs) to be a promising candidate for grid services. In this paper, by following the CPS design approach, we propose a novel framework for the local aggregator to estimate the charging status and solve for the charging control signals for PHEVs. The physical battery charging is executed by charging stalls, where charging information is processed in the embedded system and only the generated index information is transmitted to the aggregator via Internet. An aggregation model is developed for the entire cyberspace to inherently guarantee heterogeneous charging requirements, i.e., deadlines for charging. Furthermore, we develop a nonlinear model-predictive control (NMPC) scheme for the overnight valley-filling service. Both the aggregation model and control strategy are designed based on the PHEV population migration probabilities. From the CPS perspective, both the cyber and physical loads of this novel framework are extremely low. As part of this paper, we present a case study to verify the proposed approaches.

This paper is concerned with the problem of adaptive tracking control for a class of nonlinear systems with parametric uncertainty, bounded external disturbance, and actuator saturation. In order to achieve robust output tracking for the saturated uncertain nonlinear systems, a combination of adaptive robust control (ARC) and a novel terminal sliding-mode-based nonlinear disturbance observer (TSDO) is proposed, where the modeling inaccuracy and disturbance are integrated as a lumped disturbance. Specifically, the observer errors of estimating the lump disturbances converge to zero in finite-time for improving the precision of estimation. The estimated disturbances are then used in the controller to compensate for the system's lumped disturbances. The analytical results show that the proposed scheme is stable and can guarantee the asymptotic tracking with the tracking error converging to zero even in the presence of disturbances. Finally, the developed method is illustrated the effectiveness by the application to control of a quarter-car model with active suspension system.

This paper addresses robust multiobjective identification of driver nodes in the neuronal network of a cat's brain, in which uncertainties in determination of driver nodes and control gains are considered. A framework for robust multiobjective controllability is proposed by introducing interval uncertainties and optimization algorithms. By appropriate definitions of robust multiobjective controllability, a robust nondominated sorting adaptive differential evolution (NSJaDE) is presented by means of the nondominated sorting mechanism and the adaptive differential evolution (JaDE). The simulation experimental results illustrate the satisfactory performance of NSJaDE for robust multiobjective controllability, in comparison with six statistical methods and two multiobjective evolutionary algorithms (MOEAs): nondominated sorting genetic algorithms II (NSGA-II) and nondominated sorting composite differential evolution. It is revealed that the existence of uncertainties in choosing driver nodes and designing control gains heavily affects the controllability of neuronal networks. We also unveil that driver nodes play a more drastic role than control gains in robust controllability. The developed NSJaDE and obtained results will shed light on the understanding of robustness in controlling realistic complex networks such as transportation networks, power grid networks, biological networks, etc.

Multivariate time series (MTS) datasets broadly exist in numerous fields, including health care, multimedia, finance, and biometrics. How to classify MTS accurately has become a hot research topic since it is an important element in many computer vision and pattern recognition applications. In this paper, we propose a Mahalanobis distance-based dynamic time warping (DTW) measure for MTS classification. The Mahalanobis distance builds an accurate relationship between each variable and its corresponding category. It is utilized to calculate the local distance between vectors in MTS. Then we use DTW to align those MTS which are out of synchronization or with different lengths. After that, how to learn an accurate Mahalanobis distance function becomes another key problem. This paper establishes a LogDet divergence-based metric learning with triplet constraint model which can learn Mahalanobis matrix with high precision and robustness. Furthermore, the proposed method is applied on nine MTS datasets selected from the University of California, Irvine machine learning repository and Robert T. Olszewski's homepage, and the results demonstrate the improved performance of the proposed approach.

self-triggered control of networked systems. The system considered here includes parameter uncertainties, packet dropouts, and time delays. The time delay is described in a stochastic way, which takes a value from a given finite set. In order to compensate for the existence of deterministic packet dropouts, a new self-triggered control scheme is proposed. The main feature of the proposed self-triggered control strategy is that the next control task is predicted based on the self-triggered technique, in which the predicted event interval is divided equally for the sake of packet dropouts. The triggered condition is developed to ensure the stability of the uncertain sampled system by utilizing an uncertain algebraic Riccati equation and the comparison principle. Finally, an example of the inverted pendulum of a cart is provided to illustrate the effectiveness of the proposed results.

In this paper, the performance-based control design problem for double-layer networked industrial processes is investigated. At the device layer, the prescribed performance functions are first given to describe the output tracking performance, and then by using backstepping technique, new adaptive fuzzy controllers are designed to guarantee the tracking performance under the effects of input dead-zone and the constraint of prescribed tracking performance functions. At operation layer, by considering the stochastic disturbance, actual index value, target index value, and index prediction simultaneously, an adaptive inverse optimal controller in discrete-time form is designed to optimize the overall performance and stabilize the overall nonlinear system. Finally, a simulation example of continuous stirred tank reactor system is presented to show the effectiveness of the proposed control method.

This paper studies the delay robustness of a class of synchronous hybrid event- and time-driven consensus protocols in undirected networks. These protocols can ensure the system performance at reduced data-sampling rates. We consider three types of time delays in feedbacks, including one common time delay, multiple time-invariant delays, and multiple time-varying delays; and by sampled-data control techniques, we characterize the maximum allowable time delay and the event-detecting period for solving the average consensus problem in terms of the algebraic structure of interaction topologies. Simulations are given to show the effectiveness of theoretical results.

The key performance indicator (KPI) has an important practical value with respect to the product quality and economic benefits for modern industry. To cope with the KPI prognosis issue under nonlinear conditions, this paper presents an improved incremental learning approach based on available process measurements. The proposed approach takes advantage of the algorithm overlapping of locally weighted projection regression (LWPR) and partial least squares (PLS), implementing the PLS-based prognosis in each locally linear model produced by the incremental learning process of LWPR. The global prognosis results including KPI prediction and process monitoring are obtained from the corresponding normalized weighted means of all the local models. The statistical indicators for prognosis are enhanced as well by the design of novel KPI-related and KPI-unrelated statistics with suitable control limits for non-Gaussian data. For application-oriented purpose, the process measurements from real datasets of a proton exchange membrane fuel cell system are employed to demonstrate the effectiveness of KPI prognosis. The proposed approach is finally extended to a long-term voltage prediction for potential reference of further fuel cell applications.

This paper investigates the problem of output feedback robust ℋ∞control for a class of nonlinear spatially distributed systems described by first-order hyperbolic partial differential equations (PDEs) with Markovian jumping actuator faults. The nonlinear hyperbolic PDE systems are first expressed by Takagi-Sugeno fuzzy models with parameter uncertainties, and then, the objective is to design a reliable distributed fuzzy static output feedback controller guaranteeing the stochastic exponential stability of the resulting closed-loop system with certain ℋ∞disturbance attenuation performance. Based on a Markovian Lyapunov functional combined with some matrix inequality convexification techniques, two approaches are developed for reliable fuzzy static output feedback controller design of the underlying fuzzy PDE systems. It is shown that the controller gains can be obtained by solving a set of finite linear matrix inequalities based on the finite-difference method in space. Finally, two examples are presented to demonstrate the effectiveness of the proposed methods.

Clustering image pixels is an important image segmentation technique. While a large amount of clustering algorithms have been published and some of them generate impressive clustering results, their performance often depends heavily on user-specified parameters. This may be a problem in the practical tasks of data clustering and image segmentation. In order to remove the dependence of clustering results on user-specified parameters, we investigate the characteristics of existing clustering algorithms and present a parameter-free algorithm based on the DSets (dominant sets) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithms. First, we apply histogram equalization to the pairwise similarity matrix of input data and make DSets clustering results independent of user-specified parameters. Then, we extend the clusters from DSets with DBSCAN, where the input parameters are determined based on the clusters from DSets automatically. By merging the merits of DSets and DBSCAN, our algorithm is able to generate the clusters of arbitrary shapes without any parameter input. In both the data clustering and image segmentation experiments, our parameter-free algorithm performs better than or comparably with other algorithms with careful parameter tuning.

