Laparoscopic navigation systems increasingly leverage augmented reality (AR) to overlay preoperative anatomical data in real time, enhancing surgical precision. However, reliable navigation requires accurate camera pose estimation and highfidelity 3D organ reconstruction. To address these challenges, we propose LA-SLAM, a real-time visual SLAM system specifically designed for laparoscopic surgery. The system incorporates three key innovations: (1) An optical-flow-based depth-pose joint estimation module. This module establishes accurate dense correspondences between images through the introduced global correlation softmax, providing sufficiently strong constraints for subsequent optimization to achieve high-precision pose and depth estimation with only a single update, significantly improving computational efficiency; (2) a hybrid loop closure strategy that integrates projection flow and dense optical flow for reliable detection, and introduces a Sim(3)-based optimization using 3D point cloud feature matching; and (3) a refined 3D reconstruction strategy combining depth consistency validation with statistical and gradient-based filtering to address laparoscopic challenges, including specular reflections, occlusions, and tissue deformation. Evaluations on standard SLAM datasets (TUM-RGBD, EuRoC) and laparoscopic datasets (SCARED, DePoLL, Stereo-MIS) demonstrate that LA-SLAM achieves tracking accuracy comparable to state-of-the-art SLAM systems while maintaining significant advantages in computational efficiency. It also produces geometrically faithful and visually robust reconstructions. These results highlight LA-SLAM’s potential for integration into real-time surgical navigation systems.

A novel collaborative position and orientation control scheme (CPOCS) for dual-arm robots is proposed, which is capable of controlling the end-effectors' positions with high precision while preserving their orientations unchanged to some practical tasks (e.g., box handling). To solve the proposed CPOCS in real time while considering key factors such as unknown bounded noise and strict time response constraints in practical engineering environments, this article proposes a novel precisely predefined-time convergent barrier recurrent neural network (PCB-RNN) based on a newly developed piecewise barrier evolution formula. Unlike existing RNNs, the proposed PCB-RNN, owing to its piecewise barrier evolution formula, can achieve precisely predefined-time convergence (PPTC) when addressing the proposed CPOCS under unknown bounded noise conditions. Comprehensive theoretical analysis rigorously proves the PPTC ability of the PCB-RNN under both noise-free and unknown bounded noise conditions. Furthermore, extensive simulation and physical experiments on dual-arm robots validate the effectiveness of the proposed CPOCS and demonstrate the advanced PPTC capability of the proposed PCB-RNN under unknown bounded noises.

The reliability of signature verification systems and the accuracy of forensic signature comparison are affected by factors, such as time and writing posture. Current work on posture-induced variations relies mainly on qualitative classification methods, which lack objective quantitative metrics. To understand the underlying patterns of dynamic signature handwriting under various time and postural conditions, this study collected 12 000 signature samples across five sessions, incorporating six postures. A signature posture dataset was defined using four joint angles (wrist, elbow, hip, and ankle) to quantify signing postures. Different statistical methods were applied to analyze the static and dynamic features of signature handwriting in order to evaluate the significance of time and posture influences on signature features. For the signature posture dataset, the generalized linear model and structural equation model were employed to assess the extent of joint angle effects. Principal component analysis was further utilized to identify the features that are the most significantly affected by posture. The results show that time and posture have statistically significant effects on different signature features. Posture-induced variations predominantly (80% ) arise from differences between sitting and nonsitting postures, with 69.5% of these variations attributable to changes in hip and ankle joint angles. Among the posture-sensitive features, area exhibits the highest sensitivity to postural variations. These results provide important insights for research in the fields of signature verification and forensic handwriting.

Time-variant-gain zeroing neural networks (TVG-ZNNs) are among the most powerful solvers for time-variant linear matrix-vector inequalities (TVLMVIs). Although TVG-ZNNs with complex nonlinear activation functions achieve effective convergence within finite or predefined time, they incur high computational costs and face challenges in precisely predefining their actual convergence time. In contrast, TVG-ZNNs with linear activation functions offer lower computational costs but struggle to achieve convergence within a finite or predefined time. In addition, the gain values of most existing TVG-ZNNs tend to increase over time, resulting in a significant rise in computational costs. To address these contradictory issues, we propose a novel hybrid-gain ZNN without a nonlinear activation function (HG-ZNN-WNAF) to solve TVLMVIs in both noisy and noise-free environments. Specifically, a new hybrid gain is cleverly designed to construct the HG-ZNN-WNAF activated by a linear activation function, while ensuring that the gain value does not keep increasing over time. Unlike the state-of-the-art TVG-ZNNs with or without nonlinear activation functions, our proposed HG-ZNN-WNAF achieves precisely predefined-time convergence due to the hybrid gain, meaning its actual convergence time can be accurately predefined. Additionally, the piecewise design of the hybrid gain, along with the use of the simple linear activation function, effectively reduces the model's computational cost. Rigorous theoretical analysis demonstrates the precisely predefined-time convergence ability of the HG-ZNN-WNAF in both noisy and noise-free environments. Simulation and physical experiments validate the theoretical analysis and demonstrate that the HG-ZNN-WNAF achieves state-of-the-art performance in terms of convergence speed, robustness, and computational cost.

Traditional magnetic tactile sensors are highly susceptible to external magnetic field interference, limiting their reliability in practical applications. To address this challenge, we propose a dual-modal soft magnetic skin capable of simultaneously acquiring magnetic and force tactile information across spatiotemporal domains, inspired by the sensory mechanisms of human skin. The system integrates a Convolutional Neural Network-Convolutional Neural Network-Multilayer Perceptron (CNN-CNN-MLP) architecture to fuse these dual-modal signals effectively. Furthermore, we introduce a novel Dynamic Weighting Coefficient Layer (DWCL) to dynamically optimize fusion weights for each modality based on real-time input characteristics, thereby enhancing robustness against magnetic interference. The DWCL leverages temporal discrepancies between modalities during pre-contact sensing and quantifies the magnetic field strength of target objects to autonomously adjust fusion ratios, prioritizing the more reliable modality under varying interference conditions. Extensive experimental evaluations demonstrate that the proposed DWCL significantly improves interference resistance compared to conventional fusion methods, advancing the feasibility of magnetic tactile sensing in real-world environments.

Accurate prediction of grasp stability is crucial for reliable and precise operations with multi-fingered robotic hands. Traditional methods tend to oversimplify tactile information and pay equal attention to all regions of the data. This can obscure subtle yet critical variations and introduce noise, increasing the risk of stability assessment errors. To address these challenges, a novel self-supervised method, Adversarial Subgraph Contrastive Learning (ASCL), is proposed. It constructs an instance graph from the spatial distribution and features of perceptual nodes. It employs a bi-level adversarial strategy to enhance latent data representations by maximizing the mutual information between the instance graph and its semantic subgraphs, while minimizing it with its noisy subgraphs. To prevent trivial solutions and continuous relaxation of semantic subgraphs, node confidence and edge connection terms are incorporated to ensure stabilization. From an information-theoretic perspective, ASCL exhibits notable advantages on unlabeled or sparsely labeled data, well outperforming existing methods in empirical tests with robotic hands.

This article focuses on an observer-based adaptive sensor fault compensation fixed-time tracking control problem for uncertain nonlinear systems. A sixth-power Lyapunov function is designed for the first time which lays the foundation to construct the effective adaptive fixed-time fault compensation mechanism. Meanwhile, in the controller design procedure, owing to the existence of the sensor fault, only the actual output can be measured, unlike existing results, an improved state observer is constructed to estimate the unmeasured states effectively. Under our developed control mechanism, all closed-loop signals are bounded within fixed-time interval, observation errors and tracking error can converge into a small domain around zero. Simulation verifies the availability of the presented approach further.

Zeroing neural network (ZNN), as a special type of recurrent neural network (RNN), is very competitive in solving time-varying linear matrix-vector equations. Recently, various ZNNs with predefined-time convergence (PTC) capabilities have been reported. Such ZNNs with PTC capabilities can achieve the predefined convergence time via explicitly presetting multiple parameters related to the upper bounds of their convergence time. However, obtaining suitable and robust values for these parameters through reasonable adjustments is a challenging task in many engineering applications. To address this problem, we propose a novel arbitrarily predefined-time convergent RNN (APTC-RNN) with a novel nonlinear piecewise activation-function (NPAF). Unlike most existing ZNNs with PTC capabilities, the proposed APTC-RNN, due to its NPAF, can achieve arbitrarily PTC (APTC) without adjusting any upper bound parameters. Furthermore, due to the piecewise computation form of the NPAF, the proposed APTC-RNN can provide a lower computational cost compared to most existing RNNs. The stability and APTC capability of the proposed APTC-RNN are proven by rigorous theoretical analysis and mathematical derivation. Numerical simulations show that APTC-RNN has faster and more accurate PTC capability than three state-of-the-art RNNs, while having less computational time. Finally, the practicality of the APTC-RNN is verified by applying it to the UR3 robotic arm and multiagent systems.

Laparoscopic liver surgery is a newly developed minimally invasive technique and represents an inevitable trend in the future development of surgical methods. By using augmented reality (AR) technology to overlay preoperative CT models with intraoperative laparoscopic videos, surgeons can accurately locate blood vessels and tumors, significantly enhancing the safety and precision of surgeries. Point cloud registration technology is key to achieving this effect. However, there are two major challenges in registering the CT model with the point cloud surface reconstructed from intraoperative laparoscopy. First, the surface features of the organ are not prominent. Second, due to the limited field of view of the laparoscope, the reconstructed surface typically represents only a very small portion of the entire organ. To address these issues, this paper proposes the keypoint correspondence registration network (KCR-Net). This network first uses the neighborhood feature fusion module (NFFM) to aggregate and interact features from different regions and structures within a pair of point clouds to obtain comprehensive feature representations. Then, through correspondence generation, it directly generates keypoints and their corresponding weights, with keypoints located in the common structures of the point clouds to be registered, and corresponding weights learned automatically by the network. This approach enables accurate point cloud registration even under conditions of extremely low overlap. Experiments conducted on the ModelNet40, 3Dircadb, DePoLL demonstrate that our method achieves excellent registration accuracy and is capable of meeting the requirements of real-world scenarios.

