This paper investigates the fully distributed control problem with performance guarantees for swarm robotics in noncooperative monotone games. To address this problem, a fully distributed hierarchical control framework is proposed. Specifically, by integrating regularization techniques and gradient-based optimization methods, a fully distributed Nash equilibrium seeking strategy is developed to generate desired trajectories for swarm robots in noncooperative monotone games. Next, to track the desired trajectories, we propose an element-wise adaptive funnel control strategy, which guarantees prescribed transient and steady-state performance for each robot. The core of this strategy lies in establishing a tighter asymmetric feasible region to achieve superior overshoot performance, along with an adaptive compensation component that dynamically compensates for lumped uncertainties, thereby effectively suppressing chattering in the control signals. Theoretical analysis demonstrates that the swarm robots converge arbitrarily close to the least-norm Nash equilibrium under the proposed control framework. Finally, its effectiveness is validated through numerical simulations and physical experiments on a least-distance formation task. Note to Practitioners—The motivation of this paper stems from the formation control problem of heterogeneous swarm robots, and the proposed framework can also be applied to other multi-agent systems operating in noncooperative game scenarios. Although some existing studies have designed control laws for Nash equilibrium seeking, their practical applicability is often limited. In this paper, a fully distributed hierarchical control framework is developed, which accomplishes least-norm Nash equilibrium seeking in merely monotone games and drives the nonlinear swarm robots to an arbitrarily small neighborhood of the Nash equilibrium. Notably, the proposed algorithm operates effectively in typical merely monotone game settings, such as the least-distance formation problem, without requiring any information of the Laplacian matrix. Furthermore, the introduced element-wise adaptive funnel control strategy independently constrains both positive and negative overshoot of the collective robots, thereby guaranteeing prescribed transient and steady-state performance. The effectiveness of the method is verified through numerical simulations and physical experiments, thereby demonstrating its feasibility for industrial applications.

Traditional rigid-time control in multimachine power systems faces severe actuator saturation risks due to the conflict between strict stability demands and finite generator capacities. To address this challenge, this paper presents a novel distributed control strategy for auxiliary voltage regulation, establishing a control chain comprising a modeling foundation, a core coordination mechanism, and an implementation guarantee. For the modeling foundation, a nondimensional analysis framework is developed to provide a unified structural basis across heterogeneous generators. This framework reveals intrinsic multimachine physical scaling laws, facilitating the active regulation of effective constraint boundaries through base quantity adjustment. Based on this structural foundation, an input-time coordination mechanism functions as the core autonomous saturation mitigation mechanism, which dynamically extends the convergence deadline in response to surging energy demands to relieve overloads. Finally, regarding the implementation guarantee, a distributed adaptive controller is synthesized, where a fuzzy logic system is employed to approximate system nonlinearities, and a singularity-free prescribed-time function is introduced to ensure initial feasibility and eliminate infinite gain risks. For the studied multimachine scenarios, the performance of the proposed scheme is evaluated based on numerical simulations, demonstrating that this synergistic framework significantly alleviates the practical control burden while preserving transient stability.

As a proactive maintenance approach, remaining useful life (RUL) prediction plays a key role in smart operation and maintenance of industrial systems. To enhance the interpretability of deep neural network, and to measure the uncertainty of complex systems in the degradation process, an RUL prediction approach based on interpretable serialized variational autoencoder with drift-diffusion stochastic equation (ISVAE-DDSE) is proposed. Specifically, considering a dynamic sequential modeling method, this article proposes a generative deep learning approach to ensure that the model effectively captures the distribution characteristics of degradation data. On this basis, from the perspective of probabilistic deep generative network, this article derives a new type of generative loss function with the aid of the Bayesian theory. Furthermore, this article proposes an interpretable latent variable construction pattern based on DDSE, which integrates the dynamic representation of states, and rate of state change. In this sense, the network model can understand, and predict the evolutionary behavior of complex systems over time. Moreover, a Gaussian distribution network is designed to evaluate the RUL prediction’s uncertainty. This article demonstrates the advantages of the ISVAE-DDSE using a NASA aircraft turbofan engine dataset.

