This article addresses the problem of distributed output formation optimal tracking for heterogeneous multiagent systems (MASs). Unlike the main approach in most existing formation tracking studies, which relies on prespecified trajectories, this work aims to enable heterogeneous MASs to achieve desired formation while tracking the optimal reference trajectory generated by a distributed optimization algorithm. First, a distributed time-varying optimization algorithm is proposed as the distributed optimal reference trajectory generator, which accounts for inequality constraints and ensures fixed-time convergence in consensus and asymptotic convergence in optimality. Then, a distributed output formation optimal tracking control protocol is developed for heterogeneous MASs, with the integration of the distributed optimal reference trajectory generator. Subsequently, the convergence of the proposed distributed output formation tracking control algorithm-based on time-varying optimization-is rigorously proven using Lyapunov stability analysis. Finally, simulation examples are provided to validate the theoretical results.

This paper presents a distributed observer-based non-linear control framework to address a prescribed-time output formation tracking problem of heterogeneous nonlinear networks under a nonlinear leader system and a digraph with a spanning tree, wherein dynamics and dimensions of leader-follower systems can be different. To solve this issue, prescribed-time nonlinear distributed observers are firstly developed to estimate the nonlinear leader’s state, and the prescribed-time distributed control law is further presented. Under this framework, the zero-error output formation tracking is obtained within a pre-specified and user-defined time by leveraging the time transformation approach. The proposed algorithm is free of global knowledge, and its convergence time is irrelevant to initial conditions, control parameters or network structures. Simulation and experimental results are given to verify the designs’ effectiveness. The main features of developed designs lie in that: (1) prescribed-time output formation tracking of heterogeneous nonlinear systems can be achieved under directed graphs, wherein both dynamics and dimensions of leader-follower nonlinear systems can be different; (2) the convergence time is pre-specified and thus, irrelevant to any initial conditions, control parameters, or network structures; (3) the proposed algorithm that does not depend on any global information, is distributed under a general communication graph with a directed spanning tree; and (4) the prescribed-time output formation tracking solution can be extended beyond the terminal time.

This article addresses a prescribed-time time-varying output formation tracking (TVOFT) problem for heterogeneous multiagent systems (MASs) under directed topologies. Formation tracking in heterogeneous linear MASs is critical for practical applications, such as cooperative robotics, autonomous transportation, and surveillance. However, many existing related designs often fail to guarantee convergence within a prescribed time. To overcome this limitation, a distributed prescribed-time TVOFT protocol associated with a corresponding design algorithm is presented. In the designed protocol, a distributed output-feedback observer is constructed for each follower to estimate the state of the leader within a prescribed time. Then, a local output-feedback controller is developed by incorporating a local state observer. It is proved that the heterogeneous MASs can achieve the desired TVOFT within the prescribed time. Furthermore, a heterogeneous experimental platform consisting of two autonomous aerial vehicles and three autonomous ground vehicles, is constructed to verify the effectiveness of the proposed prescribed-time TVOFT design. Comparative experiment results highlight the advantages of the proposed design over existing methods from the perspective of accurate prescribed-time convergence and practical feasibility.

This article investigates the Nash equilibrium (NE) seeking problems for multicoalition games with heterogeneous general linear dynamics over unbalanced digraphs. The coalition can be regarded as a virtual player but the true decision-makers are the actual players themselves which can only access to their own cost functions. To deal with the unbalanced digraphs, the left eigenvector is estimated and its prescribed-time convergence is illuminated with the time transfer approach. Then the NE seeking part and the output regulation part are designed to adapt the dynamics of the players. The initial values of the auxiliary vectors are selected to avoid utilizing the out-degree information. The steady state of the closed-loop system is analyzed and the prescribed-time convergence is proved based on the Lyapunov method. Finally, the simulation results of both the mobile sensor connectivity game and the electricity market game are presented to show the effectiveness of the proposed algorithm.