In object classification, feature combination can usually be used to combine the strength of multiple complementary features and produce better classification results than any single one. While multiple kernel learning (MKL) is a popular approach to feature combination in object classification, it does not always perform well in practical applications. On one hand, the optimization process in MKL usually involves a huge consumption of computation and memory space. On the other hand, in some cases, MKL is found to perform no better than the baseline combination methods. This observation motivates us to investigate the underlying mechanism of feature combination with average combination and weighted average combination. As a result, we empirically find that in average combination, it is better to use a sample of the most powerful features instead of all, whereas in one type of weighted average combination, the best classification accuracy comes from a nearly sparse combination. We integrate these observations into the k-nearest neighbors (kNNs) framework, based on which we further discuss some issues related to sparse solution and MKL. Finally, by making use of the kNN framework, we present a new weighted average combination method, which is shown to perform better than MKL in both accuracy and efficiency in experiments. We believe that the work in this paper is helpful in exploring the mechanism underlying feature combination.

This paper investigates the multirate networked industrial process control problem in double-layer architecture. First, the output tracking problem for sampled-data nonlinear plant at device layer with sampling period T(d) is investigated using adaptive neural network (NN) control, and it is shown that the outputs of subsystems at device layer can track the decomposed setpoints. Then, the outputs and inputs of the device layer subsystems are sampled with sampling period T(u) at operation layer to form the index prediction, which is used to predict the overall performance index at lower frequency. Radial basis function NN is utilized as the prediction function due to its approximation ability. Then, considering the dynamics of the overall closed-loop system, nonlinear model predictive control method is proposed to guarantee the system stability and compensate the network-induced delays and packet dropouts. Finally, a continuous stirred tank reactor system is given in the simulation part to demonstrate the effectiveness of the proposed method.

In this paper, a model based robust control for three-phase three-level Neutral Point Clamped (NPC) power converters is studied. Based on the continuous averaged model of the system, a Second Order Sliding Mode (SOSM) technique is employed to the control design. The control objectives are to achieve desired dc-link capacitor voltage regulation, voltage balance in the two dc-link capacitors and the instantaneous active and reactive power tracking. In order to achieve fast dynamic response of the proposed controller in the presence of external disturbances, an Extended State Observer (ESO) is employed to asymptotically reject the disturbances which are integrated in the controller design. The proposed control strategy has a cascaded structure which consists of power tracking (inner loop) ESO-based dc-link voltage regulation and capacitors voltage balance (outer loop). Multi-rate simulation illustrates the effectiveness and robustness of the proposed controller under parametric uncertainties and load variations.

Kernel method has been applied to many multivariate statistical analysis techniques. In this paper, we investigated the regression properties of Kernel Partial Least Squares (KPLS) and compared it to the standard technique. Basic mathematical algorithms and application of KPLS were shown. We further established regression model based on KPLS and demonstrated the model by a numerical case.

Support vector machine has obtained more and more attentions as a new method of machine learning based on the statistic learning theory. At the same time, there are increasing concerns about the fault diagnosis for practical engineering systems. Firstly, many kinds of SVM algorithms will be introduced, such as LS-SVM, LSVM and PSVM and so on. Besides, the advantages and disadvantage of those methods will be introduced. Finally, we discuss fault diagnosis and other aspects' recent applications and the directions we should research in the future.

With the rapid development in science and technology, data acquisition, storage and mining technology are widely applied to various fields. All aspects of people's lives are recorded as data. Through the analyzing and arranging of data, people can get a lot of valuable information. In this paper, support vector machine (SVM), least squares support vector machine (LSSVM) and partial least squares (PLS) are respectively used in the field of economic research. Real-time monitoring and forecasting for stock index is vital to the market. The changing trend and index of stocks are predicted according to the analysis to the history data of the stock. By combining particle swarm algorithm (PSO) algorithm and LSSVM algorithm, the parameters in the LSSVM model can be optimized. These algorithms are compared on the basis of their forecasting results.

Based on a two-stage heuristic method, this paper is concerned with the design of robust feedback controllers with restricted frequency-domain specifications (RFDSs) for uncertain linear discrete-time systems. Polytopic uncertainties are assumed to enter all the system matrices, while RFDSs are motivated by the fact that practical design specifications are often described in restricted finite frequency ranges. Dilated multipliers are first introduced to relax the generalized Kalman-Yakubovich-Popov lemma for output feedback controller synthesis and robust performance analysis. Then a two-stage approach to output feedback controller synthesis is proposed: at the first stage, a robust full-information (FI) controller is designed, which is used to construct a required output feedback controller at the second stage. To improve the solvability of the synthesis method, heuristic iterative algorithms are further formulated for exploring the feedback gain and optimizing the initial FI controller at the individual stage. The effectiveness of the proposed design method is finally demonstrated by the application to active control of suspension systems.

In this paper, the event-triggered state estimation problem is investigated for a class of complex networks with mixed time delays using sampled data information. A novel state estimator is presented to estimate the network states. A new event-triggered transmission scheme is proposed to reduce unnecessary network traffic between the sensors and the estimator, where the sampled data is transmitted to the estimator only when the so-called "event-triggered condition" is satisfied. The purpose of the problem addressed is to design an estimator for the complex network such that the estimation error is ultimately bounded in mean square. By utilizing Lyapunov theory combined with the stochastic analysis approach, sufficient conditions are established to guarantee the ultimate boundedness of the estimation error in mean square. Then, the desired estimator gain matrices are obtained via solving a convex problem. Finally, a numerical example is given to illustrate the effectiveness of the results.

Two major challenges in networked control systems are the time-varying networked-induced delays and the packet losses. To alleviate these problems, this study presents a novel fuzzy sliding mode controller, where a fuzzy system is used to estimate the nonlinear dynamical system online, and the networked-induced delay is handled by Pade approximation. The problem of packet losses is handled by viewing them as large time-varying delays in the system. The sliding mode-based design procedure used ensures the stability and the robustness of the proposed controller in the presence of disturbances and time-varying networked-induced time delays. Using an appropriate Lyapunov function, it is proved that the tracking error converges to the neighborhood of zero asymptotically. Furthermore, since the adaptation laws of the parameters are derived by using of the Lyapunov function, these laws are also found to be stable. Simulation results show that the proposed fuzzy sliding mode controller is capable of controlling nonlinear dynamical systems over a network, which is subject to bounded external disturbances, time-varying network-induced delays, and packet losses with adequate performance.

This paper investigates the adaptive fuzzy backstepping control and H∞performance analysis for a class of nonlinear systems with sampled and delayed measurements. In the control scheme, a fuzzy-estimator (FE) model is used to estimate the states of the controlled plant, while the fuzzy logic systems are used to approximate the unknown nonlinear functions in the nonlinear system. The controller is obtained based on the FE model by combining the backstepping technique with the classic adaptive fuzzy control method. In the stability analysis, all the signals in the closed-loop system are guaranteed to be semiglobally uniformly ultimately bounded (SUUB) and the outputs of the system are proven to converge to a small neighborhood of origin. Furthermore, the H∞performance is investigated and the outputs of the closed-loop system are bounded in the H∞sense. Two examples are given to illustrate the effectiveness of the proposed control scheme.

This paper presents the problem of finite-time stabilization for vehicle suspension systems with hard constraints based on terminal sliding-mode (TSM) control. As we know, one of the strong points of TSM control is its finite-time convergence to a given equilibrium of the system under consideration, which may be useful in specific applications. However, two main problems hindering the application of the TSM control are the singularity and chattering in TSM control systems. This paper proposes a novel second-order sliding-mode algorithm to soften the switching control law. The effect of the equivalent low-pass filter can be properly controlled in the algorithm based on requirements. Meantime, since the derivatives of term with fractional power do not appear in the control law, the control singularity is avoided. Thus, a chattering-free TSM control scheme for suspension systems is proposed, which allows both the chattering and singularity problems to be resolved. Finally, the effectiveness of the proposed approach is illustrated by both theoretical analysis and comparative experiment results.