In general, the motion control problem of redundant robot manipulators (RRMs) can be transformed into a constrained time-varying quadratic programming (TVQP) problem. Recently, various recurrent neural networks (RNNs) with predefined time convergence (PTC) abilities have been proposed to solve this constrained TVQP problem in real-time. However, there is still a lack of a unified paradigm to guide researchers and engineers design such RNNs more effectively based on specific requirements. To bridge this gap, we propose a unified paradigm derived from a novel segmentation evolution formula incorporating a special$\mathfrak{B}$–Classfunction. This paradigm enables the construction of various RNNs, collectively referred to as unified arbitrarily predefined-time convergent RNNs (U-APTC-RNNs). Compared with most existing RNNs, the constructed U-APTC-RNN has two significant advantages: 1) it has the arbitrarily PTC (APTC) ability, meaning its actual convergence time can be arbitrarily and precisely predefined without setting other model parameters and 2) using a novel piecewise computation strategy, redundant nonlinear calculations are effectively minimized, leading to a notable reduction in computational costs. The stability and APTC ability of the constructed U-APTC-RNN are demonstrated through detailed theoretical analysis. Numerical simulation experiments confirm the APTC capabilities of various U-APTC-RNNs constructed using the proposed unified paradigm. Comparative experiments show that U-APTC-RNN has more competitive convergence performance and lower computational cost than other state-of-the-art RNNs with PTC abilities. Finally, simulation and physical motion control experiments on the Jaco and UR5 robotic arms demonstrate the superiority and practicality of the proposed U-APTC-RNN.

This paper addresses the stabilization problem of linear impulsive systems with beyond-interval delays, for which state correlation exists between the current interval’s impulse state estimation and the historical feedback, making it difficult to obtain stability conditions for the system. In order to solve this problem, we develop a novel impulsive control method called interval partitioning, for which sufficient conditions for system stability are obtained. It indicates that time delays in impulses potentially contribute to the stabilization of linear systems with unstable system matrix, if there exist some historical state feedback. The effectiveness of the proposed approach is demonstrated through three examples.

Fractional-order chaotic systems show great potential in secure communications because of their unique properties. In order to improve their encryption and anti-attack capabilities, a new compounding mechanism is developed for fractional-order multidrive and response chaotic systems. Rather than using simple addition, which is often employed in existing work, the compounding is based on multiplication, making the system topology much more complex and difficult to predict. A synchronization method is thus developed for the compounded system using a projective approach and the synchronization error is able to converge to zero within the range defined by a prescribed performance function. In addition, the developed synchronization controller represents a general form, and it can be easily transformed into other methods by choosing different design parameters. Simulation results and comparative analysis are also carried out between the proposed and prior control techniques to illustrate the effectiveness of the presented scheme.

In this article, the adaptive tracking control problem is considered for high-order stochastic nonlinear time-delay systems in fixed-time. Being different from existing results, an improved Lyapunov-Krasovskii function is designed, which can not only compensate for the time-delay term but also remove the obstacle from the high-order term. Due to the introduction of the Lyapunov-Krasovskii function into the total Lyapunov function, it makes it difficult to stabilize the controlled system within a fixed-time interval. L'Hopital's rule is used to determine the boundedness of the Lyapunov-Krasovskii function, and the fixed-time boundedness of the integral functions can be inferred. By utilizing the fixed-time Lyapunov stability theorem, it is proved that the controlled system is semi-globally practical fixed-time stable (SGPFS), all the closed-loop signals (CLSs) are bounded within the fixed-time interval, and the tracking error converges into a small region around zero. The validity of the designed scheme is substantiated via simulation results.

Point cloud completion aims to predict the missing part for an incomplete 3D shape. Existing point cloud completion methods based on deep learning complete the point cloud by extracting global features from the incomplete point cloud. However, such methods cannot generate a uniformly distributed point cloud and the accurate structure details of the object. To solve the problem, a novel method for completing point clouds is proposed in this paper. Our approach is a two-step strategy. First, to predict the sparse point cloud with uniform density, the Sequence Generation Completion (SGC) method is proposed. By numbering the subspace obtained from the spatial subdivision, the point cloud is represented with a sequence of numbers and the point cloud completion problem is turned into a sequence generation problem. Second, to obtain the dense point cloud and generate the accurate structural details of point clouds, we propose a resolution scale network (RSN). This network takes local resolution as input and increases the weight of low-resolution regions by learning to preserve the comprehensive structural information of the sparse point cloud, which is crucial to generate dense point cloud. The comprehensive experiments on several public datasets demonstrate the effectiveness of our method. Source code and pretrained models will be available at github.com/Pikachu-NCU/Sequence-Generate-Completion-Method.

The multi-dimensional force/torque decoupling and calibration is extremely crucial to increase the accuracy of the Wheel Force Transducer/Sensor (WFT). A novel interpretable nonlinear decoupling and calibration approach to WFT is presented. A physical interpretable prime-error framework is developed such that the linear prime part accounts for most force-voltage responses while the nonlinear error part accounts for the gross error deviation. The conventional least-square decoupling is improved with the delicate nonlinear error modeling using a polynomial base module and a hyperbolic activation function. The developed framework is proved to be mathematically solvable and physically feasible by a two-step calibration scheme. A two-axis WFT is tested and compared with the proposed interpretable nonlinear decoupling model (IND), the least-square-based method (LSM), and the error-based neural network model (eNN). Results demonstrate that the proposed IND provides an accurate, practical, and effective scheme for modeling and calibrating WFTs and maintains a good balance among accuracy, generalization ability, and computational efficiency for real applications.

Emergency events such as fire, flood and COVID-19 occurred in urban rail transit (URT) usually triggered chain effect and evolved into huge disaster. This kind of chain with complexity and uncertainty evolution brought great challenges to the safety management of the system. Thus, the coupling effects of emergencies and then its relationship with the chain evolution is necessary to analyze emphatically. A Graph Evaluation and Review Technique Simulation (GERTS) evolution network is firstly constructed to describe the coupling effect and chain evolution of emergencies. Then, considering the internal and external influencing factors of the emergency chain, a dynamic evolution model of the emergency chain based on Coupled Map Lattice (CML) is proposed. This paper takes fire chain of URT as an example to simulate the evolution process of emergency chain, and analyze the impact of different coupling effects and various influencing factors on the evolution of emergency chain. The results of numerical simulation show that the AND-coupling can significantly inhibit the evolution of emergency events, while the OR-coupling and CO-coupling can expand the impact scope of emergency events. In addition, the evolution speed of emergency events can be controlled by increasing the coupling action time and improving the URT repair ability. When an emergency event occurs, the analysis of coupling effect and the accompanied chain evolution will help managers to make scientific judgment on the development trend of the emergency events and make targeted emergency defense measures.

In this research, the adaptive neural network consensus control problem is addressed for a class of non-affine multiagent systems (MASs) with actuator faults and stochastic disturbances. To overcome difficulties associated with actuator faults and uncertain functions of the designed MAS, a neural network fault-tolerant control scheme is developed. Moreover, an adaptive backstepping controller is developed to solve the non-affine appearance in multiagent stochastic non-affine systems using the mean value theorem. Being different from the existing control methods, the developed adaptive fixed-time control approach can ensure that the outputs of all followers track the reference signal synchronously in the fixed time, and all signals of the controlled system are semi-globally uniformly fixed-time stable. The simulation results confirm that the presented control strategy is effective in achieving control goals.

This article investigates the problem of finite-frequency fault estimation (FE) and adaptive event-triggered fault-tolerant consensus for linear parameter-varying multiagent systems. A polytopic parameter-varying framework is introduced to represent the dynamics of each agent with internal model perturbation and parameter uncertainties. In order to reduce the conservatism brought by full-frequency domain approaches, the finite-frequency technique is employed to design a FE observer that can estimate the magnitude of faults. To eliminate/reduce the impact of faults on system performance, an adaptive event-triggered fault-tolerant consensus controller is then developed, which adjusts the consensus protocol based on the FE information. With the developed distributed fault-tolerant protocol and adaptive event-triggered control scheme, the agents can reach consensus in the presence of system faults and the transmission of unnecessary information in the control channels is avoided. The proposed triggering scheme offers certain advantages over existing results in balancing desired consensus performance and improving network utilization. By constructing a parameter-dependent Lyapunov function, a sufficient condition for designing the consensus controller gain and the adjustment matrix can be derived in the form of linear matrix inequality. Finally, two simulation examples are included to illustrate the effectiveness of the obtained theoretical results.

This study focuses on reachability problems in differential games. An improved level set (LS) method for computing reachable tubes (RTs) is proposed in this article. The RT is described as a sub-LS of a value function, which is the viscosity solution of a Hamilton–Jacobi (HJ) equation with running cost. We generalize the concept of RTs and propose a new class of RTs, which are referred to as cost-limited one. In particular, a performance index can be specified for the system, and A set of initial states of the system’s evolutions that can reach the target set before the performance index grows to a given allowable cost is referred to as a cost-limited RT (CRT). Such an RT can be obtained by specifying the corresponding running cost function for the HJ equation. Different nonzero sub-LSs of the viscosity solution of the HJ equation at a certain time point can be used to characterize the CRTs with different allowable costs (or the RTs with different time horizons), thus reducing the storage space consumption. The validity and accuracy of the suggested technique are demonstrated via some examples.

Players-based emotion recognition can help the understanding game players’ emotional states, contributing to the improvement of the game's quality and value. This article develops a hybrid neural network learning framework called convolutional smooth feedback fuzzy network (CSFFN) to detect a player's emotional states in real-time during a gaming process based on electroencephalogram (EEG) signals. Specifically, CSFFN rationally combines a convolutional neural network (CNN), a fuzzy neural network (FNN), and a recurrent neural network (RNN). CNN not only captures spatial characteristics between EEG signals from different channels but also eliminates noise from EEG signals, improving the accuracy and anti-noise performance in game emotion recognition. FNN extracts the membership degree of a player's different emotional states, further improving the emotion recognition accuracy. Since a player's current emotional state is influenced by the previous emotional states during the game process, RNN is employed to capture the temporal characteristics of EEG signals, better improving the emotion recognition accuracy. Experimental results show that CSFFN has higher recognition accuracy and noise resistance in identifying four emotional states (happiness, sadness, superiority, and anger) compared to support vector machine (SVM) with different kernels, linear discrimination analysis (LDA), AlexNet, and VGG16 methods.

This paper focuses on the problem of distributed fault detection and leader-following output consensus for heterogeneous multiagent systems subject to deception attacks. During the information exchange and dissemination, a malicious attacker can make full use of specialized computer technology and launch stochastic deception attacks against some vulnerable agents over the network. The attack signals in actual operation tend to be energy-constrained, and Bernoulli distribution can be used to describe the random features. Taking the attack information into account, the distributed fault detection observer and the dynamic consensus compensator are designed in two separate steps. In order to reduce unnecessary information transmission, a dynamic event-triggered mechanism with output-dependent threshold is introduced to the adjustment of consensus protocol. According to Lyapunov stability theory and linear matrix inequality (LMI) techniques, sufficient conditions are derived for developing the model gains of the observer and the compensator. Finally, a simulation example of RLC circuit systems is provided to illustrate the effectiveness of the obtained theoretical results.