Time-varying formation tracking (TVFT) control problem for multiple Euler–Lagrange (EL) systems with an uncertain leader is investigated in this article. The objective of this problem is for the followers' states to track the uncertain leader's output and achieve the desired formation. One of the major challenges in solving such a problem lies in estimating the information of the leader. In this article, an observer-based TVFT control framework is presented in this article, which does not require all or part of the follower agents to know the system dynamic knowledge of the leader agent directly in advance. First, two fully distributed adaptive observers are presented for estimating the uncertain leader's state, output, and the unknown system matrices under an IE condition, which relaxes the existing restrictive persistently exciting or cooperative finite-time excitation condition. Then, on the basis of the constructed adaptive observers, state and output variables of the leader, a fully distributed TVFT for EL systems with an uncertain leader is developed, which means the proposed formation tracking controller operates independently of the system dynamic knowledge of the leader agent; with the help of the Lyapunov stability theory, the TVFT criterion for the considered system is derived. Finally, in order to demonstrate the theoretical results derived in this article, a practical air–ground formation tracking platform, which consists of an unmanned aerial vehicle and four unmanned ground vehicles, is introduced, and physical experimental results are obtained.

The vulnerability of individual-database learned model for Micro-Expression Recognition (MER) has significantly hindered their performance in real-world scenarios. Cross-Database Micro-Expression Recognition (CDMER) aims to enhance the robustness and generalization performance for more complicated situations. Most existing CDMER methods typically attempt to leverage spatial information to eliminate the inevitable domain shift between source and target domains. In this paper, we propose a novel feature space reconstruction model that provides feature space with better generalization for CDMER. Specifically, we introduce the Spatial and Frequency Domain Co-learning (SFDC), a three-branch module, that adaptively exploits the spatial and frequency characteristics of intermediate feature representations, capturing the global and local features of micro-expression adequately. Furthermore, to facilitate the synchronization of two domains, Source and Target Domain Synchronization (STDS) module is employed to guide the alignment of different subspaces simultaneously. Extensive experimental results on the SMIC and CASME II databases demonstrate the effectiveness of the reconstruction and the superiority of our proposed method over state-of-the-art (SOTA) methods.

This article is concerned with resilient formation tracking problems for general linear multiagent systems, where the leader's control input is unavailable to all the followers and partial followers' behaviors are malicious due to the node attacks. Despite the presence of the nonautonomous leader and the adversarial followers, the remaining benign followers are still expected to track the leader's trajectory with the prescribed time-varying formation. To this end, a resilient scheme comprising an attack detection and isolation strategy and a formation tracking protocol is proposed. The detection strategy enables every benign follower to identify its adversarial neighbors with two-hop communication information, as long as the underlying topology meets given conditions. Then, the detected adversarial agents are directly removed to avoid the spread of their influence, which induces a new problem called node loss. To accommodate possible node loss events, the designed tracking protocol is independent of certain global knowledge, and its convergence is demonstrated by means of the impulsive Lyapunov functions. Finally, the proposed resilient scheme is verified by two simulation examples.

This article addresses finite-time connectivity-preserving rendezvous problems of networked uncertain Euler-Lagrange systems, where two types of time-varying leaders are investigated, and only a subset of followers can have access to the leader’s trajectory. The distributed estimation and control architecture is then established to solve this problem with an emphasis on the settling-time estimation. In particular, in the first layer, the finite-time distributed estimators are developed to estimate and reconstruct the states of both linear and nonlinear leaders, respectively. In the second layer, distributed controllers are designed for consensus tracking in a finite-time using estimated leader information. Further, to account for limited sensing ranges, another distributed algorithm is given via an artificial potential field to guarantee finite-time rendezvous. Numerical simulation results are given to validate the effectiveness of the proposed designs.