A unified approach to the analysis of synchronization for complex dynamical networks, i.e., networks of partial-state coupled linear systems and networks of full-state coupled nonlinear oscillators, is introduced. It is shown that the developed analysis can be used to describe the difference between the state of each node and the weighted sum of the states of those nodes playing the role of leaders in the networks, thus making it feasible to consider the error dynamics for the whole network system. Different from the other various methods given in the existing literature, the analysis employed in this paper is demonstrated successfully in not only providing the consistent convergence analysis with much simpler form, but also explicitly specifying the convergence rate.

This paper focuses on the performance tracking issue of complex industrial processes in double layer architecture. First, the nonlinear plants in the device layer are modeled by using Takagi-Sugeno (T-S) fuzzy technique, and are controlled by local proportional integral (PI) controller with the H∞performance guaranteed. Then, the outputs and inputs of local plants are sampled and transited to the operation layer to form the economic performance index (EPI), which is used to represent the performance of the tracking of economic objective. Furthermore, the setpoints, which are dynamically changing, are calculated via a compensator based on the error between the objective and the EPI at each step of the operation layer. Finally, the effectiveness of the proposed method is demonstrated by a nonlinear continuous stirred tank reactor (CSTR) model.

In this study, the authors have proposed a new super resolution (SR) model based on the Perona–Malik regularisation scheme. The new model integrates into its regularisation component an adaptive exponential term which automatically adjusts itself depending on the local image features. This lends more sensitivity and adaptability to the proposed model, thereby making the reconstruction process much less punishing against semantically important features. Therefore, regularisation is stronger in homogeneous regions, and weaker in the neighbourhood of boundaries. The proposed method has a promising capability of supressing noise more effectively, while preserving important image features. The approach used differs significantly from the available methods, especially in the manner in which adaptability has been deployed. Noting that SR methods are less sensitive to the local image topography, a factor that causes the super‐resolved images to be visually poor, the new method sensitively probes the local features of the image, and determines the necessary level of reconstruction and regularisation. Additionally, the formulation robustly introduces a backward diffusion, a phenomenon proved from literature to have a tendency of sharpening edges. The authors have included empirical reconstruction results to demonstrate that their model produces better images in comparison with other classical methods.

This paper investigates the problem of robust model predictive control (RMPC) with saturations and packet dropouts. In this model, polytopic uncertainties are adopted to describe the inconsistency arising from the discretization process of sampling, while the occurrence probabilities of packet dropouts are time-varying and saturations are taken into account to describe input and output signals. The problem of exponential RMPC with saturations and packet dropouts is solved and characterized by a convex optimization problem. The developed results of RMPC are then applied to networked flotation processes, which are made up of three layers: direct control layer, set-point control layer, and optimization layer. The RMPC is used for compensating the output information from the optimization layer to the direct control layer such that the desired economic objective can be achieved. Simulations are presented to show the effectiveness of the proposed method.

Control gains play an important role in the control of a natural or a technical system since they reflect how much resource is required to optimize a certain control objective. This paper is concerned with the controllability of neuronal networks with constraints on the average value of the control gains injected in driver nodes, which are in accordance with engineering and biological backgrounds. In order to deal with the constraints on control gains, the controllability problem is transformed into a constrained optimization problem (COP). The introduction of the constraints on the control gains unavoidably leads to substantial difficulty in finding feasible as well as refining solutions. As such, a modified dynamic hybrid framework (MDyHF) is developed to solve this COP, based on an adaptive differential evolution and the concept of Pareto dominance. By comparing with statistical methods and several recently reported constrained optimization evolutionary algorithms (COEAs), we show that our proposed MDyHF is competitive and promising in studying the controllability of neuronal networks. Based on the MDyHF, we proceed to show the controlling regions under different levels of constraints. It is revealed that we should allocate the control gains economically when strong constraints are considered. In addition, it is found that as the constraints become more restrictive, the driver nodes are more likely to be selected from the nodes with a large degree. The results and methods presented in this paper will provide useful insights into developing new techniques to control a realistic complex network efficiently.

This paper addresses the fault detection filtering issue for a class of discrete system with network-induced nonlinear characteristics. The communication limitations, including measurements quantization, signal transmission delay, and data packet dropout, frequent in many practical networked control systems (NCSs) are considered. By transforming the residual system into an input-output form consisting two interconnected subsystems via a two-term approximation to the state delay variables, sufficient conditions are established. Under these conditions, the residual system is stochastically stable with a prescribed $\mathcal{H}_{\infty}$ level with the help of the scaled small gain (SSG) theorem developed for stochastic systems. Furthermore, the fault detection filter design approach is presented. A numerical example is also provided to demonstrate the effectiveness of the proposed method.

How to select and weigh features has always been a difficult problem in many image processing and pattern recognition applications. A data-dependent distance measure can address this problem to a certain extent, and therefore an accurate and efficient metric learning becomes necessary. In this paper, we propose a LogDet divergence-based metric learning with triplet constraints (LDMLT) approach, which can learn Mahalanobis distance metric accurately and efficiently. First of all, we demonstrate the good properties of triplet constraints and apply it in LogDet divergence-based metric learning model. Then, to deal with high-dimensional data, we apply a compressed representation method to learn, store, and evaluate Mahalanobis matrix efficiently. Besides, a dynamic triplets building strategy is proposed to build a feedback from the obtained Mahalanobis matrix to the triplet constraints, which can further improve the LDMLT algorithm. Furthermore, the proposed method is applied to various applications, including pattern recognition, facial expression recognition, and image retrieval. The results demonstrate the improved performance of the proposed approach.

Natural image statistics plays an important role in image denoising, and various natural image priors, including gradient-based, sparse representation-based, and nonlocal self-similarity-based ones, have been widely studied and exploited for noise removal. In spite of the great success of many denoising algorithms, they tend to smooth the fine scale image textures when removing noise, degrading the image visual quality. To address this problem, in this paper, we propose a texture enhanced image denoising method by enforcing the gradient histogram of the denoised image to be close to a reference gradient histogram of the original image. Given the reference gradient histogram, a novel gradient histogram preservation (GHP) algorithm is developed to enhance the texture structures while removing noise. Two region-based variants of GHP are proposed for the denoising of images consisting of regions with different textures. An algorithm is also developed to effectively estimate the reference gradient histogram from the noisy observation of the unknown image. Our experimental results demonstrate that the proposed GHP algorithm can well preserve the texture appearance in the denoised images, making them look more natural.

This paper is concerned with the problem of pinning synchronization of nonlinear dynamical networks with multiple stochastic disturbances. Two kinds of pinning schemes are considered: 1) pinned nodes are fixed along the time evolution and 2) pinned nodes are switched from time to time according to a set of Bernoulli stochastic variables. Using Lyapunov function methods and stochastic analysis techniques, several easily verifiable criteria are derived for the problem of pinning distributed synchronization. For the case of fixed pinned nodes, a novel mixed optimization method is developed to select the pinned nodes and find feasible solutions, which is composed of a traditional convex optimization method and a constraint optimization evolutionary algorithm. For the case of switching pinning scheme, upper bounds of the convergence rate and the mean control gain are obtained theoretically. Simulation examples are provided to show the advantages of our proposed optimization method over previous ones and verify the effectiveness of the obtained results.

In the industrial quality assurance procedures, the Automatic Visual Inspection (AVI) has been widely used for various tasks, such as dimension measurement, shape distortion detection and surface damage detection. First, an AVI system for E-shaped magnetic core elements is described and a surface damage inspection algorithm is proposed in this paper. Second, the paper proposed a robust K-tSL-center clustering method to improve the accuracy, robustness and efficiency of classification. Third, the gray-scale feature (S-feature) and Gabor wavelet feature (W-feature) of the interfaces of elements are extracted to combine the SW-feature and the proposed clustering method is used to classify these interfaces into normal and damaged areas. Performance evaluations are carried out on benchmark datasets and an E-shaped magnetic core image database, in which all images are captured by the designed AVI system. Experimental results show that the proposed methods achieve an improved performance when comprising with the state-of-the-art methods in this application.