This paper considers the dynamic coverage control of multi-rotor Unmanned Aerial Vehicles (UAVs) with the limited sensory range, which aims to collect sensor information from all points of interest in the given task area until the desired prescribed level is reached. However, the unknown environments are usually unavoidable for coverage task, where the presence of various obstacles and communication interferences affect the flight safety and communication stability of UAVs. Therefore, collision avoidance and connectivity maintenance are considered as the two safety issues in this paper, in which connectivity maintenance ensures the communication environment for UAVs to collaboratively accomplish task, and collision avoidance is used for UAVs to avoid obstacles and neighbors. In order to realize dynamic coverage control with safety constraints based on local environment information, this paper proposes the reinforcement learning-based algorithm with shield, where the shield designed by discrete-time Control Barrier Function (CBF) not only ensures the safety of the UAVs in the learning and control phases, but also maximizes the coverage performance of UAVs. In addition, each UAV only relies on local information to generate safe actions for advancing the coverage process during the execution phase. Finally, the effectiveness of the algorithm is verified by numerical simulations and physical experiment. Note to Practitioners—A typical application scenario of dynamic coverage control is search and rescue (SAR), in which UAVs equipped with multiple sensors focus on monitoring areas where trapped people may be present, e.g., anomalous areas detected by infrared sensors due to human body temperature. Since SAR always occurs in unknown environments, it is crucial to ensure the safety of UAVs during missions, of which the safety issues considered in this paper include collision avoidance and connectivity maintenance. To perform the SAR mission safely, we construct the CBF-based shield, which minimizes corrections the exploration actions of the UAVs and ensures the safety of the cluster during the mission. In addition, UAVs are difficult to obtain global environmental information in unknown environments and only rely on their sensors to collect local information. Therefore, the reinforcement learning algorithm with shield proposed in this paper adopts the centralized training and decentralized execution strategy, where the UAVs only need local observation information to plan their next actions. Physical experiments were also conducted to validate the feasibility of implementing the proposed algorithm using real UAVs.

One of the core tasks of design for testability (DFT) is to generate an optimal test strategy based on the test mode, to isolate faults quickly and accurately. There are currently two modes: 1) sequential test mode (STM) and 2) parallel test mode (PTM). For complex systems, limited testing resources are difficult to meet parallel test conditions, so STM is mostly used. The multisignal flow graph is a widely used model for generating optimal sequential test strategy (STS) in DFT. However, this STM-based model overlooks the possibility of conducting some tests in parallel, resulting in lengthy test time and greatly affecting the reliability and security of the systems. To solve this problem, an optimal sequential-parallel test strategy (SPTS) generation method is proposed. First, a new test mode of global sequential testing and local parallel testing is proposed to generalize the original model. Second, to overcome the combinatorial explosion caused by the new model, we approximate the discrete model to continuous and derive a probability heuristic function. Then, a neural network-intelligent algorithm structure is established to simplify the complex recursion of the heuristic function. Finally, this heuristic function is used to guide the generation of SPTS, which has a shorter test time than STS. Simulation results show that the reduction in time is related to the type and number of locally parallel tests, and reaches 39.5% in a real case.

Existing fully distributed protocols for flocking are built upon the network of mobile agents with only cooperative interactions. Rather than investigating such networks, this paper deals with the problem of leader-follower flocking control over signed communication networks, where there impose less restrictions on the distributions of cooperations and competitions in the network of mobile agents. Firstly, for the second-order dynamics model, a novel state feedback controller that relies only on the relative velocity information of neighboring agents is developed. Secondly, the solvability of flocking control problem is transformed to the asymptotic stability of error system, and the latter is guaranteed by treating the product convergence of infinite super-stochastic matrices. Then, sufficient condition for the solvability of flocking control problem is proposed by establishing the inequality constraints on positive and negative edge weights. Finally, a numerical example is performed to illustrate the correctness of the theoretical result.

Switching mechanical wave (SMW) occurring at switching moment in insulated gate bipolar transistor (IGBT) has attracted extensive attention in recent years. However, AE sensors widely utilized in current researches can hardly realize the SMW detection in the local area of single-tube IGBTs due to the limitations of large volume and low spatial resolution, thus hindering the further investigation of SMW effect mechanism revelation and physical properties. Therefore, this work aims to develop a high spatial resolution laser interferometric detection system to capture SMW signals at different spatial locations of single-tube IGBTs, thus enhancing the comprehension on the underlying mechanisms and physical properties of SMW effect in single-tube IGBTs.