Existing driving fatigue detection methods rarely consider how to effectively fuse the advantages of the electroencephalogram (EEG) and electrocardiogram (ECG) signals to enhance detection performance under noise conditions. To address the issues, this article proposes a new type of the deep learning (DL) framework based on EEG and ECG called the product fuzzy convolutional network (PFCN). It should be noted that this article first investigates how to fuse EEG and ECG signals to deal with driving fatigue detection under noise conditions in both simulated and real-field driving environments. Specifically, the PFCN includes three subnetworks. The first uses a fuzzy neural network (FNN) with feedback and a product layer, effectively capturing the particularity and temporal variation of high-dimensional EEG signals and reducing the time-space complexity. The second subnetwork uses a 1-D convolution to convert the ECG data into feature sequences, providing high accuracy and low computational complexity in ECG data classification. The third subnetwork proposes a fusion-separation mechanism to effectively fuse the extracted ECG and EEG features, suppressing the noise interference and ensuring higher detection accuracy. To evaluate the performance of PFCN, a series of experiments has been set up in both simulated and real-field driving environments. The results indicate that the proposed PFCN model has better robustness and detection accuracy compared with several mainstream fatigue detection models.

In this article, we consider the input-to-state stability (ISS) problem for a class of time-delay systems with intermittent large delays, which may cause the invalidation of traditional delay-dependent stability criteria. The topic of this article features that it proposes a novel kind of stability criterion for time-delay systems, which is delay dependent if the time delay is smaller than a prescribed allowable size. While if the time delay is larger than the allowable size, the ISS can be preserved as well provided that the large-delay periods satisfy the kind of duration condition. Different from existing results on similar topics, we present the main result based on a unified Lyapunov-Krasovskii function (LKF). In this way, the frequency restriction can be removed and the analysis complexity can be simplified. A numerical example is provided to verify the proposed results.

This research addresses the finite-time control problem for nonaffine stochastic nonlinear systems with actuator faults and input saturation. Specifically, a new finite-time control scheme is constructed based on the adaptive backstepping framework, with the usage of a state observer and taking advantage of the universal approximation capability of the fuzzy-logic system (FLS). The novelty of this work is that it considers the output feedback problem of a completely nonaffine stochastic system and incorporates the idea of the dynamic surface control (DSC) design. By using the Lyapunov stability theory, all the signals of the controlled system can be semiglobal finite-time stable in probability (SGFSP) while the system is imposed with multiple actuator constraints. In the meantime, the problem of "complexity explosion" is avoided. Two simulation examples are given to demonstrate the validity of the presented strategy.

In order to help robots understand and perceive an object's properties during noncontact robot-object interaction, this article proposes a deeply supervised subspace learning method. In contrast to previous work, it takes the advantages of low noise and fast response of noncontact sensors and extracts novel contactless feature information to retrieve cross-modal information, so as to estimate and infer material properties of known as well as unknown objects. Specifically, a depth-supervised subspace cross-modal material retrieval model is trained to learn a common low-dimensional feature representation to capture the clustering structure among different modal features of the same class of objects. Meanwhile, all of unknown objects are accurately perceived by an energy-based model, which forces an unlabeled novel object's features to be mapped beyond the common low-dimensional features. The experimental results show that our approach is effective in comparison with other advanced methods.

Connectionist artificial intelligence (AI) can power many critical tasks for connected and autonomous vehicles (CAVs). However, connectionist AI lacks interpretability and usually needs large amount of data for learning. A Markov logic network (MLN), which combines first-order logic (FOL) with statistical learning, learns weighted FOL formulas for inference. MLNs can incorporate domain expert knowledge in the form of FOL formulas to achieve data-efficient learning and transparent decision process. In this paper, we propose a hybrid driving decision-making system, which integrates a MLN module and a deep Q-network (DQN) for enhanced driving safety. The MLN module evaluates the safety of ranked actions from DQN to reduce potential collisions. A collective MLN (Co-MLN) learning algorithm is proposed and it enables CAVs collectively learn a global MLN model for safe state transitions, given distributed small amount of noisy data. A hybrid DQN-MLN learning algorithm is also developed for CAVs to collectively learn to drive in new driving environments. Simulations performed using a highway driving simulator show that the proposed Co-MLN algorithm is highly data-efficient and the learned hybrid driving system can effectively reduce collisions. In addition, the learned MLN module provides transparency for safety-critical driving decisions.

In order to enable intelligent robots to recognize unknown objects as accurately as human beings, object perception research is of great significance in service and industrial robot application scenarios. However, object perception using spectral measurements under few-shot learning usually leads to a poor result because of inadequate training samples. To overcome this problem, this work proposes a novel few-shot learning with coupled dictionary learning (FSL-CDL) framework. First, a hybrid feature fusion method is developed to extract the multiple dimension-reduced features of original spectral measurements to build the hybrid features. Then, based on the hybrid features, a multitask coupled learning method is developed to effectively recognize unknown objects under few-shot learning. In this method, two coupling patterns, i.e., interspectroscopy coupling and intraspectroscopy coupling, effectively bridge the gap between two spectral measurements. Finally, the proposed FSL-CDL is compared with other advanced algorithms on the SMM50 dataset, and reaches 97.5% and 98.4% recognition accuracy under one-shot and five-shot learning, respectively, which are better than other algorithms. Besides, FSL-CDL can be extended to other perception tasks which contains multiple heterogeneous measurements.

This article investigates the stabilization control and stabilizing data-rate condition problems for networked control systems, which transmit signals from the sensor to the controller over the communication network with denial-of-service (DoS) attacks. Considering a class of DoS attacks that only constrain its frequency and duration, we aim to explore the constraint condition for stabilization and minimum stabilizing data rate of the networked control systems. The framework consists of two main parts. The first part considers the stabilizing control by the state-feedback approach under ideal bandwidth capacity. While the second part characterizes the average stabilizing data rate in terms of the eigenvalues of system matrix and DoS constraint functions to explicitly reveal the relationship between the attacks and the network bandwidth capacity. The stabilizing result is novel in the sense that the DoS-attack intensity, which is characterized by its frequency and duration, can vary for different time intervals. With this feature, the minimum average data-rate condition can vary for different time intervals according to the intensity of DoS attacks.

In this article, finite-time-prescribed performance-based adaptive fuzzy control is considered for a class of strict-feedback systems in the presence of actuator faults and dynamic disturbances. To deal with the difficulties associated with the actuator faults and external disturbance, an adaptive fuzzy fault-tolerant control strategy is introduced. Different from the existing controller design methods, a modified performance function, which is called the finite-time performance function (FTPF), is presented. It is proved that the presented controller can ensure all the signals of the closed-loop system are bounded and the tracking error converges to a predetermined region in finite time. The effectiveness of the presented control scheme is verified through the simulation results.

This article presents a fuzzy finite-time command filtering output feedback control method for a class of nonlinear systems. A fast convergent output feedback control algorithm based on backstepping finite-time command filtering is developed. Fuzzy logic system is used to estimate uncertain functions in nonlinear systems. A fuzzy state observer is designed to measure the unknown state. The developed finite-time command filtering feedback control method overcomes well the computational complexity problem due to the calculation of the derivatives of virtual control signals. A compensation mechanism is also introduced to compensate for the error caused by the filter. The proposed method ensures not only that all signals in the closed-loop system are finite-time bound, but also that the tracking error converges to a small neighborhood around the origin. The effectiveness of the proposed method is demonstrated in the simulation results.

Most traditional methods for extracting key points from the 3D point cloud are based on the geometric features of points and they pose problems such as low accuracy. In order to solve these problems, this paper proposes a novel approach based on 2D image mapping, making it able to achieve highly accurate localization of key points. Specifically, it works as follows: input images are first selected for Harris corner detection; the three pairs of marker points of the images and the point cloud are then selected to calculate the transformation matrix T between them; next, the image key points are mapped onto the three-dimensional points through the transformation matrix T, for which the extraction of key points is achieved. Experimental results show that the proposed algorithm is able to greatly improve the extraction accuracy of key points in comparison with traditional algorithms.

Point cloud completion aims to infer the complete point clouds from incomplete ones, which is used in remote sensing applications such as reconstructing and autonomous driving. However, most existing methods cannot recover accurate structure details of the object. In this paper, we propose point shift network (PS-Net). Our main contributions lie in the following three-folds. First, we propose a multi-resolution encoder, which extracts and fuses multi-resolution point cloud features hierarchically, thus avoiding information loss caused by a single global feature. Second, we design a multi-resolution point cloud generation structure, which can be combined with the multi-resolution encoder to generate gradually dense point clouds, avoiding the problem of non-uniformly density of the single-layer decoder. Third, we design the shift network, which is used to generate shift vectors to shift the coordinates of each point cloud, so as to further finetune the coordinate positions of point clouds, achieving more accurate prediction. We conduct comprehensive experiments on ShapeNet, KITTI, ScanObjectNN, and ModelNet40 datasets, which demonstrate that the proposed PS-Net achieves better performance than existing methods and verify the robustness of the proposed method. This paper contributes a new method to point cloud completion, realizes fine point cloud shape completion, and brings new possibilities to the research of autonomous driving, registration, and reconstruction.

Existing visual-based fatigue detection methods usually monitor drivers’ fatigue by capturing their facial features, including eyelid movements, yawn frequency and head pose. However, these approaches typically do not take drivers’ biological signals into consideration. An accurate model for fatigue detection requires combining both facial behavior and biological data. This paper proposes a novel non-intrusive method for driver multimodal fusion fatigue detection by extracting eyelid features and heart rate signals from the RGB video. The multimodal feature fusion method could significantly increase the accuracy of fatigue detection. Specifically, we established two fatigue detection models based on heart rate and the PERCLOS value respectively with one-dimensional Convolutional Neural Network (1D CNN), where the PERCLOS refers to the percentage of eyelid closure over the pupil. Finally, the outputs of the two models are weighted to achieve the multimodal fusion fatigue detection. Simulation results show that our method yield better performance than traditional methods.

For the purpose of avoiding injury and realizing precise operations, the multilateral teleoperation system is the most efficient way to transport trace toxic or radioactive substances, to perform minimally invasive surgery, etc. It is essential to enhance the transparency of a multilateral teleoperation system including multiple masters and the single slave manipulator. However, there are few researchers focus on the allocation of the contact force of the single slave manipulator to different master manipulators. In this paper, we firstly introduce the concept of force translation for teleoperation systems consisting of dual hand master (left and right hands) manipulators and a single slave manipulator. Force translation reflects how the impedance on the single slave side is translated or allocated to contact forces on different master sides. To maintain the stability of the system and to improve transparency, we elucidate the mechanism of the force translation and analyze the relation among masters and the slave. Furthermore, the force translation mechanism is analyzed through numerical simulations and CHAI 3D virtual physical simulations. It is used to propose a multilateral impedance control, and the Lyapunov function is used to analyze the system stability. The results of numerical simulations and real robot experiments verify the effectiveness of the proposed control methods based on the proposed force translation mechanism.

