This paper aims to develop a constant output voltage controller for dynamic wireless power transfer systems(DWPTSs) incorporating sensor noise filtering and fault tolerance. DWPTSs are designed to alleviate range anxiety in electric vehicles(EVs); however, the output voltage fluctuations are their significant drawback compared to static charging mode. Additionally, DWPTSs also face sensor measurement noise and potential faults that exacerbate system instability. To mitigate above challenges, a data-driven fault-tolerant control framework is designed for DWPTS based on a self-learning predictor, which implements constant voltage regulation with enhanced noise and fault immunity. Specifically, a self-learning predictor is integrated into the feedforward loop of a high-gain extended state observer (ESO) to filter sensor noise. Then, a data memory stack is constructed to store predicted states and estimated disturbances, and a concurrent learning algorithm is introduced to recover control gains online. Finally, a composite anti-disturbance control law is implemented to generate the required control signals for the charging circuit. A notable advantage of this scheme is its ability to simultaneously address both sensor noise and faults, ensuring a constant output voltage during EV driving. Experimental results validate that the designed control framework effectively eliminates output voltage fluctuations and measurement noise, even in the presence of sensor faults.

Cross-modal retrieval with 3D models has gained significant attention with the rapid growth of 3D assets. The core challenge lies in learning modality-invariant and discriminative features in a common space. Existing methods often rely on shared class centers in Euclidean space, overlooking directional relationships between samples and non-corresponding centers, while remaining sensitive to modality-specific scales, hindering the learning of discriminative cross-modal centers, especially for dispersed modalities like real-world images. To address these limitations, we propose the Discriminative Cosine Center Learning (DCCL) framework for 3D cross-modal retrieval. DCCL integrates the Adaptive Cosine Center Learning (ACCL) mechanism, optimizing cosine similarity on a shared hypersphere with adaptive penalties for challenging samples. Additionally, the Cross-Modal Affinity Learning (CMAL) mechanism reduces cross-modal discrepancies by pairwise matching data from different modalities. Extensive experiments on five benchmarks demonstrate that DCCL significantly outperforms baseline methods in both synthetic and real-world scenarios.

Point clouds captured by scanning devices are often sparse and incomplete. Most existing methods for point cloud completion use 3D coordinates only to infer geometric shapes, making it difficult to reconstruct accurate structures and details. We propose a novel cross-modal approach, called Multi-Scale Cross-modal Network for Point Cloud Completion (MSC-Net), which leverages the information of image modality to guide the geometric inference of missing parts. In order to obtain more abundant geometric information, we extract multi-scale features of partial point cloud. Then we design a feature fusion module, which employs multi-layer cross-attention to achieve the interaction between the image features and point cloud features at different scales. To improve the ability of local feature perception, we further devise an enhanced cross-attention block. In the decoding stage, we adopt a coarse-to-fine strategy, where the geometry-aware upsampling layer is utilized to refine the point cloud step by step. Experiments and ablation studies have demonstrated the effectiveness of our network, proving that our approach outperforms existing approaches.

Generating motions for robots interacting with objects of various shapes is a complex challenge, further complicated by the robot’s geometry and multiple desired behaviors. While current robot programming tools (such as inverse kinematics, collision avoidance, and manipulation planning) often treat these problems as constrained optimization, many existing solvers focus on specific problem domains or do not exploit geometric constraints effectively. We propose an efficient first-order method, Augmented Lagrangian Spectral Projected Gradient Descent (ALSPG), which leverages geometric projections via Euclidean projections, Minkowski sums, and basis functions. We show that by using geometric constraints rather than full constraints and gradients, ALSPG significantly improves real-time performance. Compared to second-order methods like iLQR, ALSPG remains competitive in the unconstrained case. We validate our method through toy examples and extensive simulations, and demonstrate its effectiveness on a 7-axis Franka robot, a 6-axis P-Rob robot and a 1:10 scale car in real-world experiments. Source codes, experimental data and videos are available on the project webpage: https://sites.google.com/view/alspg-oc

Generating overtaking trajectories in autonomous racing is a challenging task, as the trajectory must satisfy the vehicle’s dynamics and ensure safety and real-time performance running on resource-constrained hardware. This work proposes the Fast and Safe Data-Driven Planner to address this challenge. Sparse Gaussian predictions are introduced to improve both the computational efficiency and accuracy of opponent predictions. Furthermore, the proposed approach employs a bi-level quadratic programming framework to generate an overtaking trajectory leveraging the opponent predictions. The first level uses polynomial fitting to generate a rough trajectory, from which reference states and control inputs are derived for the second level. The second level formulates a model predictive control optimization problem in the Frenet frame, generating a trajectory that satisfies both kinematic feasibility and safety. Experimental results on the F1TENTH platform show that our method outperforms the State-of-the-Art, achieving an 8.93% higher overtaking success rate, allowing the maximum opponent speed, ensuring a smoother ego trajectory, and reducing 74.04% computational time compared to the Predictive Spliner method. The code is available at: https://github.com/ZJU-DDRX/FSDP.