This paper deals with the modeling and parameter tuning of a spar-type floating wind turbine with a tuned mass damper (TMD) installed in nacelle. Firstly, a mathematical model for the system surge-heave-pitch motion is established based on first principles. Secondly, different parameter tuning methods are adopted to find the optimal TMD parameters for load reduction. Thirdly, nonlinear wind turbine simulations with different designs are conducted under different wind and wave conditions. The results show that TMD with small spring and damping coefficients will help to produce much load reduction in above rated condition. However, it may deteriorate system performance when the turbine is working below rated. In contrast, the design with large spring and damping constants will achieve moderate load reduction in both working conditions.

In this paper, we investigate the multiobjective identification of controlling areas in the neuronal network of a cat's brain by considering two measures of controllability simultaneously. By utilizing nondominated sorting mechanisms and composite differential evolution (CoDE), a reference-point-based nondominated sorting composite differential evolution (RP-NSCDE) is developed to tackle the multiobjective identification of controlling areas in the neuronal network. The proposed RP-NSCDE shows its promising performance in terms of accuracy and convergence speed, in comparison to nondominated sorting genetic algorithms II. The proposed method is also compared with other representative statistical methods in the complex network theory, single objective, and constraint optimization methods to illustrate its effectiveness and reliability. It is shown that there exists a tradeoff between minimizing two objectives, and therefore pareto fronts (PFs) can be plotted. The developed approaches and findings can also be applied to coordination control of various kinds of real-world complex networks including biological networks and social networks, and so on.

This paper investigates the problem of robust H(∞) state estimation for a class of continuous-time nonlinear systems via Takagi-Sugeno (T-S) fuzzy affine dynamic models. Attention is focused on the analysis and design of an admissible full-order filter such that the resulting filtering error system is asymptotically stable with a guaranteed H(∞) disturbance attenuation level. It is assumed that the plant premise variables, which are often the state variables or their functions, are not measurable so that the filter implementation with state-space partition may not be synchronous with the state trajectories of the plant. Based on piecewise quadratic Lyapunov functions combined with S-procedure and some matrix inequality linearization techniques, some new results are presented for the filtering design of the underlying continuous-time T-S fuzzy affine systems. Illustrative examples are given to validate the effectiveness and application of the proposed design approaches.

In this paper, the distributed synchronization problem of networks of agent systems with controllers and nonlinearities subject to Bernoulli switchings is investigated. Controllers and adaptive updating laws injected in each vertex of networks depend on the state information of its neighborhood. Three sets of Bernoulli stochastic variables are introduced to describe the occurrence probabilities of distributed adaptive controllers, updating laws and nonlinearities, respectively. By the Lyapunov functions method, we show that the distributed synchronization of networks composed of agent systems with multiple randomly occurring nonlinearities, multiple randomly occurring controllers, and multiple randomly occurring updating laws can be achieved in mean square under certain criteria. The conditions derived in this paper can be solved by semi-definite programming. Moreover, by mathematical analysis, we find that the coupling strength, the probabilities of the Bernoulli stochastic variables, and the form of nonlinearities have great impacts on the convergence speed and the terminal control strength. The synchronization criteria and the observed phenomena are demonstrated by several numerical simulation examples. In addition, the advantage of distributed adaptive controllers over conventional adaptive controllers is illustrated.

This paper investigates the H∞ consensus control problem of nonlinear multiagent systems under an arbitrary topological structure. A novel Takagi-Sukeno (T-S) fuzzy modeling method is proposed to describe the problem of nonlinear follower agents approaching a time-varying leader, i.e., the error dynamics between the follower agents and the leader, whose dynamics is evolving according to an isolated unforced nonlinear agent model, is described as a set of T-S fuzzy models. Based on the model, a leader-following consensus algorithm is designed so that, under an arbitrary network topology, all the follower agents reach consensus with the leader subject to external disturbances, preserving a guaranteed H(∞) performance level. In addition, we obtain a sufficient condition for choosing the pinned nodes to make the entire multiagent network reach consensus. Moreover, the fuzzy modeling method is extended to solve the synchronization problem of nonlinear systems, and a fuzzy H(∞) controller is designed so that two nonlinear systems reach synchronization with a prescribed H(∞) performance level. The controller design procedure is greatly simplified by utilization of the proposed fuzzy modeling method. Finally, numerical simulations on chaotic systems and arbitrary nonlinear functions are provided to illustrate the effectiveness of the obtained theoretical results.

This paper investigates the problem of robust H∞output feedback control for a class of continuous-time Takagi-Sugeno (T-S) fuzzy affine dynamic systems with parametric uncertainties and input constraints. The objective is to design a suitable constrained piecewise affine static output feedback controller, guaranteeing the asymptotic stability of the resulting closed-loop fuzzy control system with a prescribed H∞disturbance attenuation level. Based on a smooth piecewise quadratic Lyapunov function combined with S-procedure and some matrix inequality convexification techniques, some new results are developed for static output feedback controller synthesis of the underlying continuous-time T-S fuzzy affine systems. It is shown that the controller gains can be obtained by solving a set of linear matrix inequalities (LMIs). Finally, three examples are provided to illustrate the effectiveness of the proposed methods.

An input–output approach to the stability and stabilization of uncertain Takagi–Sugeno (T–S) fuzzy systems with time-varying delay is proposed in this paper. The time-varying parameter uncertainties are assumed to be norm-bounded, and the delay is intervally time varying. A novel method is employed to approximate the time-varying delay, based on which the considered system is transformed into a feedback interconnection form. The new formulation of the system is comprised of a forward subsystem with constant time delay and a feedback subsystem embedding the uncertainties. By applying the scaled small-gain theorem to the converted system, less conservative stability and stabilization criteria are obtained. Moreover, the applicability of the proposed approach to the robust case is simpler since both delay and parameter uncertainties are processed in a unified framework. Numerical experiments are performed to illustrate the advantage of the proposed techniques.

Automated optical inspection (AOI) has been widely used in industrial Quality Assurance (QA) procedures. Multi-task inspection in high-speed AOI systems is becoming a significant problem in the design. In this paper, the design of an AOI system for E-shaped magnetic core elements is briefly described and several novel algorithms are proposed to realize defects detection by this system. First, this paper proposes a robust k-tSL-center clustering method to classify the interfaces of the element into normal and damaged areas. Second, a modified Active Shape Model (ASM) method is adopted to perform shape distortion detection in real-time. Performance evaluations are carried out on an E-shaped Magnetic Core Image Database, in which all images are captured by the designed AOI system. Experimental results show that the proposed methods are more efficient, robust and accurate than state-of-the-art methods in this application.

Networked control systems (NCSs) have, in recent years, brought many innovative impacts to control systems. However, great challenges are also met due to the network-induced imperfections. Such network-induced imperfections are handled as various constraints, which should appropriately be considered in the analysis and design of NCSs. In this paper, the main methodologies suggested in the literature to cope with typical network-induced constraints, namely time delays, packet losses and disorder, time-varying transmission intervals, competition of multiple nodes accessing networks, and data quantization are surveyed; the constraints suggested in the literature on the first two types of constraints are updated in different categorizing ways; and those on the latter three types of constraints are extended.

The articles in this special section focus on advancements in theories and industrial applications of networked control systems in the industrial informatics industry.

In this paper, we describe a novel algorithm for unsupervised segmentation of images with low depth of field (DOF). First of all, a multi-scale reblurring model is used to detect the object of interest (OOI) in saliency space. Then, to determine the boundary of OOI, an active contour model based on hybrid energy function is proposed. In this model, a global energy item related with the saliency map is adopted to find the global minimum, and a local energy term regarding the low DOF image is used to improve the segmentation precision. In addition, an adaptive parameter is attached to this model to balance the weight of global and local energy. Furthermore, an unsupervised curve initialization method is designed to reduce the number of evolution iterations. Finally, we conduct experiments on various low DOF images, and the results demonstrate the high robustness and precision of the proposed approach.

