Search

This paper proposes a novel approach for distributed finite-time event-triggered consensus control of multi-agent systems over directed graphs. In the proposed approach, a potential function is introduced in the control protocol design and a dynamic external variable with a finite-time convergence rate is involved in the construction of triggering thresholds. By using the proposed approach, finite-time consensus can be achieved in a fully distributed manner and the Zeno behavior is ruled out in the framework of finite-time event-triggered consensus. The proposed approach does not need global information and only a directed spanning tree is required for the underlying communication graph. Additionally, the requirement on continuous communication for controller updates or triggering detection is removed. Finally, an example is given to show the feasibility of the proposed approach.

Vision and voice are two vital keys for agents' interaction and learning. In this paper, we present a novel indoor navigation model called Memory Vision-Voice Indoor Navigation (MVV-IN), which receives voice commands and analyzes multimodal information of visual observation in order to enhance robots' environment understanding. We make use of single RGB images taken by a first-view monocular camera. We also apply a self-attention mechanism to keep the agent focusing on key areas. Memory is important for the agent to avoid repeating certain tasks unnecessarily and in order for it to adapt adequately to new scenes, therefore, we make use of meta-learning. We have experimented with various functional features extracted from visual observation. Comparative experiments prove that our methods outperform state-of-the-art baselines., Comment: 8 pages, 6 figures, 2 tables, accepted at 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020)

Individuals’ opinions are affected by their friends or enemies and the relationship can be abstracted as a signed network. However, the emergence of social bots can bring adverse effect to people on the signed network, like misleading the public opinion. In this paper, in order to study the mechanism by which social bots can influence public opinion, we proposed a opinion dynamics model of signed network to study the how the opinion dynamics evolves based on multi-agent model. Afterwards, we give the condition when the probabilities of individuals’ selection of opinions will converge in this model. At last, we simulated to show the convergence of our model, and study the effect of different number of social bots and confidence level to the public opinion. The results shows with the increase of the number of social bots, the public opinion is lead out of balance more severely, and as the proportion of social bots reaches 20%, the public opinion of human agents are mislead, and the opinion held by social bots will be the final opinion in the human population. For the population of human agents and social bots, 17% social bots is needed to lead the public opinion. The value of self-confidence has no effect on the final opinion in the signed network, but only whether people are affected by others has influence on the final opinion.

We consider the problem of robotic grasping by 2. 5D image data sampling from a real sensor. We design an encoder-decoder neural network to predict grasping policy in real-time which enhances the robustness for the policy generation at different observation heights by fusing depth image and RGB image. We propose an open-loop algorithm to realize robotic grasp operation and evaluate our method in a physical robotic system. The result shows that our method is competitive with the state-of-the-art in grasp performance, real-time and model size. The video is available in https://youtu.be/Wxw_r5a8qV0.

In this work, we present a real-time, deep convolutional encoder-decoder neural network to realize open-loop robotic grasping using only depth image information. Our proposed U-Grasping fully convolutional neural network(UG-Net) predicts the quality and the pose of grasp in pixel-wise. Using only depth information to predict each pixel's grasp policy overcomes the limitation of sampling discrete grasp candidates which can take a lot of computation time. Our UG-Net improves the grasp quality comparing to other pixel-wise grasping learning methods, more robust grasping decision making within 27ms with 370MB parameters approximately (a light competitive version is also given). In the physical experiment, we achieve 93.08% and 93.23% grasp success rate on a 3D-printed adversarial object benchmark set and a household object benchmark set.

A typical assumption concluded from the existing opinion formation models is that two individuals (agents) can communicate with each other only if the distance between their opinions is less than a threshold which is called confidence bound. However, in the real world, formation of opinion and expression may be different, for the reason that agent feels pressured to express opinion that is similar to the dominant opinion in the group. In this paper, a modified Hegselmann-Krause (H-K) model is introduced by integrating the opinion dynamics with confidence bound and group pressure, which describes the evolution of an individual’s opinion that conform to the dominant opinion under group pressure. We find that, when there is only one agent hold dominant opinion, the system can always achieve a consensus in a finite time regardless of the confidence bound value. In generalized case, we further establish a sufficient condition for multiple agents with dominant opinions, we prove that in this condition the whole group could also reach a consensus.