This article explores the fuzzy adaptive bipartite consensus problem of stochastic multiagent systems (MASs) using a singularity-free prescribed performance control (PPC) approach. When bipartite consensus errors approach constraint boundaries under the effect of adverse factors, the conventional PPC method may encounter a singularity issue, which can degrade system performance or lead to system instability. To address this issue, this article generalizes the concept of shear mapping to the PPC approach of stochastic MASs. Subsequently, a reference performance function is designed to guide the evolution trend of bipartite consensus errors, which effectively decreases the overshoot of bipartite consensus errors. Moreover, a scaling function is designed to remove the feasibility conditions in the existing PPC results. The proposed approach ensures that all signals of the closed-loop systems are semiglobally ultimately uniformly bounded in probability. Finally, a set of simulation results is provided to confirm the effectiveness of the proposed approach.

This paper proposes a novel dynamic eventtriggered scheme for nonlinear multi-agent systems under denialof-service (DoS) attacks via an adaptive fuzzy resilient control method. At the beginning, fuzzy logic systems are utilized to identify the unknown system dynamics. Then, a reliable attack detection mechanism forms the basis for establishing a dynamic event-triggered protocol, where dynamic parameters are introduced to adjust the threshold of event-triggered conditions. Compared with common attack detection methods relying on residuals between system and observer values, a novel and reliable mechanism for detecting DoS attacks is proposed, grounded in the logical relationship of voltage level signals derived from the outputs of detection components. Finally, a backstepping recursive design framework is utilized for constructing an eventtriggered adaptive fuzzy controller. Through the Lyapunov analysis, it is strictly demonstrated that, even under DoS attacks, the followers remain inside the leaders’ defined convex hull. The effectiveness of the presented control scheme is illustrated through the simulation results.

Nonferromagnetic metal materials are widely used in industry. Defects generated during manufacture and use may lead to serious accidents. The defect reconstruction is important for nondestructive evaluation. Eddy current pulsed thermography (ECPT) is a well-known nondestructive testing and evaluation method, but hardly reconstruct fully defect profile. Electrical impedance tomography (EIT) shows promising potential in defect profile reconstruction, but suffers from electrode number limitation. In this article, EIT is introduced to ECPT image sequences processing, and a new method, induced current thermo-electrical impedance tomography, is developed. The proposed method takes each infrared camera pixel as a virtual electrode, captures the electric current distribution with spatial resolution as high as camera, and reconstructs conductivity distribution at pixel level from which the defect profile can be identified. Experiments with different defects are carried out to verify the performance of the proposed method.

A new distributed filtering problem is fully studied in this article for time-varying systems over sensor networks under measurement errors and measurement censoring, where the measurement error is modeled as stochastic sensor bias driven by a dynamical equation and the measurement censoring is described by the Tobit measurement model. To reduce data congestion and transmission burden, a weighted try-once-discard protocol (WTODP) is applied to transmission channels to efficiently orchestrate data communication. The transmission priority is determined in a dynamical way depending on the importance of missions. The aim of this article is to construct an optimal distributed Tobit Kalman filter (TKF) such that filter parameters are rigorously determined in the minimum mean squared error sense under the consideration of the bias, censoring and WTODP effects. Specifically, sparsity of the network topology is comprehensively considered by using the novel matrix simplification technique. Furthermore, the resultant filtering error is ensured to be exponentially bounded in the mean squared sense. Finally, an illustrative example is used to show the applicability of the proposed filter.

This article constructs a leader–follower architecture by introducing intermittent-influence opinion leaders to the DeGroot model and analyzes the influence of this type of leaders on the evolution of opinions. Different from the existing studies where the leaders can convey their opinions to the followers uninterruptedly, the leaders in this article can only convey its opinion by broadcasting at some intermittent moments. First, we analyze the relationship between the leaders’ broadcast moments and the consensus opinion of followers and explain that the marginal revenue of the broadcasts is diminishing. Second, we describe the connotation of assimilation and calculate the minimum number of broadcasts required for the leaders to assimilate the followers’ opinions. Finally, aiming to make the consensus opinion of the followers and the leaders’ as close as possible, we give an optimal strategy on how to select followers to broadcast. The correctness of theoretical results is verified by numerical simulations.