In this note, we study the problems of stability analysis and H∞controller synthesis of discrete-time switched systems with time-varying delay. The system under consideration is firstly transformed into an interconnection system. Based on the system transformation and the scaled small gain theorem, the asymptotic stability of the original system is examined via the version of the bounded realness of the transformed forward system. The aim of the proposed approach is to reduce conservatism, which is made possible by a precise approximation of the time-varying delay and the input-output approach. The proposed stability condition is demonstrated to be much less conservative than most existing results. Moreover, the problem of H∞controller synthesis involving convex optimization is further solved based on the stability condition, whose effectiveness are also illustrated via numerical examples.

This paper is concerned with designing a fuzzy robust H∞filter for a class of continuous-time nonlinear systems via Takagi-Sugeno (T-S) fuzzy affine dynamic models based on piecewise Lyapunov functions. Attention is focused on the analysis and design of an admissible full-order filter such that the filtering error dynamics is stochastically stable and a prescribed H∞attenuation level is guaranteed. It is assumed that the plant premise variables are not measurable so that the filter implementation with state space partition may not be synchronous with the state trajectories of the plant. Based on piecewise quadratic Lyapunov functions (PQLFs) combined with S-procedure and some matrix inequality linearization techniques, some new results are established for filtering design of the underling continuous-time T-S fuzzy affine systems. An examples is given to illustrate the effectiveness and applicability of the proposed design methods.

This paper puts forward an actuators and sensors allocation approach to the design of the adjacent buildings vibration attenuation under seismic excitation. A full order model of an adjacent buildings system with the location information of actuators and sensors is considered and by retaining the modes which make the largest contributions to the model with the Modal Cost Analysis (MCA), a reduced order model is established so that the controller can be designed conveniently. In view of the fact that not all the states of the system can be measured by the sensors, a dynamic output feedback H∞controller is designed for the adjacent buildings system. By considering that the output powers of the actuators are limited, a mixed H∞=GH2control is employed. Genetic algorithm (GA) is brought forward to design the dynamic output feedback controller and obtain the locations of the actuators and sensors. With the proposed approach, the allocation problem is solved and corresponding controller is obtained, which attenuates the building vibration at a sufficiently low level with constrained acting forces. Simulations demonstrate the effectiveness of the proposed approach in attenuating building vibration under earthquake excitation and some comparisons are made among the building systems with different quantities of actuators.

The quality or key performance indicator related prediction and diagnosis cover a wide range of practical requirements from industrial applications. Although much effort has been devoted to establishing an analytical model between operating conditions and quality variables based on the first principals, it is still a challenge in practice due to the complexity of large-scale industrial process. To solve this problem, the data-driven quality related prediction and monitoring schemes are proposed in this paper. In order to overcome the drawbacks of standard approach, our focus is firstly concentrated on the modifications of standard partial least squares. Moreover, under industrial operating conditions, a subspace aided data-driven approach is further utilized to construct a soft sensor in the framework of diagnostic observer based residual generator. The proposed approaches are finally applied to quality based prediction and diagnosis on an industrial hot strip mill process. Application results indicate the effectiveness of the proposed methods and demonstrate improvement in performance compared to the standard technique.

Controlling regions in cortical networks, which serve as key nodes to control the dynamics of networks to a desired state, can be detected by minimizing the eigenratio R and the maximum imaginary part \sigma of an extended connection matrix. Until now, optimal selection of the set of controlling regions is still an open problem and this paper represents the first attempt to include two measures of controllability into one unified framework. The detection problem of controlling regions in cortical networks is converted into a constrained optimization problem (COP), where the objective function R is minimized and \sigma is regarded as a constraint. Then, the detection of controlling regions of a weighted and directed complex network (e.g., a cortical network of a cat), is thoroughly investigated. The controlling regions of cortical networks are successfully detected by means of an improved dynamic hybrid framework (IDyHF). Our experiments verify that the proposed IDyHF outperforms two recently developed evolutionary computation methods in constrained optimization field and some traditional methods in control theory as well as graph theory. Based on the IDyHF, the controlling regions are detected in a microscopic and macroscopic way. Our results unveil the dependence of controlling regions on the number of driver nodes l and the constraint r. The controlling regions are largely selected from the regions with a large in-degree and a small out-degree. When r=+ \infty, there exists a concave shape of the mean degrees of the driver nodes, i.e., the regions with a large degree are of great importance to the control of the networks when l is small and the regions with a small degree are helpful to control the networks when l increases. When r=0, the mean degrees of the driver nodes increase as a function of l. We find that controlling \sigma is becoming more important in controlling a cortical network with increasing l. The methods and results of detecting controlling regions in this paper would promote the coordination and information consensus of various kinds of real-world complex networks including transportation networks, genetic regulatory networks, and social networks, etc.

This paper is focused on reliable fuzzy$H_{\infty }$controller design for active suspension systems with actuator delay and fault. The Takagi–Sugeno (T–S) fuzzy model approach is adapted in this study with the consideration of the sprung and the unsprung mass variation, the actuator delay and fault, and other suspension performances. By the utilization of the parallel-distributed compensation scheme, a reliable fuzzy$H_{\infty }$performance analysis criterion is derived for the proposed T–S fuzzy model. Then, a reliable fuzzy$H_{\infty }$controller is designed such that the resulting T–S fuzzy system is reliable in the sense that it is asymptotically stable and has the prescribed$H_{\infty }$performance under given constraints. The existence condition of the reliable fuzzy$H_{\infty }$controller is obtained in terms of linear matrix inequalities (LMIs) Finally, a quarter-vehicle suspension model is used to demonstrate the effectiveness and potential of the proposed design techniques.

This paper is concerned with the problem of robust${\mathscr H}_{\infty }$output feedback control for a class of continuous-time Takagi–Sugeno (T–S) fuzzy affine dynamic systems using quantized measurements. The objective is to design a suitable observer-based dynamic output feedback controller that guarantees the global stability of the resulting closed-loop fuzzy system with a prescribed${\mathscr H}_{\infty }$disturbance attenuation level. Based on common/piecewise quadratic Lyapunov functions combined with S-procedure and some matrix inequality convexification techniques, some new results are developed to the controller synthesis for the underlying continuous-time T–S fuzzy affine systems with unmeasurable premise variables. All the solutions to the problem are formulated in the form of linear matrix inequalities (LMIs). Finally, two simulation examples are provided to illustrate the advantages of the proposed approaches.

This paper investigates the problem of a Takagi–Sugeno (T–S) fuzzy-model-based control of a nonlinear overhead crane system with input delay and actuator saturation. The complex nonlinear dynamic system of the crane is modeled as a three-rule T–S fuzzy model with a saturated input. Based on the fuzzy model, a state-feedback controller is designed so that trajectories of the system that start from an ellipsoid will remain in it, where a decay rate is introduced to accelerate the response speed. Besides, since the input delay often appears in real equipment, the delayed feedback control is also considered with respect to the actuator saturation. Delay-dependent existence conditions of the fuzzy controller are established such that the load can be placed in a desired position by the crane with a much suppressed swing angle, where trajectories of the closed-loop system that start from a bounded set will asymptotically converge to a contractively invariant ellipsoid. The results are formulated in the form of linear matrix inequalities, which can be readily solved via standard numerical software. Simulations on the true plant are illustrated to show the feasibility and effectiveness of the proposed control method.

Iris authentication is one of the most successful applications in video analysis and image processing. In this paper, several novel approaches are proposed to improve the overall performance of iris recognition systems. First, this paper proposes a new eyelash detection algorithm based on directional filters, which achieves a low rate of eyelash misclassification. Second, a multiscale and multidirection data fusion method is introduced to reduce the edge effect of wavelet transformation produced by complex segmentation algorithms. Finally, an iris indexing method on the basis of corner detection is presented to accelerate exhausted the 1: N search in a huge iris database. The performance evaluations are carried out on two popular iris databases, and the test results are experimentally more robust and accurate with less elapsed time compared with most existing methods.