In this paper, a fixed-time rigidity-based formation maneuvering control system with distributed finite-time velocity estimator is studied. The controller of the system is realized by using the local coordinate frames of the agents without the global coordinate information. We design a distributed finite-time velocity estimator, in which the desired velocity vector is considered to be available to part with agents directly, and it can be estimated at finite-time for each agent. The target formation graph is assumed to be minimally and infinitesimally rigid. A distance-based formation maneuvering controller has been presented, which proves that in 3-D space, the agents could escape from collinear environment. Meanwhile, the formation can achieve the desired shape and track the velocity trajectory in fixed-time after the estimator estimates the velocity in finite-time. The stability and convergence of the formation maneuvering system is proved by Lyapunov and input-to-state stability theory. Finally, simulations are provided to validate the performance and effectiveness of the proposed schemes.

© 2019 EUCA. This paper focuses on the control design and stability analysis of a Susceptible-Exposed-Infected-Vigilant (SEIV) epidemic model via adaptive complex networks. The network is designed empirically as a state-dependent network, where the network structure keeps changing to inhibit the epidemic propagation. The recovery rate and the disease prevention rate are chosen as the control scheme in the epidemic system, both of which are closely associated with medical resources allocation. People may cut the connection with an infected neighbor and reduce the frequency to go out when an epidemic occurs. In order to formulate this behavior, an adaptive network structure is presented which is designed to be consistent with real human contact behaviors under epidemic prevalence. A candidate Lyapunov function is employed to analyze the system stability and guarantee the extinction of the epidemic. Simulation results are shown to illustrate the high efficiency and validity of the parameter control and the adaptive network design.

In this paper, the interaction between human awareness design and epidemic dynamics over arbitrary directed networks has been studied. People gain awareness and fear of the disease from three information sources and a novel awareness design upon two infectious states is proposed to reduce the susceptibility of individuals, which in turn could affect the threshold and outbreak of the epidemic spreading system. The novel awareness response design is applied to a continuous mean-field SEIV model to illustrate the effectiveness of the system. The system global exponential stability analysis and epidemic threshold embedded with human awareness are proposed in this paper. Simulation studies are carried out to verify the effectiveness of two human awareness which can increase the epidemic threshold, reduce the epidemic breakout scale and inhibit disease transmission.

Locating the position of a target is a fundamental task in most robotic systems. This paper focuses on a passive localization method. The presented method uses passive, synchronised and localized radio sensors to take the time-difference-of-arrival (TDoA) measurements for different pairs of sensors, with reference to an unknown target-emitted radio signal. The localization method is simple, memory efficient and robust. Importantly, the proposed measurement-wise recursive method is suitable for real-time application of time-critical robotic systems. The method is easily transferable to other problems that involve finding the intersection point of multiple (curved) lines in the presence of noise. Monte Carlo simulations and experimental tests on real data were conducted to evaluate the performance of our localization method. The results obtained compare favourably to other well-known methods.

In this paper, we present novel edge-event-based triggering algorithms to achieve multi-agent consensus with Zeno-free triggers. The control inputs and triggering conditions for each agent are designed based only on relative state measurements in each agent's own local coordinate system. The existence of strictly positive minimal inter-event times guarantees the elimination of Zeno behaviour. Two cases, namely the synchronized clock case and the unsynchronized clock case, are studied. In the synchronized clock case, all agents are activated simultaneously to measure the relative state information over edge links under a global clock. Edge events are defined and their occurrences trigger the update of control inputs for the two agents sharing the link. We show that average consensus can be achieved with our proposed algorithm. In the unsynchronized clock case, each agent executes control algorithms under its own clock which is not synchronized with other agents' clocks. An edge event only triggers control input update for an individual agent. Detailed explanations and analysis are provided to show that all agents will reach consensus in a totally asynchronous manner. Numerical simulations are given to verify the effectiveness of the proposed algorithms.