This paper investigates the tracking control problem of a class of high-order distributed systems subjected to limited communication bandwidth. An event-triggered control method is proposed, where the controller is triggered only when specific events happen. Moreover, the computational complexity is reduced by introducing command filters for virtual control signals. Specifically, the backstepping scheme is adopted as the main design framework, by which the n-th order nonlinear system is divided into n command-cascaded first order subsystems. And virtual control commands are sent through a second-order low-pass filter, by which the time derivatives of the virtual commands can be obtained directly. The theoretical analysis shows the stability of the proposed method. The tracking performance is illustrated by a simulation example.

In this paper, we investigate the adaptive fast finite-time tracking control problem for a class of uncertain nonlinear strict-feedback systems by using backstepping technique and fast finite-time stable theory. Dynamic surface control approach is introduced to reduce the computational complexity because of the repeated differentiation of virtual signals in the traditional backstepping algorithm. By employing the approximation of fuzzy logic systems, a fuzzy-based adaptive fast finite-time output tracking control approach is presented, which can guarantee the convergence of tracking error and the boundedness of all closed-loop signals in the fast finite-time. In the final, the validity of the developed control method is proved by the simulation results.

This paper addresses the synchronization control problem of leader-follower multiagent systems with each follower described by a class of high-order nonlinear multiple-input-multiple-output (MIMO) dynamics in the presence of time delays and actuator faults. A distributed synchronization scheme with guaranteed synchronization performance based on the radial basis function neural network (RBF NN) is introduced. We propose an augmented quadratic Lyapunov function by incorporating the lower bounds of control gain matrices and the actuator healthy indicator, and the problems caused by the unknown time-varying control gain matrices, actuator faults, and coupling terms among agents are solved. Meanwhile, the output of followers can track that of the leader and the steady state, and the transient performance of synchronization can be guaranteed, while all the other signals in the closed-loop system are guaranteed to be bounded. Finally, numerical analysis has been carried out to verify the effectiveness of the proposed controller.

Driver fatigue monitoring is very important for driving safety, and many intricate factors while driving make fatigue monitoring harder. To effectively predict driving fatigue, this article proposes a new deep learning framework called TSK-type convolution recurrent fuzzy network (TCRFN) based on the spatial and temporal characteristics of electroencephalogram (EEG) signals. In TCRFN, the convolution block is first introduced to extract spatial dependencies from EEG signals. Furthermore, since EEG noise has a strong spatial dependence, this convolutional neural networks is used to reduce the impact of noise. Additionally, a new local feedback method in fuzzy neural network is proposed to process the EEG signals, which can better capture the temporal dependencies from EEG signals. Finally, a logarithmic spatial firing layer function is used in the proposed TCRFN. The activation performance of this function is smoother, which allows more feature numbers and provides better prediction. The experimental results show that the proposed TCRFN model has better antinoise performance and prediction accuracy compared with other widely used and state-of-the-art models.

Driving fatigue can be detected by measuring drivers' heart rate with a wearable device or extracting their facial features with an RGB camera. However, a wearable device causes inconvenience and discomfort to the driver, and an RGB camera's detection accuracy may be affected by light, glasses, and head orientation. Furthermore, most existing methods ignored the temporal information of fatigue features and the relationship between the features, lowering recognition accuracy. Additionally, some existing fatigue detection methods focused on dealing with fatigue features with a temporal slice, ignoring temporal variations in the features. To address these problems, a single RGB-D camera is first used to extract three fatigue features: heart rate, eye openness level, and mouth openness level. More importantly, this paper proposes a novel multimodal fusion recurrent neural network (MFRNN), integrating the three features to improve the accuracy of driver fatigue detection. Specifically, a recurrent neural network (RNN) layer is applied in the MFRNN to obtain the temporal information of the features. Since the heart rate feature is a physiological signal extracted indirectly, it contains more noise and is fuzzier than the other features. To deal with the fuzziness and noise, we combine fuzzy reasoning with RNN to extract the temporal information of the heart rate. To identify the relationship between the features, we develop a new relationship layer containing a two-level RNN, for which the input is the temporal information of the features. Both the simulation and field experiment results show that the proposed method provides better performance than similar methods.

This paper addresses the finite-time tracking control problem for a class of nonaffine nonlinear systems with unknown dead zones using an adaptive fuzzy control scheme. The unknown nonlinear functions of the system are approximated by the fuzzy logic systems and a finite-time stability theorem is used to construct the control signal. The novelty of the proposed control method is that the tracking system can reach the stable equilibrium within a finite period of time without the knowledge of the boundaries of the dead zone parameters. The simulation results show that the system is semi-global practical finite-time stable and the tracking deviation converges to a small neighborhood of zero in a finite period of time.

In surgical simulation, the Navier-Stokes (N-S) equation is commonly employed to imitate the physical characteristics of bleeding and the smooth particle hydrodynamics (SPHs) algorithm is applied to solve the numerical solution of the N-S equation. However, blood is viscous, incompressible and non-Newtonian fluid whose physical properties cannot be fully incorporated by the simple N-S equation, and the kernel approximation of the SPH algorithm may lead to both edge and volume distortions plus high computational cost. In this paper, both the tension force and the effect of platelets on the viscous force of bleeding particles are incorporated into the N-S equation in order to render more realistic visual effect and biological features of bleeding in surgical simulation. Constant core radius of the kernel function of the SPH algorithm is substituted with a function of particle density, avoiding potential edge distortions in simulating bleeding area. A repulsive force between particles is introduced, which effectively prevents volume distortions. Besides, accelerated search for particles based on the cube mesh improves the computational efficiency. The simulation results show that the presented simulation method leads to smooth bleeding surface and improves the visual effects of edge and volume in comparison with existing methods, and relatively high computational efficiency can be achieved as well.

This paper addresses the trajectory tracking control problem of a class of nonstrict-feedback nonlinear systems with the actuator faults. The functional relationship in the affine form between the nonlinear functions with whole state and error variables is established by using the structure consistency of intermediate control signals and the variable-partition technique. The fuzzy control and adaptive backstepping schemes are applied to construct an improved fault-tolerant controller without requiring the specific knowledge of control gains and actuator faults, including both stuck constant value and loss of effectiveness. The proposed fault-tolerant controller ensures that all signals in the closed-loop system are semiglobally practically finite-time stable and the tracking error remains in a small neighborhood of the origin after a finite period of time. The developed control method is verified through two numerical examples.

Traditional human-robot interfaces usually have limitations in accuracy and/or operational space. This article proposes a natural human-robot interface using an adaptive tracking method, which can effectively expand the operational space while ensuring high accuracy. The natural interface allows the robot to directly reproduce the user's hand movement, making the interaction more intuitive and natural. The leap motion is fixed on the Cartesian platform to capture the movement of the user's hand. Because the Cartesian platform follows the hand and keeps the hand in the center of the detection area, the measurement accuracy is improved and the measurement space can be extended. During the process of acquiring gesture data, the measurement errors were found to increase over time because of the inherent noise of the sensor. To deal with this problem, the unscented Kalman filter is applied to estimate the position of the hand. Moreover, an adaptive velocity control method is proposed to improve the operation accuracy and reduce the task execution time with the consideration of users' habits and easiness of usage. The effectiveness of this interface is verified by a series of experiments, and the results show that the proposed interface can be used by nonprofessional users for object operation tasks and can provide users with superior interactive experiences.

In this article, a novel human-machine interface, in which two Leap Motion (LM) controllers and a coil are attached to a Cartesian platform to provide contactless electromagnetic force feedback for enhancing the accuracy and efficiency of human-robot manipulation tasks is presented. To implement such an interface, an interval Kalman filter, an improved particle filter, and a mean filter are integrated to estimate accurately the position and orientation of the hand gesture tracked by the two LM controllers, and to smoothen the movement of the Cartesian platform. The back propagation neural network is employed to regulate the electric currents of the coil attached to the Cartesian platform for accurate force feedback. A series of comparative experiments are performed, and the results show that the presented interface greatly improved the efficiency and accuracy of human-robot manipulation tasks in comparison with existing methods, indicating its great potentials for many industry scenarios.

This paper deals with the synchronization control problem in the leader-follower format of a class of high-order nonaffine nonlinear multiagent systems under a directed communication protocol. A novel adaptive neural distributed synchronization scheme with guaranteed performance is proposed. The main contribution lies in the fact that both nonaffine agent dynamics, which basically makes most existing agent dynamics as special cases, and guaranteed synchronization performance are taken into account. The difficulty lies mainly in the nonaffine terms and coupling terms due to the interactions of agents. To overcome this challenge, an augmented quadratic Lyapunov function by incorporating the lower bounds of control gains is proposed. The problems resulting from the nonaffine dynamics and the coupling terms among agents are solved by incorporating the special property of radial basis function neural network into the derivative of the augmented quadratic Lyapunov function. The unknown nonaffine terms are addressed by using an indirected neural network approach. A nonlinear mapping is built to relate the local consensus error to a new one, which is subsequently stabilized via Lyapunov synthesis. As a result, the proposed approach can ensure the outputs of all follower agents to track the outputs of the leader, while the synchronization performance bounds can be quantified on both transient and steady-state stages. All other signals in the closed loop are ensured to be semiglobally, uniformly, and ultimately bounded. Finally, the effectiveness of the proposed controller is verified through a heterogeneous four-agent example.

This paper addresses the problem of adaptive neural output-feedback decentralized control for a class of strongly interconnected nonlinear systems suffering stochastic disturbances. An state observer is designed to approximate the unmeasurable state signals. Using the approximation capability of radial basis function neural networks (NNs) and employing classic adaptive control strategy, an observer-based adaptive backstepping decentralized controller is developed. In the control design process, NNs are applied to model the uncertain nonlinear functions, and adaptive control and backstepping are combined to construct the controller. The developed control scheme can guarantee that all signals in the closed-loop systems are semiglobally uniformly ultimately bounded in fourth-moment. The simulation results demonstrate the effectiveness of the presented control scheme.