Virtual Reality (VR) creates a highly realistic and controllable simulation environment that can easily manipulate users' perception of space and time. However, while the sensation of “losing track of time” is often associated with enjoyable experiences, both the relationship between time perception and user experience in VR, and the underlying mechanisms of time perception itself, remain largely unexplored. In this study, we first investigated how different zeitgebers—such as light color, music tempo, and VR task—affect time perception. We then introduced the Relative Subjective Time Change (RSTC) method to explore the link between time perception and user experience quantitatively. Furthermore, to uncover the mechanisms underlying time perception in VR, we propose a computational model based on CNN and Transformer, named the Time Perception Modeling Network (TPM-Net), which leverages multimodal physiological data to infer users' time perception states in VR. In a between-subject experiment with 56 participants, our results indicate that the VR task factor significantly influences time perception, with red light and slow-tempo music contributing to an underestimation of time. The RSTC method effectively demonstrates that a relative underestimation of time in VR is strongly associated with enhanced user experience, presence, and engagement. Moreover, the TPM-Net shows great potential in modeling time perception, enabling further inference of relative changes in both time perception and user experience. Our study comprehensively elucidates the mechanisms of time perception in VR. It provides valuable insights and promising methodologies for exploring the relationship between time perception and user experience. Modeling time perception through physiological data marks a first step toward objectively assessing users' temporal perception states, offering a promising tool for VR-based therapy and training systems that require precise temporal awareness.

Point clouds serve as the foundational representation of 3D objects, playing a pivotal role in both computer vision and computer graphics. Recently, the acquisition of point clouds has been effortless because of the development of hardware devices. However, the collected point clouds may be incomplete due to environmental conditions, such as occlusion. Therefore, completing partial point clouds becomes an essential task. The majority of current methods address point cloud completion via the utilization of shape priors. While these methods have demonstrated commendable performance, they often encounter challenges in preserving the global structural and geometric details of the 3D shape. In contrast to those mentioned earlier, we propose a novel cross-modal coarse-to-fine network (CMNet) for point cloud completion. Our method utilizes additional image information to provide global information, thus avoiding the loss of structure. To ensure that the generated results contain sufficient geometric details, we propose a coarse-to-fine learning approach based on multiple patches. Specifically, we encode the image and use multiple generators to generate multiple coarse patches, which are combined into a complete shape. Subsequently, based on the coarse patches generated in advance, we generate fine patches by combining partial point cloud information. Experimental results show that our method achieves state-of-the-art performance on point cloud completion.

Lithium-ion battery health state estimation constitutes an important part of battery management systems, with existing methods either based on mechanistic models or data-driven approaches. This article proposes a physics-informed hybrid multitask learning approach for estimating battery full-life aging states by integrating mechanistic knowledge with data-driven methods at an early lifetime. First, a hybrid aging mode-informed feature is introduced to integrate electrode-level health states with data-driven information. An electrochemical-informed multitask generative model is established to estimate Li$^+$concentration dynamics in both the solid particle and electrolyte. An electrode-level state-constrained training strategy is implemented to guide the model to respect causality. For validation purposes, three battery datasets are utilized to estimate aging states from the electrochemical to the cell level. Compared with traditional mechanistic and data-driven models, the proposed method demonstrates higher accuracy and real-time performance in battery state estimation.

Booming in electric vehicles and the progress of dynamic wireless charging are deepening the interdependence of transportation networks (TNs) and microgrids (MGs). An imperative exists for a framework to coordinate dynamic traffic assignment (DTA) of the TN and optimal scheduling of MGs. Considering the simultaneous route-and-departure-time choice dynamic user equilibrium (DUE), this paper presents a noncooperative framework targeting the economic-emission coordinated operation of the transportation-microgrid coupled system. This framework is a Nash-Stackelberg-Nash game, where the leader problem is an economic-emission scheduling model of MGs and the follower problem is a DUE model of DTA for the TN. The best response algorithm is adopted to solve this framework. The DUE model is converted into an equivalent fixed-point problem, while the economic-emission scheduling model of each MG is reformulated as a multi-objective mixed-integer nonlinear program (MOMINLP). Moreover, we develop an alternating operator evolutionary algorithm (AOEA) to handle this MOMINLP. Case studies demonstrate that the proposed framework enables the coupled system to operate synergistically and AOEA has a competitive performance.