In this article, a new distributed filtering problem is studied for a class of state-saturated time-varying systems over sensor networks under measurement censoring, where the censored measurements are described by the Tobit measurement model. To curb the data collision and ease communication burden, a random access protocol (RAP) is implemented onto the sensor-to-filter channels to orchestrate the transmission sequence of multiple sensor nodes. The purpose of the addressed problem is to construct a state-saturated distributed filter such that upper bounds (on filtering error covariances) are guaranteed and filter parameters are determined to accommodate both measurement censoring and state saturation under the RAP. By means of matrix difference equations, the desired upper bounds are first acquired and later minimized through appropriately designing filter parameters. Particularly, the sparsity issue with respect to the network topology is tackled via the employing certain matrix simplification technique. A simulation example is finally presented to showcase the applicability of the proposed state-saturated distributed filtering algorithm.

In this article, a new outlier-resistant mechanism is proposed to deal with the variance-constrained filtering problem for a class of networked systems subject to sensor resolution under the round-robin protocol (RRP). Sensor resolution, which serves as an important index in determining measurement accuracy, is taken into account in the addressed filtering problem, and the sensor-resolution-induced uncertainty is tackled by using an upper-bounding technique. The RRP is employed to regulate the order of signal transmission in order to relieve communication overhead. In the case of measurement outliers, a tailored saturation function is dedicatedly introduced to the filter structure for the purpose of suppressing the outlier-corrupted innovations, thereby maintaining satisfactory filtering performance. By solving a matrix difference equation, an upper bound is first acquired on the error covariance of the devised filter, and the associated filter parameters are subsequently determined through minimizing the acquired bound. The validity of the developed variance-constrained filter design approach is thoroughly demonstrated via two simulation examples.

This paper presents an investigation of dynamic thermal deformation characteristics in insulated gate bipolar transistor (IGBT) modules under bonding wire cracking condition by means of finite element simulation and experimental validation. Firstly, a realistically restored three-dimensional geometric model for IGBT modules is constructed and simulated to investigate thermal deformation field. Then the thermal deformation field characteristics under bonding wire intact and cracked conditions are compared and analyzed indepth. The result shows that the thermal deformation fluctuation amplitude of the cracked bonding wire decreases by 82%, while the thermal deformation value of other unbroken wires increases by 35% on average. Finally, the experimental verification is carried out, and the conclusion shows that it coincides well with the simulation results. This work provides confident evidence and important data to facilitate more precise life-time predictions and thermal-mechanical reliability assessment for power electronic modules.

This work emphasises the bipartite flocking of leader-follower Cucker-Smale model on cooperation-competition networks under Denial-of-Service (DoS) attacks. The DoS with limited energy is the periodic signal that consists of active periods and asleep periods. It interruptes the information interactions among agents during the activation stages, while it concentrates on storing own energy for the next attack moment during the asleep stages. Meanwhile, the control strategy to defend against DoS attacks is established. The products convergence of infinite sub-stochastic matrices method is employed to implement the bipartite flocking behavior. Based on this method, a algebraic condition which is related to initial states, the topological structure and the weight function is constructed. Moreover, the obtained theoretical results are demonstrated by numerical simulations.

The existing studies for tracking consensus of multiagent systems (MASs) are all restricted to networks with only cooperative relationships among agents. Tracking consensus, however, requires beyond these traditional models due to the ubiquitous competition in many real-world MASs, such as biological systems and social systems. Taking into account this fact, this article aims to extend the dynamics of tracking consensus to signed networks containing both cooperative and competitive relationships among agents. A group of agents with general linear dynamics is considered. The cases of the fixed network as well as switching networks are analyzed, respectively. In the end, some algebraic conditions related to the network structure and the positive/negative edge weight are established to ensure the implementation of tracking consensus. Moreover, the single decoupling system is allowed to be strictly unstable in theory, and the upper bound of the eigenvalue modulus of the system matrix related to the system instability is given.

In this article, the asynchronous fault detection (FD) strategy is investigated in frequency domain for nonlinear Markov jump systems under fading channels. In order to estimate the system dynamics and meet the fact that not all the running modes can be observed exactly, a set of asynchronous FD filters is proposed. By using statistical methods and the Lynapunov stability theory, the augmented system is shown to be stochastic stable with a prescribed$l_{2}$gain even under fading transmissions. Then, a novel lemma is developed to capture the finite frequency performance. Some solvable conditions with less conservatism are subsequently deduced by exploiting novel decoupling techniques and additional slack variables. Besides, the FD filter gains could be calculated with the aid of the derived conditions. Finally, the effectiveness of the proposed method is shown by an illustrative example.