This paper is concerned with the trajectory tracking control problem of a class of nonaffine stochastic nonlinear switched systems with the nonlower triangular form under arbitrary switching. Fuzzy systems are employed to tackle the problem from packaged unknown nonlinearities, and the backstepping and robust adaptive control techniques are applied to design the controller by adopting the structural characteristics of fuzzy systems and the common Lyapunov function approach. By using Lyapunov stability theory, the semiglobally uniformly ultimate boundness in the fourth-moment of all closed-loop signals is guaranteed, and the system output is ensured to converge to a small neighborhood of the given trajectory. The main advantages of this paper lie in the fact that both the completely nonaffine form and nonlower triangular structure are taken into account for the controlled systems, and the increasing property of whole state functions is removed by using the structural characteristics of fuzzy systems. The developed control method is verified through a numerical example.

The state-feedback decentralized stabilization problem is considered for interconnected nonlinear systems in the presence of unmodeled dynamics. The functional relationship in affine form between the strong interconnected functions and error signals is established, which makes backstepping-based fuzzy control successfully generalized to strong interconnected nonlinear systems. By combining adaptive control with both backstepping design and the approximation property of fuzzy systems, an adaptive decentralized control algorithm is developed. It is demonstrated by both theoretical analysis and simulation study that the proposed control strategy ensures semiglobally uniformly ultimately bounded of all signals within the closed-loop systems.

An integrated model-based control and scheduling scheme is proposed for the load frequency control (LFC) of large-scale power systems under the distributed structure and uncertainties. Specifically, the limited bandwidth constraint is considered when state observation is exchanged over shared communication networks. Each area controller uses the explicit models of its own and neighboring areas to predict state observations when the actual one is not available. At each transmission instant, the state observation of the scheduled area is broadcasted to the relevant areas and the model-based controllers are partially updated. By properly scheduling the transmission sequence and intervals, the stability of the power system can be guaranteed with a substantial reduction of the bandwidth usage and this is proven by performing a thorough theoretical analysis. Simulation results of a four-area power system verify that the proposed distributed model-based control scheme integrated with a proper scheduling strategy can greatly enhance the performance and the resiliency to parameter uncertainty in large-scale power systems.

This paper presents the development of an adaptive neural controller for a class of nonlinear systems with unmodeled dynamics and immeasurable states. An observer is designed to estimate system states. The structure consistency of virtual control signals and the variable partition technique are combined to overcome the difficulties appearing in a nonlower triangular form. An adaptive neural output-feedback controller is developed based on the backstepping technique and the universal approximation property of the radial basis function (RBF) neural networks. By using the Lyapunov stability analysis, the semiglobally and uniformly ultimate boundedness of all signals within the closed-loop system is guaranteed. The simulation results show that the controlled system converges quickly, and all the signals are bounded. This paper is novel at least in the two aspects: 1) an output-feedback control strategy is developed for a class of nonlower triangular nonlinear systems with unmodeled dynamics and 2) the nonlinear disturbances and their bounds are the functions of all states, which is in a more general form than existing results.

This paper presents an adaptive neural control approach for nonstrict-feedback nonlinear systems in presence of unmodeled dynamics, unknown control directions and input dead-zone nonlinearity. To handle the difficulty due to uncertain control directions, Nussbaum gain functions are applied. Based on the structural characteristic of radial basis function neural networks, a backstepping-based adaptive neural control algorithm is developed. The main contributions of this paper lie in the fact that a backstepping-based neural control algorithm is developed for nonstrict-feedback nonlinear systems with unmodeled dynamics, unknown control directions and actuator dead-zone, and the total number of adaptive laws is not greater than the order of control system. As a beneficial result, the controller is much easier to be implemented in practice with less computational burden. A simulation example is given to reveal the viability of the presented approach. It is demonstrated by both theoretical analysis and simulation study that the presented control strategy ensures the semiglobally uniform ultimate boundedness of all closed-loop system signals.

Consensus based distributed robust adaptive finite-time secondary voltage control is designed for inverter-based islanded AC microgrids. The design combines decentralized local states information with the states of the neighboring distributed generators with directed communication topology. Robust control algorithms are used locally for each distributed generator to deal with uncertainty. Lyapunov and terminal sliding mode theory uses to guarantee that the proposed distributed control design can restore voltage to the reference value in finite-time. Analysis shows that the finite-time robust consensus can force the voltage of the distributed generators to reach the designed terminal sliding surface in finite-time and remain there. The proposed distributed secondary controller does not require a priori knowledge of the nonlinear dynamical model and uncertainty associated with microgrids.

In this paper, we develop haptic based force reflecting interaction interface for bilateral telemanipulation of miniature aerial vehicle. The human-master interface combines shared control term with the reflected force fields mapped by using artificial force field and virtual impedance force field. The shared control for the human-master comprises velocity signals of the with the scaled position of the master haptic manipulator. The shared input interface for the slave-flying environment is developed by combining scaled position of the master manipulator with the velocity of the remote MAV. The data transmission between ground station and remote vehicle are carried out by open internet communication network. Evaluation results on a qudrotor MAV system are presented to demonstrate the effectiveness for real-time applications.

In this paper, we investigate distributed consensus problems for multiple miniature aerial vehicles (MAVs) with nonlinear dynamics and uncertainty. We develop distributed consensus protocol to solve regulation synchronization problem for leaderless MAVs with directed interaction topology. Adaptive control algorithms are used locally for each vehicle to deal with nonlinear dynamics and uncertainty associated with flying environment, such as, wind gust, payload mass, aerodynamic friction and other external disturbances. The resulting protocol for synchronization problem combines simple decentralized proportional-plus-derivative like term and robust adaptive control term with position signal based consensus protocol. Lyapunov method uses to show the asymptotic convergence of the consensus errors of the closed loop systems formulated by multiple MAVs. It is shown in our analysis that all MAVs reach an agreement and synchronize to a common value which is not a priori defined. The convergence of the asymptotic consensus error is shown by using Lyapunov method and sliding mode control theory. The proposed design is simple as it does not require exact knowledge of the dynamical model and uncertainty.

This paper investigates the uncertainty of the day-ahead distribution system scheduling considering the random variations of both Photovoltaic-based distributed generator (PV-DG) output power and load. Instead of Monte-Carlo simulation (MCS), a two-point estimation method (2PEM) is applied to obtain accurate and computation-efficient analysis results. Based on the two-year real-world hourly weather and load data in the city of Ottawa, the estimation accuracy of the 2PEM has been verified in an equivalent 44 kV distribution feeder system. In terms of computational efficiency, the 2PEM can significantly reduce the computation burden with comparison to MCS. By using the 2PEM, the impact of PV-DG output power and load variations on the uncertainty of the distribution system scheduling under different seasons is thoroughly studied. The analytical results show that the range of standard deviation of optimally scheduled DG generation for this distribution feeder system is larger in summer than that in winter.

While the RGB2GRAY conversion with fixed parameters is a classical and widely used tool for image decolorization, recent studies showed that adapting weighting parameters in a two-order multivariance polynomial model has great potential to improve the conversion ability. In this paper, by viewing the two-order model as the sum of three subspaces, it is observed that the first subspace in the two-order model has the dominating importance and the second and the third subspace can be seen as refinement. Therefore, we present a semiparametric strategy to take advantage of both the RGB2GRAY and the two-order models. In the proposed method, the RGB2GRAY result on the first subspace is treated as an immediate grayed image, and then the parameters in the second and the third subspace are optimized. Experimental results show that the proposed approach is comparable to other state-of-the-art algorithms in both quantitative evaluation and visual quality, especially for images with abundant colors and patterns. This algorithm also exhibits good resistance to noise. In addition, instead of the color contrast preserving ratio using the first-order gradient for decolorization quality metric, the color contrast correlation preserving ratio utilizing the second-order gradient is calculated as a new perceptual quality metric.

This paper considers the master and slave synchronization control for bilateral teleoperation systems with time delay and backlash-like hysteresis. Based on radial basis functions neural networks' approximation capabilities, two improved adaptive neural control approaches are developed. By Lyapunov stability analysis, the position and velocity tracking errors are guaranteed to converge to a small neighborhood of the origin. The contributions of this paper can be summarized as follows: 1) by using the matrix norm established using the weight vector of neural networks as the estimated parameters, two novel control schemes are developed and 2) the hysteresis inverse is not required in the proposed controllers. The simulations are performed, and the results show the effectiveness of the proposed method.

This paper is concerned with the observer-based fuzzy output-feedback control for stochastic nonlinear multiple time-delay systems. On the basis of the consistent form of virtual input signals and increasing characteristics of the system upper bound functions, a variable splitting technique is employed to surmount the difficulty occurred in the nonlower-triangular form. In the controller design procedure, a state observer is first designed, and then an adaptive fuzzy output-feedback control method is presented by combining backstepping design together with fuzzy systems' universal approximation capability. The proposed adaptive controller guarantees the semi-global boundedness of closed-loop system trajectories in terms of fourth-moment. Two simulation examples are displayed to demonstrate the feasibility of the suggested controller.

A novel meshless deformation model of biological soft tissue, which is mainly based on the radial basis function point interpolation, is presented for interactive simulation applications such as virtual surgery simulators. Compared with conventional mesh models, the proposed model is particularly suitable for simulating large deformation, sucking and cutting tasks since there is no need to maintain grid information. Kelvin viscoelasticity, which represents relaxation, creep, and hysteresis of soft tissue, is integrated into the proposed model, making the simulation much more realistic than many existing meshless models. To verify the validity of the proposed model, a biomechanical test was performed on real-life biological tissue and the results show that the maximum relative error between the forces from the biomechanical test and those obtained from the model is less than 5.8%. The proposed model was also implemented on a neurosurgery simulator, which showed that the deformation of the brain tumor can be simulated in a high degree of accuracy with real-time performance. In particular, the error and distortion from the remeshing process inherited in conventional mesh models when deformation is large are avoided.

Adaptive neural intelligent control is investigated for a class of pure-feedback nonlinear time-delay systems with unmodeled dynamics in nonlower-triangular form, which views the lower-triangular structure as a special structure. A variable partition technique is applied to surmount the difficulty in the nonlinear functions of whole state variables. By utilizing the backstepping recursive design approach and the universal approximation capability of neural networks, an adaptive neural controller is systemically designed. Then, based on the utilization of Lyapunov-Krasovskii functionals, the semiglobally uniform boundedness of all closed-loop signals is guaranteed. Finally, the suggested control method is verified through a numerical example. The main advantage of this paper is that an intelligent control method is developed for pure-feedback nonlinear systems with state time delay, unmodeled dynamics and nonlower triangular form. Further developments will focus on how to deal with the problem of output feedback control of pure-feedback nonlinear time-delay systems with unmodeled dynamics and nonlower-triangular structure.

This work investigates the impact of the communication-channel cyber attacks on the dynamic performance of the islanded microgrid secondary frequency control. The cyber-physical system structure of the secondary frequency control is described. The secondary frequency control is introduced. A set of cyber attacks including denial of service (DoS) and false data attacks are then modeled. The Canadian urban benchmark distribution system has been built for testing the impact of cyber attacks. The testing results show that both DoS and false data attacks could result in the performance degradation and even the instable operation of the islanded microgrid.