Point cloud completion aims at completing shapes from their partial. Most existing methods utilized shape’s priors information for point cloud completion, such as inputting the partial and getting the complete one through an encoder-decoder deep learning structure. However, it is very often to easily cause the loss of information in the generation process because of the invisibility of missing areas. Unlike most existing methods directly inferring the missing points using shape priors, we address it as a cross-modality task. We propose a new Cross-modal Dual Phases Network (CDPNet) for shape completion. Our key idea is that the global information of the shape is obtained from the extra single-view image, and the partial point clouds provide the geometric information. After that, the multi-modal features jointly guide the specific structural information. To learn the geometric details of the shape, we chose to use patches to preserve the local geometric feature. In this way, we can generate shapes with enough geometric details. Experimental results show that our method achieves state-of-the-art performance on point cloud completion.

Prior research on emotion recognition in extended reality (XR) has faced challenges due to the occlusion of facial expressions by Head-Mounted Displays (HMDs). This limitation hinders accurate Facial Expression Recognition (FER), which is crucial for immersive user experiences. This study aims to overcome the occlusion challenge by integrating physiological signals with partially visible facial expressions to enhance emotion recognition in XR environments. We employed a multi-task approach, utilizing a feature-level fusion to fuse Electroencephalography (EEG) and Galvanic Skin Response (GSR) signals with occluded facial expressions. The model predicts valence and arousal simultaneously from both macro-and micro-expression. Our method demonstrated improved accuracy in emotion recognition under partial occlusion conditions. The integration of temporal physiological signals with other modalities significantly enhanced performance, particularly for half-face emotion recognition. The study presents a novel approach to emotion recognition in XR, addressing the limitations of facial occlusion by HMDs. The findings suggest that physiological signals are vital for interpreting emotions in occluded scenarios, offering potential for real-time applications and advancing social XR applications.

Previous research has demonstrated the potential of Augmented Reality in enhancing psychological comfort in Human-Robot Interaction (AR-HRI) through shared robot intent, enhanced visual feedback, and increased expressiveness and creativity in interaction methods. However, the challenge of selecting interaction methods that enhance physical comfort in varying scenarios remains. This study purposes a dynamic dual-layer interaction adjustment mechanism to improve user comfort and interaction efficiency. The mechanism comprises two models: an general layer model, grounded in ergonomics principles, identifies appropriate areas for various interaction methods; a individual layer model predicts user discomfort levels using physiological signals. Interaction methods are dynamically adjusted based on discomfort level changes, enabling the system to adapt to individual differences and dynamic changes, thereby reducing misjudgments and enhancing comfort management. The mechanism's success in authoring tasks validates its effectiveness, significantly advancing AR-HRI and fostering more comfortable and enhancing efficient human-centered interactions.

With the progression of VR tech, virtual interactive environments are becoming increasingly realistic and controllable. Research has substantiated the influence of VR environmental variables on user experience and engagement. Concurrently, real-time user status monitoring advancements have unlocked dynamic adjustments to VR environments through user interaction with real-time status and feedback, increasing researchers’ focus on enhancing user experience and engagement by adjusting VR environmental variables. This paper introduces an interactive paradigm for VR environments called the Dynamic Scene Adjustment (DSA) mechanism, which seeks to modify the VR environmental variables in real-time according to the user’s status and performance to enhance user engagement and experience. We selected the perspective of the impact of visual environment variables on player status, embedding the DSA mechanism into a music VR game with brain-computer interaction for specific VR tasks. Experimental findings affirm that incorporating the DSA mechanism into the VR game enhances the user’s engagement and performance, thereby strongly validating the rationality of the proposed DSA approach. This work can assist researchers think about dynamic regulation in VR environments from a new perspective and will shed light on the design of VR healing, VR education, VR games, and other fields.