For the operation process of proton exchange membrane fuel cells (PEMFCs) system, fault diagnosis plays a crucial role in ensuring the safety and reliability of system. To improve the accuracy and rapidity of fault diagnosis, a fault diagnosis method of PEMFC based on data space feature decision tree group (DTS) and extreme learning machine (ELM) is proposed. Firstly, principal component analysis (PCA) is used to reduce the dimension of sensor data such as current, temperature and gas flow rate of system, which reflects the operating states of fuel cell. Besides, spatial features of system can be extracted and processed by random rotation matrix. Furthermore, multiple sets of spatial rotation feature data are set to diagnose the health state of fuel cell system by combining decision tree group and extreme learning machine (DTS-ELM). Finally, the proposed method was verified and analyzed, and the experimental results indicate that this method can quickly identify four operating states such as normal state, flooding, membrane dying and hydrogen leakage. The diagnostic accuracy and operating time of this method are 99.54% 0.261s, respectively. Therefore, on-line rapid fault diagnosis of proton exchange membrane fuel cell system can be realized by the proposed method.

Electrochemical impedance spectroscopy (EIS) has broad application prospects in the field of structural analysis and performance optimization of proton exchange membrane fuel cells (PEMFC), and the establishment of an equivalent impedance model based on EIS can intuitively and accurately characterize the internal characteristics of fuel cells. In this paper, an EIS diagram is drawn based on experimental data, and an equivalent impedance model that can well characterize PEMFC is proposed, and compared with commonly used equivalent models. The correspondence between each topology in the model design and the actual PEMFC electrochemical process is explained, and the electrochemical reaction principle of the catalytic layer is explained based on the model. The fitting analysis shows that the accuracy (chi-square error) of the designed model on the experimental data is improved by about 58.16%. This model can guide the process of EIS image analysis and establishing equivalent circuit models, and provide guiding ideas for related mechanism research.

This article addresses a resilient exponential distributed convex optimization problem for a heterogeneous linear multiagent system under Denial-of-Service (DoS) attacks over random digraphs. The random digraphs are caused by unreliable networks and the DoS attacks, allowed to occur aperiodically, refer to an interruption of the communication channels carried out by the intelligent adversaries. In contrast to many existing distributed convex optimization works over a perfect communication network, the global optimal solution might not be sought under the adverse influences that result in performance degradations or even failures of optimization algorithms. The aforementioned setting poses certain technical challenges to optimization algorithm design and exponential convergence analysis. In this work, several resilient algorithms are presented such that a team of agents minimizes a sum of local nonquadratic cost functions in a safe and reliable manner with the global exponential convergence. Numerical simulation results are further presented to validate the effectiveness of the proposed distributed approaches.

This article is concerned with a fault-tolerant formation tracking problem of nonlinear systems under unknown faults, where the leader's states are only accessible to a small set of followers via a directed graph. Under these faults, not only the amplitudes but also the signs of control coefficients become time-varying and unknown. The current setting will enhance the investigated problem's practical relevance and at the same time, it poses nontrivial design challenges of distributed control algorithms and convergence analysis. To solve this problem, a novel distributed control algorithm is developed by incorporating an estimation-based control framework together with a Nussbaum gain approach to guarantee an asymptotic cooperative formation tracking of nonlinear networked systems under unknown and dynamic actuator faults. Moreover, the proposed control framework is extended to ensure an asymptotic task-space coordination of multiple manipulators under unknown actuator faults, kinematics, and dynamics. Lastly, numerical simulation results are provided to validate the effectiveness of the proposed distributed designs.

We investigate a distributed time-varying formation control problem for an uncertain Euler-Lagrange system. A time-varying optimization-based approach is proposed. Based on this approach, the robots can achieve the expected formation configuration and meanwhile optimize a global objective function using only neighboring and local information. We consider the time-varying optimization where the objective functions can change in real time. In this case, the consensus-based formation tracking control issues and formation containment tracking control issues in the literature can be solved by the proposed approach. By a penalty-based method, the robots' states asymptotically converge to the estimated optimal solution to an equivalent time-varying optimization problem, whose optimal solution can achieve simultaneous formation and optimization. Furthermore, we consider two more general scenarios: 1) the local objective functions can have non-neighbor's information and 2) the optimization problems can have inequality constraints.