In order to make the unmanned air vehicle(UAV) in the flight process to better complete the altitude control, and can perform more accurate tasks. This paper presents the altitude control based on Millimeter Wave Radar sensors, and combined with the Pixhawk (The flight controller of UAVs) design altitude hold algorithm. The main idea is to transmit the sensor's measurement data to the Pixhawk via the serial port and to fuse with the predictor of the accelerometer. At the same time, the telecontroller switch into Altitude Hold Mode (It is a mode that can make the UAV keep a fixed altitude flight). The UAV will be keeping the target altitude which is set in the program. Finally, compared with the data collected by the Vicon (Motion capture system), to verify the effect of the algorithm. The experimental results show that the Millimeter Wave Radar sensors accuracy meet the demand of subject, and the UAV flight is very stable.

The particle swarm optimization algorithm is improved by introducing the immune selection, adaptive propagation, multi-population evolution. An improved adaptive propagation chaotic particle swarm optimization algorithm based on immune selection (IS-APCPSO algorithm for short) is proposed in this paper. The performance of several algorithms has been compared by a classic example of traffic network optimization. It is proved that the improved algorithm in accelerating convergence rate, increasing the diversity of particles, and preventing premature phenomenon is effective. The novel algorithm is expected to be used in the model solution of large-scale complex traffic network optimization problem.

This paper considers how to design consensus algorithms using only sampled position data instead of involving real-time ones under directed coupling graphs. The proposed consensus algorithm utilizes only sampled position information and consists of two parts: a general part with communication delay and a special part with both communication and forced delays. A necessary and sufficient condition is provided for achieving consensus under the designed cooperative control law. Simulations are also provided to validate the proposed control protocol.

Spatial epidemiology, as an important branch of epidemiology, is attracting people's attention increasingly. Spatial assessment evaluation, as a non-typical research point of spatial epidemiology, is still attracting our attention in the field of spatial epidemiology. However, little research on behavior analysis about how people go to doctor exists, which is emphasized on in this paper from a spatial perspective. Meanwhile, the real situation is also compared with two proposed trends (i.e., trend influenced by spatial distance, trend influenced by familiarity degree) taking Ningbo city, Zhejiang province, China as example.

In this paper, we presented a novel graph-based clustering algorithm (GC). GC contains two main steps: the first step is to create a graph and find out the key nodes as centers, the second step is to divide every data point to each center. The centers are selected from a graph view. Experimental results on 8 datasets demonstrated that GC could do better than k-means, k-medoids, Hierarchical Clustering and Gaussian Mixture Models. Moreover, the most important parameter of clustering algorithms is the number of clusters K and the comparing algorithms need K set to true number of clusters. But for GC, the only requirement is that K is not less than the true number of clusters.

In this paper, we develop two event-triggered controllers with quantized relative state measurements to achieve consensus for an undirected network where each agent is modelled by single integrator dynamics. Both uniform and logarithmic quantizers are considered, which, together with two different controllers, yield four cases of study in this paper. The quantized information is used to update the control input as well as to determine the next trigger event. We show that approximate consensus can be achieved by the proposed algorithms and Zeno behavior can be completely excluded if constant offsets with some computable lower bounds are added to the trigger conditions. Numerical simulations are provided to illustrate the effectiveness of each proposed algorithm.

The problem of trajectory tracking of a moving leader for a directed network where each fully-actuated agent has Euler-Lagrange self-dynamics is studied in this paper using a distributed, model-independent control law. We show that if the directed graph contains a directed spanning tree, with the leader as the root node, then a model-independent algorithm semi-globally achieves the trajectory tracking objective exponentially fast. By model-independent we mean that each agent can execute the algorithm with no knowledge of the agent self-dynamics, though reasonably, certain bounds are known. For stability, a pair of control gains for each agent are required to satisfy lower bounding inequalities and so design of the algorithm is centralised and requires some limited knowledge of global information. Numerical simulations are provided to illustrate the algorithm's effectiveness.