Effective management of lithium-ion batteries is pivotal for energy supply systems. However, the significance of battery early prognostics is often overlooked and conventional data-driven approaches frequently fall short in precisely predicting lifespan or reconstructing degradation trajectories during the initial stages of battery life. To address these challenges, this article proposes a pseudo meta-learning (PML) neural network integrating hybrid cyclic-based and physical-informed features for battery early lifespan estimation and capacity reconstruction. A convolutional variational autoencoder is initially employed to train a robustness encoder using sufficient battery early aging data. Furthermore, PML combines features of batteries with different lifespans for checkpoint prediction from full-life battery aging data. To mitigate overfitting concerns, multiple PMLs are interconnected through a gating network, and a joint-learning strategy is introduced to fine-tune hyperparameters. In the experimental validation, comparative experiments on other battery prognostic methods are conducted, and a detailed discussion on enhancing prediction performance, considering both computational complexity and accuracy, is presented.

Precise and real-time localization is the key for autonomous vehicles (AVs) to solve traffic, environmental problems and support intelligent vehicle applications. Existing AV localization methods typically rely on the cooperation between onboard sensors, cellular systems, and vehicular networks to satisfy the high localization requirements. The advent of 5G networks can not only revolutionize cellular localization performance but also provide adept capacity to handle massive sensor data and dependable vehicle-to-everything (V2X) transmission at impressive speeds with minimal latency. These advancements stir expectations to promote cooperative AV localization from lane-level accuracy to centimeter-level. The purpose of this survey is to elaborate on the predominance of applying 5G networks for cooperative AV localization, which overviews 5G new radio features, 5G localization techniques, and the pivotal algorithms supporting 5G AV localization, including filtering-based and machine learning algorithms. Three applications of 5G cooperative AV localization are highlighted, respectively the integrations of 5G networks with satellite systems, simultaneous localization and mapping algorithms, and vehicular networks. Moreover, this survey analyzes the challenges in existing 5G cooperative AV localization systems and summarizes the potential research hotspots for further development.

Nowadays, AR HMDs are widely used in scenarios such as intelligent manufacturing and digital factories. In a factory environment, fast and accurate text input is crucial for operators' efficiency and task completion quality. However, the traditional AR keyboard may not meet this requirement, and the noisy environment is unsuitable for voice input. In this article, we introduce Eye-Hand Typing, an intelligent AR keyboard. We leverage the speed advantage of eye gaze and use a Bayesian process based on the information of gaze points to infer users' text input intentions. We improve the underlying keyboard algorithm without changing user input habits, thereby improving factory users' text input speed and accuracy. In real-time applications, when the user's gaze point is on the keyboard, the Bayesian process can predict the most likely characters, vocabulary, or commands that the user will input based on the position and duration of the gaze point and input history. The system can enlarge and highlight recommended text input options based on the predicted results, thereby improving user input efficiency. A user study showed that compared with the current HoloLens 2 system keyboard, Eye-Hand Typing could reduce input error rates by 28.31 % and improve text input speed by 14.5%. It also outperformed a gaze-only technique, being 43.05% more accurate and 39.55% faster. And it was no significant compromise in eye fatigue. Users also showed positive preferences.

This article reports a study on the operation of coupled power-transportation networks that serve electric vehicles and dynamic wireless charging lanes. A dynamic coordinated pricing model is proposed to optimize the network's operation and to achieve a dynamic network equilibrium (DNE). The transportation network uses a dynamic user equilibrium (DUE) model to determine the congestion toll and route choice, while the power distribution network uses an optimal power flow (OPF) model to determine the electricity price and power generation. To address the DNE represented by a fix-point problem, an adaptive step projection algorithm is proposed. The proposed model and algorithm are verified through case studies, which demonstrate their effectiveness in improving the network's efficiency.

In this paper, we propose a safety-critical controller based on time-varying control barrier functions (CBFs) for a robot with an unicycle model in the continuous-time domain to achieve navigation and dynamic collision avoidance. Unlike previous works, our proposed approach can control both linear and angular velocity to avoid collision with obstacles, overcoming the limitation of confined control performance due to the lack of control variable. To ensure that the robot reaches its destination, we also design a control Lyapunov function (CLF). Our safety-critical controller is formulated as a quadratic program (QP) optimization problem that incorporates CLF and CBFs as constraints, enabling real-time application for navigation and dynamic collision avoidance. Numerical simulations are conducted to verify the effectiveness of our proposed approach.