As it is difficult to directly share data among different organizations, data federation brings new opportunities to the data-related cooperation among different organizations by providing abstract data interfaces. With the development of Cloud computing, organizations store data on the Cloud to achieve elasticity and scalability for data processing. The existing data placement approaches generally only consider one aspect, which is either communication cost or time cost, and do not consider the features of jobs that process the data. In this paper, we propose an approach to enable secure data processing on the Cloud with the data from different organizations. The approach consists of a data federation platform for secure data processing on the Cloud named FedCube and a greedy data placement algorithm that creates a plan to store data on the Cloud in order to achieve multiple objectives based on a cost model. The cost model is composed of two objectives, i.e., reducing both monetary cost and execution time. We present an experimental evaluation by comparing our data placement algorithm with the existing methods based on the data federation platform. The experiments show that our proposed algorithm significantly reduce the total cost (up to 69.8%).

This paper addresses task-space adaptive coordinated tracking of networked manipulators with an inner/outer-loop closed control architecture, considering all the uncertain kinematics, dynamics, disturbances, and unavailable task-space velocities. One observation is that modern robotic applications may encounter situations that task-space controllers cannot be implemented on robots with the closed control architecture. In addition, it assumes that the combination of the inner/outer loop is stable and effects of unknown robotic dynamics are neglected. Existing papers in [9]-[12] provide task-space synchronization with an open control architecture and require each robot to fully access a desired global task, and to communicate via undirected or strongly connected digraphs. In contrast, this paper proposes a distributed framework over a directed graph so that a robust, distributed, outer-loop control scheme is developed to achieve task-space coordination with dynamic effects being considered and not modifying the inner control loop. Numerical simulations are presented to show the effectiveness of the design.

This paper develops a fully distributed framework to investigate the cooperative behavior of multiagent systems in the presence of distributed denial-of-service (DoS) attacks launched by multiple adversaries. In such an insecure network environment, two kinds of communication schemes, that is, sample-data and event-triggered communication schemes, are discussed. Then, a fully distributed control protocol with strong robustness and high scalability is well designed. This protocol guarantees asymptotic consensus against distributed DoS attacks. In this paper, "fully" emphasizes that the eigenvalue information of the Laplacian matrix is not required in the design of both the control protocol and event conditions. For the event-triggered case, two effective dynamical event-triggered schemes are proposed, which are independent of any global information. Such event-triggered schemes do not exhibit Zeno behavior even in the insecure environment. Finally, a simulation example is provided to verify the effectiveness of theoretical analysis.

Federated machine learning systems have been widely used to facilitate the joint data analytics across the distributed datasets owned by the different parties that do not trust each others. In this paper, we proposed a novel Gradient Boosting Machines (GBM) framework SecureGBM built-up with a multi-party computation model based on semi-homomorphic encryption, where every involved party can jointly obtain a shared Gradient Boosting machines model while protecting their own data from the potential privacy leakage and inferential identification. More specific, our work focused on a specific “dualparty” secure learning scenario based on two parties — both party own an unique view (i.e., attributes or features) to the sample group of samples while only one party owns the labels. In such scenario, feature and label data are not allowed to share with others.To achieve the above goal, we firstly extent — LightGBM — a well known implementation of tree-based GBM through covering its key operations for training and inference with SEAL homomorphic encryption schemes. However, the performance of such re-implementation is significantly bottle-necked by the explosive inflation of the communication payloads, based on ciphertexts subject to the increasing length of plaintexts. In this way, we then proposed to use stochastic approximation techniques to reduced the communication payloads while accelerating the overall training procedure in a statistical manner. Our experiments using the real-world data showed that SecureGBM can well secure the communication and computation of LightGBM training and inference procedures for the both parties while only losing less than 3% AUC, using the same number of iterations for gradient boosting, on a wide range of benchmark datasets. More specific, compared to LightGBM, the proposed SecureGBM would slowdown with $3\mathrm{x} \sim 64\mathrm{x}$ time consumption per iteration in the training procedure, while SecureGBM becomes more and more efficient when the scale of the training dataset increases (i.e., the larger training set, the lower slowdown ratio).