An adaptive, distributed, event-triggered controller is proposed in this paper to study the problem of leader-follower consensus for a directed network of Euler-Lagrange agents. We show that if each agent uses the proposed controller, the leader-follower consensus objective is globally asymptotically achieved if the directed network contains a directed spanning tree with the leader as the root node. We provide a trigger function to govern the event time; at each event time the controller is updated. In doing so, we also obtain an explicit lower bound on the time interval between events and thus we conclude that the proposed controller does not exhibit Zeno behavior. Simulations are provided which show the effectiveness of the proposed controller. Also shown in the simulations is the piecewise constant nature of the control law; this significantly reduces the number of updates required by each actuator, thereby saving energy resources.

A variable-gain model-independent control law is proposed to solve the problem of rendezvous to a leader for a directed network of Euler-Lagrange agents. A sufficiency condition for stability is developed, requiring centralised design of the two control gains. Compared to existing results which use constant-gain model-independent controllers for directed networks, our work has two key differences. Firstly, the damping term may begin at zero, and increases only if rendezvous has not been achieved. Constant-gain controllers use conservative gain values which negatively impact convergence speed. Secondly, we introduce a novel method of analysing the Lyapunov derivative, which provides useful and unique insight into analysis of variable-gain controllers for multi-agent systems. Simulations are provided to show the effectiveness of the controller.

Given the number of vertices only, we provide a uniform upper bound for spectrum of rooted graphs under the equal-neighbor rule, by acquiring the tight upper bound of the scrambling constant (SC). Furthermore, with the concept of canonical form of rooted graphs, we find the least connective topology of rooted graphs in the sense of SC.

In this paper we consider the finite time synchronization problem for networked Kuramoto oscillators. We propose two types of synchronization protocols, both of which involve continuous phase couplings. Based on the finite time Lyapunov theory, we prove that if initial phases of all oscillators are located in a semi-circle, then their phases will reach synchronization within a finite time. The upper bounds of the finite settling time for both synchronization protocols are also derived. We then extend the results to the case of Kuramoto oscillators with identical natural frequency. Several simulations are presented to demonstrate the performance of the proposed finite time phase-coupling controllers.

In this paper, we present a fast, non-iterative approach to smooth a noisy input on the Special Euclidean Group, SE(3) manifold. The translational part can be smoothed by a simple Gaussian convolution. We then proposed a novel approach to rotation smoothing. Unlike existing rotation smoothing methods using either iterative optimization methods or stochastic filtering methods, our method allows direct computation of the smoothing result and allows parallelization of the computation. Furthermore, we have done a comparative study on Jia and Evans's method published in 2014 [1], and shown that our method can better smooth an input rotation sequence, with shorter computational time. The smoothed camera path is then used for video stabilisation, which shows fluid and smooth camera motion.

The integrated navigation pattern that is formed by the scene matching navigation and the inertial navigation or the satellite navigation is an important development direction of UAV autonomous navigation technology. As for the error identification problem of matching area in the process of scene matching landmark detection, the algorithm of visual saliency landmark detection for scene matching based on deep neural network for feature extraction is proposed. The experiments illustrated that the algorithm can effectively solve the problem of landmark detection in different environments and improve the robustness and effectiveness of landmark detection.

The performance of SAA (Sense and Avoid) for UAV (Unmanned Aerial Vehicle) is the key function to ensure the UAV's autonomous flight safety. Due to the limitation of payload and size, the micro UAV can't equip large sensors such as radar, etc. Toward the challenging operation condition in low altitude flight environment, we proposed a complete stereo vision based SAA solution suitable for MUAV (Micro Unmanned Aerial Vehicle). By processing the stereo vision data onboard the micro UAV, the proposed method is able to efficiently detect obstacle, evaluate threat level and perform collision avoid maneuver. Experimental results show that the SAA method based on stereo vision can achieve accurate obstacles sensing and effective avoidance maneuver in low altitude airspace, The proposed SAA method can be a practical resolution to ensure low altitude SUAV's safety.

This paper investigates the distributed consensus for second-order multi-agent systems via a novel cooperative protocol which involves only sampled position data of each agent. The proposed consensus algorithm consists of two parts: a general part with communication delay and a special part with both communication and forced delays. It is revealed that the forced delay is lower bounded by a threshold and plays an important role for achieving consensus. A necessary and sufficient condition with respect to the sampling period, coupling gains, time delays, and eigenvalues of the Laplacian matrix, is established for achieving consensus under an undirected interaction topology. It is found that choosing a relatively small sampling period does not necessarily promote achieving consensus under fixed coupling gains and time delays. Finally, some illustrative examples are provided to demonstrate the effectiveness of the theoretical findings.