Maximizing the controllability of complex networks by selecting appropriate nodes and designing suitable control gains is an effective way to control distributed complex networks. In this paper, some novel particle swarm optimization (PSO) approaches are developed to enhance the controllability of distributed networks. The proposed PSO algorithm is combined with a global search scheme and a modified simulated binary crossover (MSBX). In addition, the node importance-based method is introduced to study the controllability of distributed complex networks. A set of experiments show that the PSO with the global search and the MSBX (PSO-GSBX) can outperform some well-known evolutionary algorithms and pinning schemes. Following the PSO-GSBX approach, some interesting findings about pinned nodes, coupling strengths and the eigenvalues for enhancing the controllability of distributed networks are revealed. The obtained results and methods are applied in unmanned aerial vehicle (UAV) coordination to show their effectiveness. These findings will help to understand controllability of complex networks and can be applied in control science and industrial system.

This paper is concerned with the network-based robust fault detection problem for a class of uncertain discrete-time Takagi-Sugeno fuzzy systems with stochastic mixed time delays and successive packet dropouts. The mixed time delays comprise both the multiple discrete time delays and the infinite distributed delays. A sequence of stochastic variables is introduced to govern the random occurrences of the discrete time delays, distributed time delays, and successive packet dropouts, where all the stochastic variables are mutually independent but obey the Bernoulli distribution. The main purpose of this paper is to design a fuzzy fault detection filter such that the overall fault detection dynamics is exponentially stable in the mean square and, at the same time, the error between the residual signal and the fault signal is made as small as possible. Sufficient conditions are first established via intensive stochastic analysis for the existence of the desired fuzzy fault detection filters, and then, the corresponding solvability conditions for the desired filter gains are established. In addition, the optimal performance index for the addressed robust fuzzy fault detection problem is obtained by solving an auxiliary convex optimization problem. An illustrative example is provided to show the usefulness and effectiveness of the proposed design method.

The problem of the convergence of the consensus strategies for multiple agents with double-integrator dynamics is studied in this paper. The investigation covers two kinds of different settings. In the setting with the interaction topologies for the position and velocity information flows being modeled by different graphs, some sufficient conditions on the fixed interaction topologies are derived for the agents to reach consensus. In the setting with the interaction topologies for the position and velocity information flows being modeled by the same graph, we systematically investigate the consensus algorithm for the agents under both fixed and dynamically changing directed interaction topologies. Specifically, for the fixed case, a necessary and sufficient condition on the interaction topology is established for the agents to reach (average) consensus under certain assumptions. For the dynamically changing case, some sufficient conditions are obtained for the agents to reach consensus, where the condition imposed on the dynamical topologies is shown to be more relaxed than that required in the existing literature. Finally, we demonstrate the usefulness of the theoretical findings through some numerical examples.

This paper focuses on fuzzy H∞controller design for uncertain active suspension systems with actuator delay based on Takagi-Sugeno (T-S) model approach. This dynamic system is presented by taking into account the sprung and unsprung mass variations, the actuator delay, and the suspension performance. The fuzzy H∞controller is designed such that the resulting T-S fuzzy system is asymptotically stable and guarantees H∞performance, and simultaneous satisfying the constraint performance. The existence condition of fuzzy H∞control is obtained in terms of linear matrix inequalities (LMIs) and can be solved by using the standard software. A quarter-car suspension model is provided to validate the effectiveness of the proposed design procedures.

This paper is concerned with synchronizing complex networks with arbitrary topological structures via pinning control. The necessary and sufficient conditions are established for choosing the pinned nodes to guarantee the pinning synchronzability of the complex networks. Under the assumption of the sufficient large coupling strength, it is shown that the way to pin the nodes is a decisive factor in determining the pinning synchronizability of complex networks and the entire network can achieve an exponentially fast speed of synchronization.

This paper investigates the problem of robustH∞state estimation for a class of multichannel networked nonlinear systems with multiple packet dropouts. The nonlinear plant is represented by Takagi-Sugeno (T-S) fuzzy-affine dynamic models with norm-bounded uncertainties, and stochastic variables with general probability distributions are adopted to characterize the data missing phenomenon in output channels. The objective is to design an admissible state estimator guaranteeing the stochastic stability of the resulting estimation-error system with a prescribedH∞disturbance attenuation level. It is assumed that the plant premise variables, which are often the state variables or their functions, are not measurable so that the estimator implementation with state-space partition may not be synchronized with the state trajectories of the plant. Based on a piecewise-quadratic Lyapunov function combined with S -procedure and some matrix-inequality-convexifying techniques, two different approaches are developed to robust filtering design for the underlying T-S fuzzy-affine systems with unreliable communication links. All the solutions to the problem are formulated in the form of linear-matrix inequalities (LMIs). Finally, simulation examples are provided to illustrate the effectiveness of the proposed approaches.

This paper investigates the problem of robust $\hbox{\scr{H}}_{\infty }$ output-feedback control for a class of networked nonlinear systems with multiple packet dropouts. The nonlinear plant is represented by Takagi–Sugeno (T–S) fuzzy affine dynamic models with norm-bounded uncertainties, and stochastic variables that satisfy the Bernoulli random binary distribution are adopted to characterize the data-missing phenomenon. The objective is to design an admissible output-feedback controller that guarantees the stochastic stability of the resulting closed-loop system with a prescribed $\hbox{\scr{H}}_{\infty }$ disturbance attenuation level. It is assumed that the plant premise variables, which are often the state variables or their functions, are not measurable so that the controller implementation with state-space partition may not be synchronous with the state trajectories of the plant. Based on a piecewise quadratic Lyapunov function combined with an S-procedure and some matrix inequality convexifying techniques, two different approaches to robust output-feedback controller design are developed for the underlying T–S fuzzy affine systems with unreliable communication links. The solutions to the problem are formulated in the form of linear matrix inequalities (LMIs). Finally, simulation examples are provided to illustrate the effectiveness of the proposed approaches.

This paper investigates the problem of H∞filtering for a class of nonlinear discrete-time systems with measurement quantization and packet dropouts. Each output is transmitted via an independent communication channel, and the phenomenon of packet dropouts in transmission is governed by an individual random binary distribution, while the quantization errors are treated as sector-bound uncertainties. Based on a piecewise-Lyapunov function, an approach to the design of H∞-piecewise filter is pro posed such that the filtering-error system is stochastically stable with a guaranteed H∞performance. Some slack matrices are introduced to facilitate the filter design procedure by eliminating the coupling between the Lyapunov matrices and the system matrices. The filter parameters can be obtained by solving a set of linear matrix inequalities (LMIs), which are numerically tractable with commercially available software. Finally, two illustrative examples are provided to show the effectiveness of the proposed method.

This brief is concerned with the robust stability problem for a class of discrete-time uncertain Markovian jumping neural networks with defective statistics of modes transitions. The parameter uncertainties are considered to be norm-bounded, and the stochastic perturbations are described in terms of Brownian motion. Defective statistics means that the transition probabilities of the multimode neural networks are not exactly known, as assumed usually. The scenario is more practical, and such defective transition probabilities comprise three types: known, uncertain, and unknown. By invoking the property of the transition probability matrix and the convexity of uncertain domains, a sufficient stability criterion for the underlying system is derived. Furthermore, a monotonicity is observed concerning the maximum value of a given scalar, which bounds the stochastic perturbation that the system can tolerate as the level of the defectiveness varies. Numerical examples are given to verify the effectiveness of the developed results.

In this paper, the robust global asymptotic stability (RGAS) of generalized static neural networks (SNNs) with linear fractional uncertainties and a constant or time-varying delay is concerned within a novel input-output framework. The activation functions in the model are assumed to satisfy a more general condition than the usually used Lipschitz-type ones. First, by four steps of technical transformations, the original generalized SNN model is equivalently converted into the interconnection of two subsystems, where the forward one is a linear time-invariant system with a constant delay while the feedback one bears the norm-bounded property. Then, based on the scaled small gain theorem, delay-dependent sufficient conditions for the RGAS of generalized SNNs are derived via combining a complete Lyapunov functional and the celebrated discretization scheme. All the results are given in terms of linear matrix inequalities so that the RGAS problem of generalized SNNs is projected into the feasibility of convex optimization problems that can be readily solved by effective numerical algorithms. The effectiveness and superiority of our results over the existing ones are demonstrated by two numerical examples.