This paper studies a distributed secure consensus tracking control problem for multiagent systems subject to strategic cyber attacks modeled by a random Markov process. A hybrid stochastic secure control framework is established for designing a distributed secure control law such that mean-square exponential consensus tracking is achieved. A connectivity restoration mechanism is considered and the properties on attack frequency and attack length rate are investigated, respectively. Based on the solutions of an algebraic Riccati equation and an algebraic Riccati inequality, a procedure to select the control gains is provided and stability analysis is studied by using Lyapunov's method.. The effect of strategic attacks on discrete-time systems is also investigated. Finally, numerical examples are provided to illustrate the effectiveness of theoretical analysis.

This paper addresses a distributed fault diagnosis and fault-tolerant control problem for a multi-agent system modeling a large-scale power generator network. The goal is to enable all the agents to achieve the control objective of asymptotic stability without losing the system tracking performance. Before solving this DFTC problem, a distributed fault detection (DFD) method is provided for fault detection. Next, the designs focus on a DFTC scheme without estimating the upper bound of the coupled, nonlinear, state-dependent unknown input. A model-based distributed state estimator (DSE) together with a proportional-integral-like nonlinear distributed identifier (DI) is then developed to identify the unknown input. By exploiting the redundancies from the estimated states and unknown input information obtained from the DSE and DI, a novel continuous DFTC is designed to enable the agents to achieve asymptotic consensus tracking without losing the system tracking performance while achieving distributed unknown input identification. A power system example and numerical simulations are provided to illustrate the effectiveness of the proposed DFTC method.

This paper presents a distributed formation control method for a group of robots. The global objective of achieving a desired formation is obtained by dividing it into a set of local objectives which are achieved in a distributed manner. A basic repetitive pattern in the desired formation is identified and a corresponding unique differentiable task function is defined based on the position coordinates of the robots forming the pattern. Neighbor selection rules are designed for the robots in such a way that each robot is part of one or more such patterns. A singularity-robust task-priority inverse kinematics method is used to design velocity controllers to achieve these patterns. Since a robot can receive multiple control actions being part of multiple task functions or patterns, a distributed null space behavioral (NSB) approach is designed to combine such multiple control actions in a prioritized way. A comprehensive stability analysis of the proposed approach based on Lyapunov methods is presented. Simulation results are provided to verify the effectiveness of the proposed approach.

This paper is concerned with the problems of passivity and passification for a class of uncertain switched systems subject to stochastic disturbance and time-varying delay. The passivity property is adopted to analyze the influence of the external disturbance on such systems to achieve prescribed attenuation levels. Based on average dwell time approach, free-weighting matrix method, and Jensen's integral inequality, delay-dependent sufficient conditions are obtained in terms of linear matrix inequalities, which ensure the uncertain switched stochastic time-delay system to be robustly mean-square exponentially stable and stochastically passive. Then, the switched passive controllers are synthesized by linearization techniques. Finally, two numerical examples are given to illustrate the effectiveness of the proposed methods.

This paper studies the passivity-based consensus analysis and the consensus synthesis problem (called passification) for a class of stochastic multi-agent systems subject to external disturbances. Based on Lyapunov methods, graph theory, and slack matrix methods such as the free-weighting matrix and Jensen's integral inequality, a new storage Lyapunov functional is proposed to derive delay-dependent sufficient conditions on mean-square exponential consensus and stochastic passivity for the stochastic multi-agent systems. By proposing passive time-varying stochastic consensus protocols, the solvability conditions for the passification problem are derived based on linearization techniques. A numerical example is provided to illustrate the effectiveness of the theoretical results.

This paper is concerned with the problem of observer design for switched recurrent neural networks with time-varying delay. The attention is focused on designing the full-order observers that guarantee the global exponential stability of the error dynamic system. Based on the average dwell time approach and the free-weighting matrix technique, delay-dependent sufficient conditions are developed for the solvability of such problem and formulated as linear matrix inequalities. The error-state decay estimate is also given. Then, the stability analysis problem for the switched recurrent neural networks can be covered as a special case of our results. Finally, four illustrative examples are provided to demonstrate the effectiveness and the superiority of the proposed methods.