In this paper, we investigate the distributed synchronization problem for multiple fully actuated rigid bodies without a leader. A new mathematical tool-dual quaternion is employed to design the distributed control law. The definition and some properties about dual quaternion are introduced. A distributed control law is proposed to control the attitude and translation of these rigid bodies simultaneously with notion concision and non-singularity. And the analysis based on dual quaternion is a unified solution for the attitude and translation. Finally, the proposed control algorithm is simulated to illustrate the theoretical results.

In this paper, we propose dual quaternion based dynamic output feedbacks for the translational and rotational stability problem of a rigid body without linear velocity and angular velocity measurements. Dual quaternion is a new kind of tools to express the motion of rigid bodies in 3D space, including translation and rotation. Our approach consists of an output feedback controller and introducing an auxiliary system whose input is the relative error between the states of the rigid body and the states of the auxiliary system. The resulting linear and angular velocity free control algorithm guarantees the asymptotical stability of the rigid body. Next, a stability algorithm based on unit dual quaternion is also proposed for the rigid body under its general dynamics. One simulation is provided to show the effectiveness of the proposed control algorithm.

This work considers the scenario where three agents that can sense only bearings use two landmarks to control their formation shape. We define a method of relating the known distance separating the landmarks back to the edge lengths of the triangular formation. The result is used to define a formation control law that incorporates inter-agent distance constraints. We prove a strong exponential convergence result and show how one can extend the controller such that global stability from any initial position is possible.

Sense and Avoid (SAA) has been identified as one of the crucial technologies for UAV safety flight. The SAA systematic design is to develop a system with sensor configuration and SAA algorithms. Normally, the SAA system integration and test can be both expensive and dangerous without carefully system and algorithms development. In this paper, we proposed a software based SAA simulation framework for SAA system configuration and algorithms design. First, the software structure and functional submodules are described. Then the SAA function is modelled and optimization based algorithms integrating environment sensing, collision evaluation and avoidance. Simulation results and analysis are provided to validate the simulation software and algorithms. By configuring the sensors and adjusting the algorithms parameters, the software based simulation framework can act as a guidance to the development of practical SAA systems.

© 2016 IEEE. In this paper, the tracking problem for a rigid spacecraft with input saturation is investigated. Due to the complexity of the dynamic of a rigid spacecraft, a hierarchical control scheme, dividing the model of a rigid spacecraft into translational level and attitude level, is designed. The controllers for translational dynamic and attitude dynamic of the six-freedom-degrees spacecraft are successively proposed respectively. The input saturation is also considered in designing the two controllers. For translational level, a kind of special saturation function is employed to get the controller whose amplitude is constrained. For the attitude dynamic, a new adaptive quaternion-based control scheme is proposed to withstand the effect of bounded uncertain items and input saturation. Finally, simulation results are provided to show the effectiveness of the proposed scheme.

This paper proposes a distributed, modelindependent algorithm to achieve rendezvous to a stationary leader for a directed network where each fully-actuated agent has Euler-Lagrange self-dynamics. We show that if the directed graph contains a directed spanning tree, with the leader as the root node and with no incoming edges, then a model-independent algorithm semi-globally achieves the rendezvous objective exponentially fast. By model-independent we mean that each agent can execute the algorithm with no knowledge of the parameters of the self-dynamics of any agent in the network. For stability, a pair of control gain terms for each agent are required to meet several inequalities and so design of the algorithm requires some limited knowledge of global information. Numerical simulations are provided to illustrate the algorithm's effectiveness.

This paper discusses generalized controllers for rigid formation shape stabilization. We provide unified analysis to show convergence using different controllers reported in the literature, and further prove an exponential stability of the formation system when using the general form of shape controllers. We also show that different agents can use different controllers for controlling different distances to achieve a desired rigid formation, which enables the implementation of heterogeneous agents in practice for formation shape control. We further propose an event-triggered rigid formation control scheme based on the generalized controllers. The triggering condition, event function and convergence analysis are discussed.