In this paper, the robustH∞-control problem is investigated for a class of uncertain discrete-time fuzzy systems with both multiple probabilistic delays and multiple missing measurements. A sequence of random variables, all of which are mutually independent but obey the Bernoulli distribution, is introduced to account for the probabilistic communication delays. The measurement-missing phenomenon occurs in a random way. The missing probability for each sensor satisfies a certain probabilistic distribution in the interval. Here, the attention is focused on the analysis and design ofH∞fuzzy output-feedback controllers such that the closed-loop Takagi-Sugeno (T-S) fuzzy-control system is exponentially stable in the mean square. The disturbance-rejection attenuation is constrained to a given level by means of theH∞-performance index. Intensive analysis is carried out to obtain sufficient conditions for the existence of admissible output feedback controllers, which ensures the exponential stability as well as the prescribedH∞performance. The cone-complementarity-linearization procedure is employed to cast the controller-design problem into a sequential minimization one that is solved by the semi-definite program method. Simulation results are utilized to demonstrate the effectiveness of the proposed design technique in this paper.

This paper investigates the problem of robust H∞output-feedback control for a class of nonlinear systems under unreliable communication links. The nonlinear plant is represented by a Takagi-Sugeno (T-S) uncertain fuzzy model, and the communication links between the plant and controller are assumed to be imperfect, i.e., data-packet dropouts occur intermittently, which is often the case in a network environment. Stochastic variables that satisfy the Bernoulli random-binary distribution are adopted to characterize the data-missing phenomenon, and the attention is focused on the design of a piecewise static-output-feedback (SOF) controller such that the closed-loop system is stochastically stable with a guaranteed H∞performance. Based on a piecewise Lyapunov function combined with some novel convexifying techniques, the solutions to the problem are formulated in the form of linear matrix inequalities (LMIs). Finally, simulation examples are also provided to illustrate the effectiveness of the proposed approaches.

In this brief, the problem of passivity analysis is investigated for a class of uncertain neural networks (NNs) with both discrete and distributed time-varying delays. By constructing a novel Lyapunov functional and utilizing some advanced techniques, new delay-dependent passivity criteria are established to guarantee the passivity performance of NNs. Essentially different from the available results, when estimating the upper bound of the derivative of Lyapunov functionals, we consider and best utilize the additional useful terms about the distributed delays, which leads to less conservative results. These criteria are expressed in the form of convex optimization problems, which can be efficiently solved via standard numerical software. Numerical examples are provided to illustrate the effectiveness and less conservatism of the proposed results.

This paper addresses the problem of asymptotic stability for discrete-time recurrent neural networks with time-varying delay. The analysis starts with a general assumption that the time-varying delay may be expressed as the lower bound plus the length of an interval over which the delay varies. Then the delay partitioning technique is used to establish a new delay-dependent sufficient condition under which the asymptotic stability of recurrent neural networks with time-varying delay can be guaranteed. The new stability criterion takes the form of linear matrix inequalities, thus lending itself to being readily checkable by the available software package. The obtained theoretical result is further illustrated by numerical results, including their superiority over the existing results on asymptotic stability of delayed recurrent neural networks.

This paper is concerned with theHinfinfuzzy control problem for a class of systems with repeated scalar nonlinearities and random packet losses. A modified Takagi-Sugeno (T-S) fuzzy model is proposed in which the consequent parts are composed of a set of discrete-time state equations containing a repeated scalar nonlinearity. Such a model can describe some well-known nonlinear systems such as recurrent neural networks. The measurement transmission between the plant and controller is assumed to be imperfect and a stochastic variable satisfying the Bernoulli random binary distribution is utilized to represent the phenomenon of random packet losses. Attention is focused on the analysis and design ofHinfinfuzzy controllers with the same repeated scalar nonlinearities such that the closed-loop T-S fuzzy control system is stochastically stable and preserves a guaranteedHinfinperformance. Sufficient conditions are obtained for the existence of admissible controllers, and the cone complementarity linearization procedure is employed to cast the controller design problem into a sequential minimization one subject to linear matrix inequalities, which can be readily solved by using standard numerical software. Two examples are given to illustrate the effectiveness of the proposed design method.

This paper investigates the problem of$H_{\infty }$fuzzy control of nonlinear systems under unreliable communication links. The nonlinear plant is represented by a Takagi--Sugeno (T--S) fuzzy model, and the control strategy takes the form of parallel distributed compensation. The communication links existing between the plant and controller are assumed to be imperfect (that is, data packet dropouts occur intermittently, which appear typically in a network environment), and stochastic variables satisfying the Bernoulli random binary distribution are utilized to model the unreliable communication links. Attention is focused on the design of$H_{\infty }$controllers such that the closed-loop system is stochastically stable and preserves a guaranteed$H_{\infty }$performance. Two approaches are developed to solve this problem, based on the quadratic Lyapunov function and the basis-dependent Lyapunov function, respectively. Several examples are provided to illustrate the usefulness and applicability of the developed theoretical results.

This paper is concerned with the problem ofHinfinfuzzy filtering of nonlinear systems with intermittent measurements. The nonlinear plant is represented by a Takagi-Sugeno (T-S) fuzzy model. The measurements transmission from the plant to the filter is assumed to be imperfect, and a stochastic variable satisfying the Bernoulli random binary distribution is utilized to model the phenomenon of the missing measurements. Attention is focused on the design of anHinfinfilter such that the filter error system is stochastically stable and preserves a guaranteedHinfinperformance. A basis-dependent Lyapunov function approach is developed to design theHinfinfilter. By introducing some slack matrix variables, the coupling between the Lyapunov matrix and the system matrices is eliminated, which greatly facilitates the filter-design procedure. The developed theoretical results are in the form of linear matrix inequalities (LMIs). Finally, an illustrative example is provided to show the effectiveness of the proposed approach.

This paper proposes a new approach, namely, the delay partitioning approach, to solving the problems of stability analysis and stabilization for continuous time-delay Takagi-Sugeno fuzzy systems. Based on the idea of delay fractioning, a new method is proposed for the delay-dependent stability analysis of fuzzy time-delay systems. Due to the instrumental idea of delay partitioning, the proposed stability condition is much less conservative than most of the existing results. The conservatism reduction becomes more obvious with the partitioning getting thinner. Based on this, the problem of stabilization via the so-called parallel distributed compensation scheme is also solved. Both the stability and stabilization results are further extended to time-delay fuzzy systems with time-varying parameter uncertainties. All the results are formulated in the form of linear matrix inequalities (LMIs), which can be readily solved via standard numerical software. The advantage of the results proposed in this paper lies in their reduced conservatism, as shown via detailed illustrative examples. The idea of delay partitioning is well demonstrated to be efficient for conservatism reduction and could be extended to solving other problems related to fuzzy delay systems.

This paper investigates the problem of fault detection for Takagi-Sugeno (T-S) fuzzy systems with intermittent measurements. The communication links between the plant and the fault detection filter are assumed to be imperfect (i.e., data packet dropouts occur intermittently, which appear typically in a network environment), and a stochastic variable satisfying the Bernoulli random binary distribution is utilized to model the unreliable communication links. The aim is to design a fuzzy fault detection filter such that, for all data missing conditions, the residual system is stochastically stable and preserves a guaranteed performance. The problem is solved through a basis-dependent Lyapunov function method, which is less conservative than the quadratic approach. The results are also extended to T--S fuzzy systems with time-varying parameter uncertainties. All the results are formulated in the form of linear matrix inequalities, which can be readily solved via standard numerical software. Two examples are provided to illustrate the usefulness and applicability of the developed theoretical results.