It is a great pleasure to welcome you to the 2016 IEEE 20thInternational Conference on Computer Supported Cooperative Work in Design (CSCWD 2016), which takes place at Qianhu Hotel, Nanchang, China, from May 4thto 6th, 2016.

This paper presents a novel gradient correlation similarity (Gcs) measure-based decolorization model for faithfully preserving the appearance of the original color image. Contrary to the conventional data-fidelity term consisting of gradient error-norm-based measures, the newly defined Gcs measure calculates the summation of the gradient correlation between each channel of the color image and the transformed grayscale image. Two efficient algorithms are developed to solve the proposed model. On one hand, due to the highly nonlinear nature of Gcs measure, a solver consisting of the augmented Lagrangian and alternating direction method is adopted to deal with its approximated linear parametric model. The presented algorithm exhibits excellent iterative convergence and attains superior performance. On the other hand, a discrete searching solver is proposed by determining the solution with the minimum function value from the linear parametric model-induced candidate images. The non-iterative solver has advantages in simplicity and speed with only several simple arithmetic operations, leading to real-time computational speed. In addition, it is very robust with respect to the parameter and candidates. Extensive experiments under a variety of test images and a comprehensive evaluation against existing state-of-the-art methods consistently demonstrate the potential of the proposed model and algorithms.

Due to its great potential to improve the overall performance of data transmission with its dynamic and adaptive spectrum allocation capability in comparison with many other networking technologies, cognitive radio (CR) networking technology has been increasingly employed in networking and communication infrastructures for smart grids. However, a secondary user (SU) of a CR network has to be squeezed out from a channel when a primary user reclaims the channel, which may occur in a randomized fashion. The random interruption of SU traffic may cause packet losses and delays for SU data, and it will in turn affect the stability of the monitoring and control of smart grids. In this paper, we address this problem and investigate the modeling and stability analysis of the automatic generation control (AGC) of a smart grid for which CR networks are used as the infrastructure for the aggregation and communication of both system-wide information and local measurement data. For this purpose, a randomly switched power system model is proposed for the AGC of the smart grid. By modeling the CR network as an On–Off switch with sojourn times, the stability of the AGC of the smart grid is analyzed. In particular, we investigate the smart grid with two main types of CR networks: 1) the sojourn times are arbitrary but bounded and 2) the sojourn times follow an independent and identical distribution process. The sufficient conditions are obtained for the stability of the AGC of the smart grid with these two CR networks, respectively. Simulation results show the effects of the CR networks on the dynamic performance of the AGC of the smart grid and illustrate the usefulness of the developed sufficient conditions in the design of CR networks in order to ensure the stability of the AGC of the smart grid.

This paper presents an algorithm for robotic self-localization implemented on a context-aware robotic system. The self-localization algorithm is developed using the Particle Filtering (PF) method, with the enhancement from the technique of adaptive multi-model and entropy-based active sensing. The proposed solution is then utilized in the scenarios of robotic self-localization on a mobile robot platform. The feasibility and effectiveness of the adaptive multi-model and entropy based self-localization method is demonstrated in the experimental results.

Unsupervised image segmentation is a fundamental but challenging problem in computer vision. In this paper, we propose a novel unsupervised segmentation algorithm, which could find diverse applications in pattern recognition, particularly in computer vision. The algorithm, named Two-stage Fuzzy c-means Hybrid Approach (TFHA), adaptively clusters image pixels according to their multichannel Gabor responses taken at multiple scales and orientations. In the first stage, the fuzzy c-means (FCM) algorithm is applied for intelligent estimation of centroid number and initialization of cluster centroids, which endows the novel segmentation algorithm with adaptivity. To improve the efficiency of the algorithm, we utilize the Gray Level Co-occurrence Matrix (GLCM) feature extracted at the hyperpixel level instead of the pixel level to estimate centroid number and hyperpixel-cluster memberships, which are used as initialization parameters of the following main clustering stage to reduce the computational cost while keeping the segmentation performance in terms of accuracy close to original one. Then, in the second stage, the FCM algorithm is utilized again at the pixel level to improve the compactness of the clusters forming final homogeneous regions. To examine the performance of the proposed algorithm, extensive experiments were conducted and experimental results show that the proposed algorithm has a very effective segmentation results and computational behavior, decreases the execution time and increases the quality of segmentation results, compared with the state-of-the-art segmentation methods recently proposed in the literature.

The analysis and prediction of time series data has played an important role for intelligent systems used in the area of cybernetics and human-machine interaction. Time series prediction is especially important in the case of unreliable communication of data acquired by intelligent systems. Computationally efficient kernel based regression algorithms have allowed for the prediction of non-linear relationships within time series data. In this paper, we present the smooth delta corrected kernel least mean square (SDC-KLMS) algorithm. The SDC-KLMS scales in linear time with the number of samples stored, hence making it computationally efficient. We present a theoretical motivation for our algorithm and we experimentally show how our approach overcomes a limitation imposed by the use of a finite storage buffer. Experiments with simulated, benchmark, and real world data were conducted to verify the accuracy of our algorithm.

There is great interest in considering the energy efficiency aspect of wireless cellular networks. When a wireless cellular network is powered by the smart grid, only considering energy efficiency in the cellular network is not enough. In this paper, we consider not only energy-efficient communications but also the dynamics of the smart grid in operating green wireless cellular networks. We formulate the system as a two-level Stackelberg game. A backward induction method is used to analyze the proposed scheme. we prove that the Stackelberg equilibrium (SE) of the proposed game exists and is unique. An iteration algorithm is proposed to obtain the SE. Simulation results show that the smart grid has a significant impact on green wireless cellular networks, and our proposed scheme can significantly reduce operational expenditure and CO2 emissions in green wireless cellular networks.

There is great interest in considering the energy efficiency aspect of wireless cellular networks. Coordinated multipoint (CoMP) communication is a new method that helps with the implementation of dynamic base station coordination for energy efficient communications. On the other hand, the power grid infrastructure is experiencing a significant shift from the traditional electricity grid to the smart grid. In this paper, we consider not only energy efficient communications but also the dynamics of the smart grid in designing green wireless cellular networks. We formulate the system as a Stackelberg game, which has two levels: a cellular network level and a smart grid level. Simulation results show that the smart grid has significant impacts on green wireless cellular networks, and our proposed scheme can significantly reduce operational expenditure and CO2emissions in green wireless cellular networks.

Kernel machines have gained much popularity in applications of machine learning. Support vector machines (SVMs) are a subset of kernel machines and generalize well for classification, regression, and anomaly detection tasks. The training procedure for traditional SVMs involves solving a quadratic programming (QP) problem. The QP problem scales super linearly in computational effort with the number of training samples and is often used for the offline batch processing of data. Kernel machines operate by retaining a subset of observed data during training. The data vectors contained within this subset are referred to as support vectors (SVs). The work presented in this paper introduces a subset selection method for the use of kernel machines in online, changing environments. Our algorithm works by using a stochastic indexing technique when selecting a subset of SVs when computing the kernel expansion. The work described here is novel because it separates the selection of kernel basis functions from the training algorithm used. The subset selection algorithm presented here can be used in conjunction with any online training technique. It is important for online kernel machines to be computationally efficient due to the real-time requirements of online environments. Our algorithm is an important contribution because it scales linearly with the number of training samples and is compatible with current training techniques. Our algorithm outperforms standard techniques in terms of computational efficiency and provides increased recognition accuracy in our experiments. We provide results from experiments using both simulated and real-world data sets to verify our algorithm.

Recently, there is great interest in considering the energy efficiency aspect of cellular networks. On the other hand, the power grid infrastructure, which provides electricity to cellular networks, is experiencing a significant shift from the traditional electricity grid to the smart grid. When a cellular network is powered by the smart grid, only considering energy efficiency in the cellular network may not be enough. In this paper, we consider not only energy-efficient communications but also the dynamics of the smart grid in designing green wireless cellular networks. Specifically, the dynamic operation of cellular base stations depends on the traffic, real-time electricity price, and the pollutant level associated with electricity generation. Coordinated multipoint (CoMP) is used to ensure acceptable service quality in the cells whose base stations have been shut down. The active base stations decide on which retailers to procure electricity from and how much electricity to procure. We formulate the system as a Stackelberg game, which has two levels: a cellular network level and a smart grid level. Simulation results show that the smart grid has significant impacts on green wireless cellular networks, and our proposed scheme can significantly reduce operational expenditure and CO_2 emissions in green wireless cellular networks.

Continuous authentication is an important prevention-based approach to protect high security mobile ad-hoc networks (MANETs). On the other hand, intrusion detection systems (IDSs) are also important in MANETs to effectively identify malicious activities. Considering these two approaches jointly is effective in optimal security design taking into account system security requirements and resource constraints in MANETs. To obtain the optimal scheme of combining continuous authentication and IDSs in a distributed manner, we formulate the problem as a partially observable distributed stochastic system. We present structural results for this problem in order to decrease computational complexity, making our solution usable in large networks. The policies derived from structural results are easy to implement in practical MANETs. Simulation results are presented to show the performance of this method for the proposed scheme.

Continuous user authentication is an important prevention-based approach to protect high security mobile ad-hoc networks (MANETs). On the other hand, intrusion detection systems (IDSs) are also important in MANETs to effectively identify malicious activities. Considering these two approaches jointly is effective in optimal security design taking into account system security requirements and resource constraints in MANETs. To obtain the optimal scheme of combining continuous user authentication and IDSs in a distributed manner, we formulate the problem as a partially observable Markov decision process (POMDP) multi-armed bandit problem. We present a structural results method to solve the problem for a large network with a variety of nodes. The policies derived from structural results are easy to implement in practical MANETs. Simulation results are presented to show the effectiveness and the performance of the proposed scheme.

This paper addresses the stability-analysis problem for teleoperation systems with time delays. Compared with previous work, communication delays are assumed to be both time-varying and asymmetric, which is the case for network-based teleoperation systems. The stability analysis is performed for two classes of controllers: delayed position-error feedback and delayed torque feedback. By choosing Lyapunov-Krasovskii functional, we show that the master-slave teleoperation system is stable under specific linear-matrix-inequality (LMI) conditions. With the given controller-design parameters, the proposed stability criteria can be used to compute the allowable maximal transmission delay. Finally, both simulations and experiments are performed to show the effectiveness of the proposed method.