This paper considers a rigidity optimization problem for mobile robotic teams modeled in a proximity network with state-dependent network topology. The aim is to move all robots' positions to reach a configuration such that the worst-case rigidity metric can be maximized. Key properties of a Gramian matrix involving a weighted rigidity matrix are discussed for solving this optimization problem. We design a decentralized algorithm to update all robots' positions to maximize the eigenvalue function, which requires local information from each robot itself and its neighbors. Furthermore, a distributed eigenvector estimation scheme based on inverse shifted power iteration method and averaging consensus algorithm is devised to allow each robot to estimate the global eigenvector information. Simulation results are also provided to demonstrate the effectiveness of the estimation and optimization scheme.

Existing results on distance-based rigid formation stabilization commonly require continuous measurements and control update. In this paper, we consider an event-triggered scheme for distance-based formation control problem. We prove the local exponential stability of this event-driven system for stabilizing formations which are infinitesimally and minimally rigid. Furthermore, we prove that Zeno-behavior does not exist and obtain a strict positive lower bound in the inter-event time. Simulations are also provided to verify the proposed controller.

In this paper we detail the implementation of a Differential GPS system using only low-cost GPS receivers. The nature of the errors generated by low-cost modules is investigated and contrasted against typical error models of traditional, commercial DGPS systems. It is found that the error profile unique to low-cost GPS modules leads to a system that offers particular advantage in the horizontal plane compared to uncorrected receivers and as such is especially suited for tasks such as mapping and field robotics. A particular incarnation is demonstrated on a mobile robot with experimental results from static and moving tests showing an accuracy in the plane on the order of one metre.

In this paper, methods to improve a practical Time Difference of Arrival (TDOA) based source localization system are investigated. Firstly, our previous work on the development of a TDOA based source localization system using reconfigurable Software Defined Radios (SDRs) is described in detail. The system can achieve less than 100m error in a coverage of several kilometers. However, the performance of the localization system is limited by many factors. In order to further improve the localization accuracy, a method to improve the resolution of TDOA measurements is proposed; and by investigating the synchronization error and sensor position error through experiments, their error distributions are obtained to reduce measurement error. When the measurement accuracy reaches a limit constrained by current hardware platforms, localization optimization algorithms are implemented. Instead of proving the effectiveness of the algorithms through simulation, all numerical results are obtained from real-world experiments and the results show great enhancement of the localization accuracy.

In this paper we detail the design and implementation of a Differential GPS system that uses only low-cost, light-weight and widely available GPS modules. Such a system is desirable for modern field robotics in order to improve the performance of mapping, navigation, collision avoidance and other common tasks, especially on payload-constrained robots. The particular noise and error modes present in the low-cost system are investigated and algorithms introduced to minimise their effect. The system is demonstrated in the context of field robotics for stationary, ground-based and airbourne agents. It is found that the majority of the error due to the low cost components can be manipulated to be of a form easily mitigated for most robots. The system is shown to provide good results in the horizontal plane in open environments such as those typically used for outdoor experimental robotics while being one to two orders of magnitude cheaper than comparable commercial systems.

This paper discusses the construction of rigid formation from arbitrary non-rigid components in two-dimensional space. Specifically, we focus on developing strategies for the construction sequences under the premise of building minimum number of links between the non-rigid components. Three operations, namely spindle splitting, rigid component shrink and edge floating, are proposed. The scenarios of acquiring a rigid formation from different kinds of non-rigid components are discussed respectively. It is proved that our strategy will guarantee the rigidity of the obtained formation with minimum number of inserted links, and will cover all the possible solutions during the construction process.

In this paper, we study the synchronization problem for a class of identical linear, high order systems. The interconnections between the systems are assumed to be time-varying, directed, and the underlying graph is not necessarily connected at any time instant. We show that in this setup, even destabilization may occur through diffusive couplings. In order to overcome this problem, we present a distributed control law, which is able to guarantee positive invariance of energy level-sets, and we investigate its synchronizing properties.