The fluctuation in system efficiency caused by coil misalignment in wireless power transfer has drawn the attention of researchers in the field.Therefore, this paper proposes a method to obtain coil misalignment. By utilizing the acquired coil position information, anti-misalignment correction can be applied to enhance system efficiency. Firstly, two auxiliary coils are added at the receiving end. Then, the misalignment between the X and Y axes is converted into polar coordinates. The output current and coil position misalignment angle are used as inputs to establish a multiple linear regression model for identifying the receiving end's position information. Finally, a random motion trajectory is set and simulated in the system to validate the feasibility of the proposed model.

Dynamic wireless charging (DWC) of electric vehicles (EVs) is a promising technology that can promote the widespread of EVs. However, the output power fluctuation occurs due to the varying mutual inductance between the receiver coil and the transmitter coils caused by the EV motion, which would lead to control performance deterioration and even instability of the system. To mitigate the power fluctuation, this paper proposes a control strategy based on exact output regulation (EOR) theory for the receiver-side buck converter of the DWC system to compensate the disturbance arising from the mutual inductance fluctuation. The mutual inductance fluctuation can be approximated as a sinusoidal signal and the FEA analysis informanation is used to obtain an predefined exosystem that can characterize the dynamics of the mutual inductance fluctuation. A state feedback controller combined with a state observer is designed to achieve a constant output voltage of the DWC system by compensating for such disturbance. The simulation results verify the effectiveness and superiority of the proposed control strategy as compared with the traditional control methods.

The matching of incorrect pairs affects the precision of the SLAM/VO system. Because the calculation is complex and time-consuming, the various limitation methods struggle to meet the system's real-time requirements. FMS is a novel approach that adapts well to both sparse and dense mapping processes, allowing for the verification of matched pairs of features. By displacing features between two consecutive frames, the projection formula contacts motion patterns. This classifier's overall characteristics demonstrated that it was extremely effective at applying suitable matched pairings from mismatched features. The proposed algorithm outperformed the raw technique in terms of accuracy and stability when compared to the ORB-SLAM technique.

We propose a binocular stereo matching algorithm CS-ELAS. This is a cross-scale ELAS algorithm that improves the accuracy and robustness of parallax in weakly textured regions and edge regions. Our approach focuses on improving the accuracy and number of support point sets. We uniformly sample the stereo images to obtain candidate support point sets and determine robustly matched support point sets based on an adaptive cross skeleton. In this way a richer and more accurate set of support points can be obtained in the weakly textured areas near the edges. In addition, our method uses the parallax and confidence maps of low-resolution images as a priori for high-resolution images and adds high-confidence pixel information to the set of high-resolution support points. In this way, it not only increases the number of support points in weak texture regions, but also narrows the search range of candidate support points and reduces the computational cost.

In this paper we introduce a novel method to improve the general performance of fast marching tree for collision-free path planning called obstacle-based fast marching tree. Our proposed OB-FMT* samples the map in discussion initially to obtain the vertexes of all obstacles and connect those vertexes to build a collision-free roadmap, an A*algorithm is adopted to find an optimal path in the built roadmap. Then multiple prolate hyper spheroids with focuses on vertexes of optimal path are established to limit the sampling range. Within the limited sampling point set, we utilize FMT* to find optimal solution with high efficiency. Theoretical analyses are given to prove the probabilistic completeness and computational complexity of our proposed method. Experiments in multiple scenarios show that our algorithm has a better performance comparing with other existing algorithms including RRT*, FMT*, Informed RRT*.

The receiving coil of dynamic wireless charging (DWC) systems of electric vehicles will be offset relative to the transmitting coil, which will cause a decrease in mutual inductance and thus a decrease in output power. The conventional solution is to add a buck converter on the energy receiving side to compensate for the power drop. Indeed, the receiving-side buck converter is a nonlinear structure, and the corresponding nonlinear control strategy study is required. In addition, the rapid and large-range changes in the mutual inductance of the DWC system will also increase the control difficulty. To solve these problems, we propose a feedback linearization control (FLC) algorithm for the buck converter on the receiving side of the DWC system. We carry out detailed modeling of the receiving-side buck converter, linearize the relationship between input and output, and analyze the stability of the controlled system. The results show that the adjustment speed of the proposed FLC is fast, and it is suitable for the DWC system. Moreover, when the mutual inductance changes rapidly in a wide range, the controlled output of the receiving-side buck converter can still track the reference value stably, demonstrating the effectiveness and superiority of the proposed FLC algorithm.