A multiinnovation least-squares (MILS) identification algorithm is presented for linear regression models with unknown parameter vectors by expanding the innovation length in the traditional recursive least-squares (RLS) algorithm from the viewpoint of innovation modification. Because the proposed MILS algorithm uses p innovations (not only the current innovation but also past innovations) at each iteration (with the integer p > 1 being an innovation length), the accuracy of parameter estimation is improved, compared with that of the RLS algorithm. Performance analysis and simulation results show that the proposed MILS algorithm is consistently convergent. Moreover, a new interval-varying MILS algorithm is proposed, for which the key is to dynamically change the interval in order to deal with cases where some measurement data are missing. Furthermore, an auxiliary-model-based MILS algorithm is derived for pseudolinear models corresponding to output error moving average systems with colored noises. Finally, the proposed algorithms are applied to model an experimental water level control system.

The use of support vector (SV) methods has been successful in many areas involving pattern recognition. Video surveillance requires pattern recognition algorithms that are efficient in their operation, and requires the use of online processing for the detection and identification of events, objects, and behaviours. To successfully use SV methods in video surveillance, on-line training methods must be employed; NORMA is one such training method. A video surveillance system represents a dynamic system with non-stationary characteristics. It is the purpose of our work to enhance NORMA to better adapt to sudden changes (switches) in the surveillance environment. We show that the decision hypothesis that NORMA generates is more accurate when a switch in the data is explicitly detected and managed. Our preliminary testing involves simulated data, real world benchmark data, and real video data captured from a digital camera.

For pseudo-linear regression identification models corresponding output error systems with colored measurement noises, a difficulty of identification is that there exist unknown inner variables and unmeasurable noise terms in the information vector. This paper presents an auxiliary model based multi-innovation stochastic gradient algorithm by using the auxiliary model technique and by expanding the scalar innovation to an innovation vector. Compared with single-innovation stochastic gradient algorithm, the proposed approach can generate highly accurate parameter estimates. The simulation results confirm theoretical findings.

This paper investigates the stability analysis problem of teleoperation system. Compared with previous work, the communication delays are assumed to be both time-varying and unsymmetric. The stability analysis is performed on two classes of controllers: delayed position error feedback and delayed force feedback. By choosing Lyapunov Krasovskii functional, we show that the master-slave teleoperation system is asymptotically stable under specific LMI conditions. With the given controller design parameters, the proposed stability criteria can be used to compute the allowable maximum delay values. Finally, the simulations are performed to show the effectiveness of the proposed method.

In the face of large scale parametric uncertainties, the single model (SM)-based classical adaptive control approach demands high observer, controller and adaptation gains in order to achieve good tracking performance. The well known problem of having high-gain based design is that it amplifies the input and output disturbance as well as excites hidden unmodeled dynamics causing poor tracking performance. In this paper, a multi-model based adaptive design is proposed to reduce the level of parametric uncertainty in order to reduce the observer-controller gains. The key idea of this approach is to allow the parameter estimate of the SM-based classical adaptive control design to be reset into a model that best approximates the plant among a finite set of candidate models. For this purpose, we uniformly distribute the compact set of unknown parameters into a finite number of smaller compact subsets. Then we design a family of candidate controllers for each of these smaller compact subsets. The derivative of the Lyapunov function candidate is used as a resetting criterion to identify a candidate model that closely approximates the plant at each instant of time. The proposed method is evaluated on a 2-DOF robot manipulator to demonstrate the effectiveness of the theoretical development.

Results of experimental studies of a teleoperator system with projection-based force reflection algorithms in the presence of communication constraints are presented. It is demonstrated that, using the projection-based force reflection algorithms, the admissible force reflection gain can be substantially increased without loosing the overall stability, which confirms the earlier theoretical results. It is also shown that this improvement is achieved without transparency deterioration.

In this paper, we propose an adaptive fuzzy output feedback control method for trajectory tracking control problem for robotic systems. Using Lyapunov method, we first develop a stable adaptive fuzzy state feedback control algorithm by assuming that the systems output and its derivatives are available for feedback control design. The algorithm combines fuzzy systems with robust adaptive controller. The fuzzy system approximates the certainty equivalent (CE)-based optimal controller while robustifying adaptive control term is used to cope with uncertainties that appeared from the effect of external disturbance, fuzzy approximation errors and other modeling errors. Then, an output feedback form of the position-velocity (state feedback) controller is proposed where unknown velocity signal is replaced by the output of model-free linear estimator. We show via asymptotic analysis that the tracking performance of the output feedback design can recover the performance achieved under the state feedback control design. Finally, the proposed method is implemented and evaluated on a 2-DOF robotic system to demonstrate the theoretical development for the real-time applications.

In this work, we propose an output feedback sliding mode control (SMC) method for trajectory tracking of robotic manipulators. The design process has two steps. First, we design a stable SMC approach by assuming that all state variables are available for feedback. Then, an output feedback version of this SMC design is presented which incorporates a model-free linear observer to estimate unknown velocity signals. We then show that the tracking performance under output feedback design can asymptotically recover the performance achieved under state feedback based SMC design. A detailed stability analysis is given which shows semi-global uniform ultimately boundedness property of all the closed loop signals. The proposed method is implemented and evaluated on a 2-DOF robotic system to illustrate the effectiveness of the theoretical development.

In this paper, a multi-model based hybrid sliding mode control (HSMC) system is proposed for trajectory tracking control problem of robotic systems. The idea of introducing multi-model/controller based HSMC design is to reduce the level of parametric uncertainty in order to reduce the controller gains that reduces the control effort. The key idea is to allow the parameter estimate of classical sliding mode control (SMC)design to be reset into a model that best approximates the plant among a finite set of candidate models. For this purpose, we uniformly distribute the compact set of unknown parameters into a finite number of smaller compact subsets. Then we design a family of candidate controllers for each of these smaller subsets. The derivative of the Lyapunov function candidate is used as a resetting criterion to identify a candidate model that closely approximates the plant at each instant of time. The proposed method is evaluated on a 2-DOF robot manipulator to demonstrate the effectiveness of the theoretical development.

A general stability result for force-reflecting teleoperator systems with projection-based force reflection algorithms is established. It is shown that the closed-loop system’s gain can be assigned arbitrarily by an appropriate choice of certain weighting function of the projection-based force reflection algorithm. In particular, this allows to achieve stability of the force-reflecting teleoperator system in presence of timevarying irregular delays for arbitrarily large force-reflecting gain and arbitrarily low damping and stiffness of the master. The proposed approach solves, to some extent, the trade-off between stability, manoeuvrability, and high force reflection gain in force-reflecting teleoperator system with network-induced communication constraints.

In wave-variable-based teleoperation systems, the perceived force at the master side is biased due to the nature of wave-variable-based communication. This paper proposes an augmented wave-variable-based approach that can partially cancel the bias portion and improve the fidelity of force feedback significantly. In this approach, the returning wave is augmented by the velocities of the both sides of the communication channel. The steady-state position tracking is not affected by the modification. Passivity of the new teleoperation scheme can be obtained by tuning the bandwidth of a low-pass filter. Hence stability is always achievable. Simulation results demonstrate the effectiveness of the scheme.

In order to reduce computational burden of identification methods for multivariable systems, a hierarchical least squares (HLS) algorithm is developed. The basic idea is to use the hierarchical identification principle to decompose the identification model of the multivariable system into several submodels with smaller dimensions and fewer variables, and then to identify the parameter vector of each submodel. The analysis indicates that the parameter estimation error given by the proposed algorithm converges to zero under the persistent excitation. Also, the algorithm has much less computational efforts than the recursive least squares algorithm and is easy to implement on computer. Finally, we test the proposed algorithm by an example.

In this paper, we derive a mathematical model for dual-rate systems and present a stochastic gradient identification algorithm to estimate the model parameters and an output estimation algorithm to compute the intersample outputs based on the dual-rate input-output data directly. Moreover, we investigate convergence properties of the parameter and intersample estimation, and we test the proposed algorithms with example systems, including an experimental water-level system.

The problem of stable force-reflecting teleoperation is addressed where the communication between the master and the slave is subject to multiple time-varying, discontinuous, and possibly unbounded communication delays. A new force reflection algorithm is proposed which improves the stability of the system without decreasing its transparency. Based on an estimate of the human forces provided by a high-gain input observer, the proposed algorithm restricts the reflected force in such a way that it eliminates the motion of the master induced by the force reflection signal without changing the human perception of the environmental force. It is shown that the force reflection algorithm proposed allows to achieve stability of the system for arbitrarily high force-reflection gain and arbitrarily low damping/stiffness of the master manipulator.

Optical flow is the apparent motion of the brightness pattern in an image. It generally corresponds to the motion field of the captured scene in the image so that we can use it distinguish moving objects. The computation of optical flow is conventionally based on the assumption of uniform brightness, which however does not always hold. Numerous algorithms have been proposed to improve the computation precision of optical flow. In this paper, we propose to incorporate optical flow information into a novel geodesic active contour model for moving-object detection in monocular robots. Specifically, an active contour is formulated by using the level set method, which eliminates the need of a re-initialization procedure. The developed scheme alleviates the effect of optical flow noise, increasing the robustness of the perception of moving objects. The experimental results show that our algorithm can successfully track a moving target, e.g., a human being

The problem of stable force-reflecting teleoperation with time-varying communication delay is addressed. A new force reflection algorithm is presented, where the environmental force reflected on the master side can be altered depending on the forces applied by the human operator. This alteration is not felt by the human operator, however, it makes the force reflection safe in the sense it does not destroy the stability of the teleoperator. In particular, using IOS small gain approach, it is shown that the overall stability in the teleoperator system with the force-reflecting algorithm proposed can be achieved theoretically for arbitrarily low damping on the master side and arbitrarily high force-reflection gain. The simulation results are presented that confirm that the proposed scheme allows to decrease master damping significantly, and thus improve the transparency of force-reflecting teleoperation, without sacrificing the overall stability

The problem of force-reflecting teleoperation over Internet protocol networks is addressed. The existence of time-varying communication delay and the possibility of data losses are taken into consideration. Since significant data loss may result in discontinuity of the reference trajectory transmitted through the communication channel, the proposed control scheme includes a filter that provides a smooth approximation of a possibly discontinuous reference trajectory. The stability of the overall system is guaranteed by a version of the input-to-output stable small-gain theorem for functional differential equations. If the communication delay in the forward channel is an "approximately smooth" function of time, the proposed scheme guarantees that the slave manipulator tracks the delayed trajectory of the master within a prescribed small error.

This paper studies the problem of improving the fidelity of bilateral teleoperators under the stability constraint. First, a new fidelity measure for teleoperators is proposed. Then the teleoperator design problem is formulated as the optimization problem of maximizing the proposed fidelity measure under the stability constraint. The robust stability theory for unstructured uncertainty is applied to analyze the stability of bilateral teleoperators. Third, a new scheme to estimate adaptively the environmental impedance is developed to improve the fidelity of the systems. Finally, a case study is presented to validate the introduced approach.