This paper proposes a two stage approach to solving a simple network localization problem arising in the control of multi-vehicle formation shapes using bearing-only measurements. While it is impossible for one agent to localize, in its own coordinate basis, a second agent undergoing arbitrary plane motion using bearing-only measurements, this paper shows how to use a combination of a Fourier Transform and an overdetermined linear system of equations to allow two agents undergoing plane circular motion to localize each other. It is postulated that each agent only knows the parameters fully describing its own motion and must determine enough parameters of the other agent to localize it. A Fourier Transform of the measured bearing is used by each agent to obtain an approximate magnitude of the other agent's angular velocity and a two-dimensional search grid is used in an overdetermined linear equation system to solve the localization problem. The paper investigates the effect of noise in bearing measurements on the accuracy of the proposed method, offering some potential methods of decreasing the effect of noise.

We study a new variant of consensus problems, termed `local average consensus', in networks of agents. We consider the task of using sensor networks to perform distributed measurement of a parameter which has both spatial (in this paper 1D) and temporal variations. Our idea is to maintain potentially useful local information regarding spatial variation, as contrasted with reaching a single, global consensus, as well as to mitigate the effect of measurement errors. We employ two schemes for computation of local average consensus: exponential weighting and uniform finite window. In both schemes, we design local average consensus algorithms to address first the case where the measured parameter has spatial variation but is constant in time, and then the case where the measured parameter has both spatial and temporal variations. Our designed algorithms are distributed, in that information is exchanged only among neighbors. Moreover, we analyze spatial frequency response and noise propagation associated to the algorithms. The tradeoffs of using local consensus, as compared to standard global consensus, include higher memory requirement and degraded noise performance.

This paper formulates the coordination control for homogeneous linear multi-agent systems under network topology without any structural confinement. This general setting is proved to be able to unify most of the existing models such as (leaderless and leader-following) synchronization/consensus control and containment control, which are usually considered separately using different analyses, within the same framework. More importantly, it can unravel the most general coordination behavior inherent in network of diffusively coupled agents with identical dynamics. Coordination analysis for such a general framework is successfully performed based on the mixed tools from Lyapunov stability theory, matrix analysis, and algebraic graph theory, which is proved being effective in specifying the convergence rate as well.

This paper investigates the problem of global leader-following consensus of a discrete-time linear multiagent system subject to actuator saturation. Global leader-following consensus algorithm is developed with proper choice of relative coupling gains. Under the assumption that each agent is marginally stable, it is shown that global consensus of the discrete-time multiagent system can be reached under the general undirected graph provided that its generated graph contains a directed spanning tree. Numerical examples are provided to demonstrate the theoretical results.

This paper presents the implementation of a low cost Time Difference of Arrival (TDoA) based localization system using Software Defined Radios (SDRs) and it shows reasonable accuracy. The sources of localization error are also discussed.

This paper is concerned with the issue of noise shaping of delta-sigma modulators. The shaped noise transfer function (NTF) is assumed to have infinite impulse response (IIR), and the optimization objective is minimizing the maximum magnitude of the NTF over the signal frequency band. By virtue of the generalized Kalman-Yakubovich-Popov lemma, the optimization of NTFs is converted into a minimization problem subject to quadratic matrix inequalities, and then an iterative linear matrix inequality algorithm is proposed to solve this alternative minimization problem. The proposed result overcomes the limitation of a latest method that can deal with NTFs with finite impulse response only. A design example is provided to demonstrate that the proposed design method has an advantage over the benchmark one in improving the signal-to-noise ratio.

This paper aims to extend the nonnegative matrix theory, which is widely employed for multiple integrator agents, to deal with the consensus control of generic linear multi-agent systems (MASs) under directed dynamic topology. It is finally shown that the exponential consensus can be reached under very relaxed conditions, i.e., the directed interaction topology is only required to be repeatedly jointly rooted and the exponentially unstable mode of each individual system is weak enough. Moreover, a least convergence rate and a bound for the unstable mode of the individual agent system, both of which are independent of the switching mode, can be explicitly specified.