Dynamic wireless power transfer (DWPT) of electric vehicles (EVs) is considered as an economic and effective solution on traffic electrification. However, the coupling coefficient between the transmitting and receiving coils changes dynamically with the movement of EV, resulting in a sharp drop in energy transfer power and efficiency. In this case, the double closed loop controller with current sharing of interleaved DC/DC converter is proposed to against coupling coefficient variation. And two layers double D coil structure is presented to make the distribution of magnetic density and temperature more uniform. Moreover, Simulation studies are carried out in PLECS to verify the proposed strategy in DWPT system. Results show that the proposed algorithm can not only significantly improve the energy transfer power and efficiency, but also ensure the current sharing of interleaved DC/DC converter, so as to enhance the robustness and stability of DWPT system.

Rock recognition is a critical step for intelligent control system in tunnel boring machine (TBM). We build the rock recognition dataset by the visual monitoring system in TBM. After that, we present an attention-fusion network (AFN) for rock recognition classification. Our model is trained with the recognition object (rock) image, and gets the rock attention map through the self-intersection operation. To complete the classification task, we propose the attention block in the attention-fusion classifier, which is effective to fuse and encode the feature and attention in an end-to-end manner. We confirm the effectiveness of the self-intersection operation and attention block by visualized experiment and contrast experiment. At the same time, experimental results of the rock recognition dataset indicate that AFN outperforms state-of-the-art methods, including LSTM (mask-guided), ABN (attention-based), ResNetSO (global-feature), both in accuracy and realtime on the rock recognition tasks.

In this paper, we study the adaptive tracking control problem for a class of uncertain nonlinear systems with input quantization. Different from the existing results, we propose a new quantizer with saturated quantization levels motivated by the saturation property of practical actuators and sensors. With this new quantizer, we know the exact number and values of the quantization levels in advance, regardless of the magnitude of the designed control signal. Thus, we only need to code these quantization levels accordingly such that less network resources are consumed. It is shown that the proposed control scheme guarantees that all the closed-loop signals are globally bounded and the tracking error converges towards a known compact set.

In this paper, the distributed cooperative control for a class of nonlinear multi-agent systems connected by undirected graph is investigated. The dynamic of each agent is first order with unknown mismatched parameters and external disturbances. To reduce the communication burden between each agent and decrease the update frequency of the controllers, the fixed threshold event-triggered mechanisms are considered during the states's broadcasting and the control signals. Based on the adaptive method, the event-based cooperative control protocol is constructed. The asymptotically consensus tracking of all agents are guaranteed and the signals in the closed-loop system are bounded, meanwhile the Zeno behavior is excluded. Simulation results of four agents show the effectiveness and performance of the proposed method.

In this paper, we present a direct multirate adaptive control algorithm that ensures global stabilization of a class of (potentially unstable and invertible) linear time-invariant discrete-time plants of known order and relative degree one. An essential feature of our scheme is that no projections are needed, no appeal is made to the persistence of excitation arguments for the stability proof and it has a better transient performance than existing schemes. The implementation of the controller requires some prior information about its Markov parameters, namely, upper and lower bounds on the systems impulse response. It is directly applicable to plants with interlacing real poles and zeros, i.e., with Cauchy index equal to the plant order, provided the adaptation gain is restricted to be smaller than some value determined by the measure of relative primeness of the pole and zero polynomial. This class contains some practically interesting systems, for instance, resistor-inductor or resistance-capacitance circuits.

In this paper, we propose a new distributed event-trigger consensus protocol for linear multiagent systems with external disturbances. Two consensus problems are considered: one is a leader-follower case and the other is a nonleader case. Different from the existing results, our proposed scheme enables each agent to decide when to transmit its state signals to its neighbors such that continuous communication between neighboring agents is avoided. Clearly, this can largely decrease the communication burden of the whole communication network. Besides, since the control signal for each agent is discontinuous because of the event-triggering mechanism, the existence of a solution for the closed-loop system in the classical sense may not be guaranteed. To solve this problem, we employ a nonsmooth analysis technique including differential inclusion and Filippov solution. Through nonsmooth Lyapunov analysis, it is shown that uniformly bounded consensus results are derived and the bound of the consensus error is adjustable by choosing suitable design parameters.

In this study, an unscented Kalman filtering approach is proposed for nonlinear singular systems to obtain not only the estimation for the states but also for the unknown inputs presented in the measurement equations. No prior information is needed for the unknown inputs to be estimated. The formulation of the proposed approach is based on the weighted least squares estimation (LSE) and the unscented transformation (UT) methods. The restriction of the proposed approach is also mentioned. An illustrative example demonstrates that accurate and consistent state and unknown input estimations are obtained with the proposed approach.