This paper introduces a centralized approach to data gathering and communication for wireless sensor networks. Inspired by the social behaviors of ants, we clearly partition the work for the base station and sensor nodes according to their different functions and capabilities. A near-optimal chain is achieved by using an ant colony optimization method ruing in the base station. The sensor nodes in the network then form a bi-direction chain structure, which is self-adaptive to any minor changes. The simulation results show that the developed AntChain algorithm performs much better than the LEACH and PEGASIS methods, in terms of energy-efficiency, data integrity and life time, when the base station.

The problem of force-reflecting teleoperation over IP networks is addressed. The existence of time-varying communication delay and possibility of data packets dropouts are taken into consideration. Since significant data dropouts may result in discontinuity of the reference trajectory transmitted through the communication channel, the proposed control scheme includes a filter that provides a smooth approximation of a possibly discontinuous reference trajectory. The stability of the overall system is guaranteed by a version of the IOS small gain theorem for functional-differential equations. It is also shown that, in the case of reliable communication protocols, the proposed scheme guarantees that the slave manipulator tracks the delayed trajectory of the master with a prescribed small error.

Collaborative teleoperation is a new concept to deal with highly complex tasks. It is being made possible by the quick advance in the technologies of Internet, shared virtual environments and robotics. Due to the heterogeneity of the Internet, operators participating in the collaboration from geographically distributed locations might receive feedback from the working site with different time delays, leading to various views of the scene. This asynchronization will make the collaboration difficult and compromise system efficiency. In this paper, we propose a new mechanism to achieve both inter-client and intra-client synchronization in collaborative teleoperation systems. Our approach uses an initial synchronization procedure, buffer management and feedback messages to synchronize data streams sent to geographically distributed operators. The delay bounds of the network do not necessarily need to be known and we use the time stamp in the initial and feedback messages for this purpose.

The images captured by wireless capsule endoscope might have nonlinear spatial distortion, which makes accurate medical examination difficult. So it is a prerequisite to have this distortion corrected. Typically, the correction uses a calibration pattern, which might be a chessboard, dot, grid, or circle pattern. Based on this pattern, enough characteristic samples can be extracted accurately and conveniently, and mathematic model can be built for the distortion in the captured image with respect to the original calibration pattern. Then the correction parameters, including image centers and mapping polynomials, could be found to realize the correction. If the model is too complicated to be accurately built, correction using neural network is a good choice, since it does not rely on the mathematic model of the distortion.

Robotic target tracking has been used in a variety of applications. Due to limited sampling rate, sensory characteristics and processing delays, an important issue in such systems is thus to extrapolate ahead the trajectory (position, orientation, velocity and/or acceleration) of moving targets based on past observations. This paper introduces a novel on-line data-driven fuzzy clustering algorithm that is based on the maximum entropy principle for this particular task. In this algorithm, the fuzzy inference mechanism is extracted automatically from observed data without any human help, which thus eliminates the necessity of expert knowledge and a priori information on moving targets, as required by most traditional techniques. This algorithm does not require training, which enables it to work in a completely on-line fashion. Another important and distinct advantage of the algorithm exists in the fact that it is very fast and efficient in terms of computational cost and thus can be implemented in real time. In the mean time, the introduced algorithm has the ability to adapt quickly to the dynamics of moving targets. All these features make it especially suitable for the task to predict the trajectory of moving targets in robotic tracking. Simulation results show the effectiveness and efficiency of the presented algorithm.

Wireless sensor networks are finding applications in many areas such as coordinated target detection, environment monitoring and border surveillance. The strict requirement on energy efficiency is one of the most significant challenges. In this paper, we develop a novel energy-efficient routing protocol called THEEM (two-hop energy-efficient mesh) for wireless sensor networks. The THEEM protocol employs a two-hop scheme for in-mesh data transmission. A centralized mesh (cluster) formation method is adopted along with other design innovations, such as the concepts of mesh layer/column, power-aware mesh head assignment and a low-energy media access protocol, to achieve energy efficiency. Simulation results show that the THEEM protocol reduces energy consumption quite significantly compared to other similar protocols under the same condition.

Robotic target tracking has been used in a variety of applications. Due to limited sampling rate, sensory characteristics and processing delays, an important issue in such systems is to extrapolate ahead the trajectory (position, orientation, velocity, and/or acceleration) of moving targets from past observations. This paper introduces a novel online data-driven fuzzy clustering algorithm that is based on the Maximum Entropy Principle for this particular task. In this algorithm, the fuzzy inference mechanism is extracted automatically from observed data without human help, which thus eliminates the necessity of expert's knowledge and a priori information on moving targets, as required by most traditional techniques. This algorithm does not require training, which enables it to work in a completely online fashion. Another important and distinct advantage of the algorithm exists in the fact that it is very fast and efficient in terms of computational cost and thus can be implemented in real time. In the meantime, the introduced algorithm is able to adapt quickly to the dynamics of moving targets. All these desired features make it especially suitable for the task to predict the trajectory of moving targets in robotic tracking. Simulation results show the effectiveness and efficiency of the presented algorithm.

In this paper, an embedded fuzzy controller for a nonholonomic mobile robot is developed. The mobile robot was built based on the behavior-based artificial intelligence, where several levels of competences and behaviors are implemented. A class of fuzzy control laws is formulated using the Lyapunov's direct method, which can guarantee the convergence of the steering errors. Theoretical analysis of the fuzzy control algorithms for steering control of the mobile robot is performed. The requirements for a suitable rule base selection in the proposed fuzzy controller are provided, which can guarantee the asymptotical stability of the system. Simulation and experimental studies are conducted to investigate the performance of the proposed fuzzy controller. It can achieve the desired turn angle and make the mobile robot follow the target trajectory satisfactorily.

For Internet-based tele-robotic systems (Internet robots), the most challenging and distinct difficulties are associated with Internet transmission delays, delay jitter and not-guaranteed bandwidth availability, which might lead to dramatic performance degradation or even instability. In this paper, a new approach to dealing with these problems is explored and implemented. Specifically, a rate-based end-to-end transport protocol is developed for real-time data transmission and an adaptive control scheme is developed to control the robot remotely. A mobile robot teleoperation system, ArtBot-I, is developed to verify and test the solutions. In the experiments, the users successfully guided a Pioneer-2 mobile robot through a laboratory environment remotely via the Internet using a web browser.

In this paper, a neural network based torque controller is proposed for real-time collision-free navigation of nonholonomic mobile robots. A torque resulted from the obstacles is incorporated in the control design based on the artificial potential technique, which locally pushes the robot away from the obstacles to avoid collisions. All the needed environment information can be obtained from on-board robot sensors that have limited visibility range only. A torque from a simply single-layer neural network is employed to learn the completely unknown robot dynamics. The system stability is guaranteed by a Lyapunov stability theory. The real-time fine control of mobile robots is achieved through the on-line learning of the neural network. The effectiveness of the proposed controller is demonstrated by simulation studies in both static and dynamic environments.

This paper presents a new visual gesture recognition method for the human-machine interface of mobile robot teleoperation. The interface uses seven static hand gestures, each of which represents an individual control command for the motion control of the remote robot. All the important aspects to develop such an interface are explored, including image acquisition, adaptive object segmentation with color image in RGB, HLS representation, morphological filtering, hand finding and labeling, and recognition with edge codes, template matching, and skeletonizing. By choosing processing methods and procedures properly, a higher ratio of correct recognition and a faster speed are achieved from the experiments.

In this paper we introduce a software and hardware structure for on-line mobile robotic systems. The system hardware configuration mainly consists of a commercially available Pioneer 2 PeopleBot mobile robot, a Sony PTZ video camera and a pair of BreezeNet indoor wireless Ethernet adaptors. The system employs a client-server software architecture in which the client server is insulated from the lower-level details of the mobile robot. This architecture is implemented on the real Internet and the preliminary result is promising. By adopting this modular structure, it will be very easy to construct an experimental platform for the research on diverse teleoperation topics such as remote control algorithms, interface designs, network protocols and applications etc.

Since data packets may get lost somewhere in the Internet connections, for real-time applications such as Internet-based teleoperation, delay boundary prediction plays an important role in determining properly whether a packet is lost or not. The predictors currently employed are lowpass filters based on the autoregressive and moving average (ARMA) models. However, recent studies and the results of the experiments in this paper show that the traditional ARMA model is not suitable because sometimes delays develop with quick and evident variation. In this paper, we present a novel adaptive algorithm for delay boundary prediction based on the maximum entropy principle (MEP). The results of our 3 successive working day experiments on 9 links which consists of academic, commercial and governmental ones among Northern America, Asia and Europe show that the MEP algorithm proposed has a better performance than the traditional ARMA method.

For Internet based teleoperation, the most difficult and distinct part is the unavoidable time-varying delays between human operators and remote robotic devices. Currently, the RTTs (roundtrip time or we can call it delay) are mostly treated as given conditions in application level. In this paper, after a statistical analysis of the huge RTT time series collected densely in a few continuous days using some linear and nonlinear methods, it is found that the RTT time series has a rather high degree of linear correlation between observations. It could be inferred that there is no high nonlinear dependence among observations. Thus, RTT is linearly predictable. We use the MEP (maximum entropy principle) method developed to predict next RTT value (one step ahead) and the results confirm our findings.

Describes two robotic prosthetic eye prototype models. The first model uses an external infrared sensor array mounted on a frame of a pair of eyeglasses to detect natural eye movement and to feed the control system to drive the artificial eye to move with the natural eye. The second model uses human brain EOG (electrooculography) signals picked up by electrodes placed on both sides of a person's head to carry out the same eye movement detection and control tasks as mentioned above. Theoretical issues on sensor failure detection and recovery, and signal processing techniques used in sensor data fusion are studied using statistical methods and artificial neural network based techniques. In addition, practical control system design and implementation using micro controllers are studied and implemented to carry out the natural eye movement detection and artificial robotic eye control tasks.

As the population-aging problem is increasingly pressing on society, and the associated healthcare costs are taking up an incremental percentage of the GNP, various inexpensive support systems for elderly people staying alone at home are becoming very demanding. Fortunately, as the Internet continues to expand exponentially and accesses to the Internet become more prevalent in our daily life, home healthcare systems based on teleoperated mobile robot platform via the Internet become feasible. In this paper, we describe a feasibility study and a basic platform design of a teleoperated home healthcare system via the Internet, which include a literature review of the state of the art in research on this topic, discussions on some open problems and challenges facing researchers in this area and a basic platform design of a home healthcare system via the Internet current under implementation in our research lab.