This paper is concerned with the stability analysis of discrete-time recurrent neural networks (RNNs) with time delays as random variables drawn from some probability distribution. By introducing the variation probability of the time delay, a common delayed discrete-time RNN system is transformed into one with stochastic parameters. Improved conditions for the mean square stability of these systems are obtained by employing new Lyapunov functions and novel techniques are used to achieve delay dependence. The merit of the proposed conditions lies in its reduced conservatism, which is made possible by considering not only the range of the time delays, but also the variation probability distribution. A numerical example is provided to show the advantages of the proposed conditions.

This paper is concerned with the problems of stability analysis and stabilization for discrete-time Takagi-Sugeno fuzzy systems with time-varying state delay. By constructing a new fuzzy Lyapunov function and by making use of novel techniques, an improved delay-dependent stability condition is obtained, which is dependent on the lower and upper delay bounds. The merit of the proposed stability condition lies in its reduced conservatism, which is achieved by avoiding the utilization of some bounding inequalities for the cross products between two vectors. Then, a delay-dependent stabilization approach based on a parallel distributed compensation scheme is developed for both state feedback and observer-based output feedback cases. The proposed stability and stabilization conditions are formulated in terms of linear matrix inequalities, which can be solved efficiently by using existing optimization techniques. Two illustrative examples are provided to demonstrate the effectiveness of the results proposed in this paper.

In this paper, the problem of asymptotic stability for stochastic Hopfield neural networks (HNNs) with time delays is investigated. New delay-dependent stability criteria are presented by constructing a novel Lyapunov-Krasovskii functional. Moreover, the results are further extended to the delayed stochastic HNNs with parameter uncertainties. The main idea is based on the delay partitioning technique, which differs greatly from most existing results and reduces conservatism. Numerical examples are provided to illustrate the effectiveness and less conservativeness of the developed techniques.

This paper investigates the problem of guaranteed cost control for Takagi-Sugeno fuzzy dynamic systems with interval parameter uncertainties. The parameter uncertainty is characterized by the matrix bound, which is quite natural in real applications. Attention is focused on the fuzzy state feedback controller design via the so-called parallel distributed compensation scheme, which guarantees the closed-loop system to be robustly stable with a prescribed upper bound of the cost function. By utilizing the instrumental idea of delay dividing, a new Lyapunov-Krasovskii functional is introduced, which leads the resultant conditions to be much less conservative than most existing results in the literature. Some other new ideas such as basis dependence are also employed, which help to reduce the conservatism. All the results are formulated in the form of linear matrix inequalities (LMIs), which can readily be solved via standard numerical software. Finally, illustrative examples are given to show the less conservatism and applicability of the obtained results.

The paper considers the problem of synchronization for dynamical systems with unreliable communication links which are modeled as stochastic dropouts. A robust state feedback controller is designed such that the closed-loop system is stochastically stable in the mean square and preserves a guaranteed H∞performance. A numerical example is provided to show the effectiveness of the proposed results.

This paper presents a new approach to the design of robust filters for uncertain 2-D systems described by the Fornasini-Marchesini (FM) model. The polynomially parameter-dependent approach is developed to solve the addressed filtering problem, with a new linear matrix inequality condition obtained for the existence of desired Hinfinfilters. An example is given to show the reduced conservatism of the proposed method.

In this brief, the problem of global asymptotic stability for delayed Hopfield neural networks (HNNs) is investigated. A new criterion of asymptotic stability is derived by introducing a new kind of Lyapunov-Krasovskii functional and is formulated in terms of a linear matrix inequality (LMI), which can be readily solved via standard software. This new criterion based on a delay fractioning approach proves to be much less conservative and the conservatism could be notably reduced by thinning the delay fractioning. An example is provided to show the effectiveness and the advantage of the proposed result.

In this correspondence, the problem of exponential stability for neural networks with time delay is investigated. By introducing a novel Lyapunov-Krasovskii functional with the idea of delay fractioning, a new criterion of exponential stability is derived and then formulated in terms of a linear matrix inequality. This new criterion proves to be much less conservative than the most recent result, and the conservatism can be notably reduced as the fractioning goes thinner. An example is provided to demonstrate the advantage of the proposed result.

This paper investigates the problem of H∞fuzzy control of nonlinear systems under unreliable communication links. The nonlinear plant is represented by a Takagi-Sugeno fuzzy model, and the control strategy takes the form of parallel distributed compensation. The communication links, existing between the plant and controller, are assumed to be imperfect (that is, data-packet dropouts occur intermittently, which appear typically in a network environment), and stochastic variables satisfying the Bernoulli random binary distribution are utilized to model the unreliable communication links. Attention is focused on the design of H∞controllers such that the closedloop system is stochastically stable and preserves a guaranteed H∞performance. Two approaches are developed to solve this problem, based on quadratic Lyapunov function and basisdependent Lyapunov function respectively. Several examples are provided to illustrate the usefulness and applicability of the developed theoretical results.

This paper investigates the problem of stabilization for a Takagi-Sugeno (T-S) fuzzy system with nonuniform uncertain sampling. The sampling is not required to be periodic, and the only assumption is that the distance between any two consecutive sampling instants is less than a given bound. By using the input delay approach, the T-S fuzzy system with variable uncertain sampling is transformed into a continuous-time T-S fuzzy system with a delay in the state. Though the resulting closed-loop state-delayed T-S fuzzy system takes a standard form, the existing results on delay T-S fuzzy systems cannot be used for our purpose due to their restrictive assumptions on the derivative of state delay. A new condition guaranteeing asymptotic stability of the closed-loop sampled-data system is derived by a Lyapunov approach plus the free weighting matrix technique. Based on this stability condition, two procedures for designing state-feedback control laws are given: one casts the controller design into a convex optimization by introducing some over design and the other utilizes the cone complementarity linearization idea to cast the controller design into a sequential minimization problem subject to linear matrix inequality constraints, which can be readily solved using standard numerical software. An illustrative example is provided to show the applicability and effectiveness of the proposed controller design methodology.

This paper is concerned with vibration control of a flexible spacecraft in the presence of parametric uncertainty/external disturbances as well as control input nonlinearity through distributed piezoelectric sensor/actuator technology. To satisfy pointing requirements and simultaneously suppress vibrations, two separate control loops are adopted. The first uses piezoceramics as sensors and actuators to actively suppress certain flexible modes by designing positive position feedback (PPF) compensators which add damping to the flexible structures in certain critical modes. The second feedback loop is designed based on an output feedback sliding mode control (OFSMC) design where control input nonlinearity is taken into consideration. Simulation studies for the proposed control strategy on a flexible spacecraft demonstrate the effectiveness of the proposed approach

This paper investigates the problem of robust H/sub /spl infin//, and l/sub 2/-l/sub /spl infin// filtering for a class of uncertain nonlinear discrete-time systems with multiple state delays. It is assumed that the parameter uncertainties appearing in all the system matrices reside in a polytope and the nonlinearities entering into both the state and measurement equations satisfy global Lipschitz conditions. Attention is focused on the design of robust full-order and reduced-order filters guaranteeing a prescribed noise attenuation level in an H/sub /spl infin// or l/sub 2/-l/sub /spl infin// sense with respect to all energy-bounded noise disturbances for all admissible uncertainties and time delays. Sufficient conditions for the existence of such filters are formulated in terms of a couple of linear matrix inequalities (LMIs), upon which admissible filters can be obtained from the solution of convex optimization problems. A numerical example is presented to illustrate the feasibility of the developed filter design methods.

This paper is concerned with the problem of stabilizing controller design for 2-D systems with Markovian jump parameters. The mathematical model of 2D jump systems is established upon the well-known Roesser model, and sufficient conditions are obtained for the existence of desired controllers in terms of linear matrix inequalities (LMIs), which can be readily solved by available numerical software. A numerical example is provided to show the applicability of the proposed theories.

This paper considers the problem of robust stabilization for stochastic time-delay systems with convex polytopic uncertainties. Sufficient conditions for the solvability of the problem are obtained by using parameter-independent and parameter-dependent Lyapunov functional, respectively. It is shown that the result derived by a parameter-dependent Lyapunov functional is less conservative. A desired state feedback controller can be designed by solving a set of linear matrix inequalities.