Signed digraphs with both positive and negative weighted edges are widely applied to explain cooperative and competitive interactions arising from various social, biological, and physical systems. This article formulates and solves the asynchronous tracking control problem of multiagent systems with input uncertainties on switching signed digraphs. In the interaction setting, we assume that the leader moves at a time-varying acceleration that cannot be measured by the followers accurately, and further suppose that each agent receives its neighbors' states information at certain instants determined by its own clock, which is not necessary to be synchronized with those of other agents. Using dynamically changing spanning subdigraphs of signed digraphs to describe graphically asynchronous interactions, the asynchronous tracking problem is equivalently transformed into a convergence problem of products of general substochastic matrices (PGSSM), in which the matrix elements are not necessarily non-negative and the row sums are less than or equal to 1. With the help of the matrix analysis technique and the composition of binary relations, we propose a new and original method to deal with the convergence problem of PGSSM, and further establish a spanning tree condition for asynchronous tracking control. Finally, the validity of the theoretical findings is verified through several numerical examples.

It is common for communities in real-world networks to possess hierarchical and overlapping structures, which make community detection even more challenging. In this paper, by investigating consensus process of the classical DeGroot model in opinion dynamics, we propose a novel method based on the cumulative opinion distance (COD) to discover hierarchical and overlapping communities. It is shown that this method is different from those classical algorithms relying on static fitness metrics that depict the inhomogeneous connectivity across the network. The proposed method is validated from two aspects. First, by estimating the eigenvectors of adjacency matrices, we investigate the detectability limit of our algorithms on random networks, which together with the results concerning the convergence speed of consensus guarantees the performance of our method theoretically. Second, experiments on both large scale real-world networks and artificial benchmarks show that our method is very effective and competitive on hierarchical modular graphs. In particular, it outperforms the state-of-the-art algorithms on overlapping community detection.

With the popularization of network equipment and the rapid development of information technology, the scale and complexity of wireless sensor networks (WSNs) continue to expand. How to effectively locate link failures has become a challenging problem in WSNs. In this paper, we propose a novel method of locating link failures based on distributed cluster consensus protocol and graph decomposition technique. In our method, the initial data is injected into sensor nodes for distributed interactions, and then link failures can be located by observing and comparing the output data of the nodes. The proposed method is suitable for the situations with both single-link failure and multi-link failures, and has no limitations on the number, distribution and correlation of link failures. Necessary and sufficient conditions are provided to guarantee the accuracy of the proposed method in locating link failures. At last, the effectiveness of the proposed method is verified by both real and simulation experiments.

This article addresses the bipartite tracking consensus for a set of mobile autonomous agents over directed cooperation-competition networks. Here, cooperative and competitive interactions among the agents are described by positive and negative edges of the directed network topology, respectively. Both fixed and switching network topologies are considered. For the case with fixed network topology, the matrix product technique is utilized to derive the convergence result. For the case with switching network topologies, some key results related to the composition of binary relations are the main technical tools of analyzing the error system. In addition, the upper bound for the spectral radius of the system matrix is given to ensure the convergence of the system even if the single uncoupled system is strictly unstable. The applicability of the derived results is verified through two simulation experiments.

This article focuses on the issue of scaled tracking consensus for discrete-time second-order multiagent systems under random packet dropouts, where the cases with a static leader and a dynamic leader are considered, respectively. The scaled tracking consensus means that all agents reach a consensus value determined by the leader but with different scales, and the phenomenon of packet dropout on each communication link is described as a Bernoulli variable independent of other communication links. By virtue of random environment-based scaled consensus algorithms, it is shown how to reconstruct the original system into augmented error systems with random coefficient matrices. With the kind assistance of substochastic matrix and super-stochastic matrix, sufficient conditions for the cases with a static leader and a dynamic leader are derived, respectively. Moreover, computer simulations are performed to demonstrate the dynamics of network agents under random packet dropouts.

This paper proposes a new model and its analysis results for time-varying structurally balanced networks. Through model transformations, the system stability problem is converted into the problem of product convergence of infinite sub-stochastic matrices (PCISM). Further, by constructing a new digraph for each interaction topology, the problem of PCISM can be handled by virtue of the properties of row-stochastic matrices. When all leaders belong to only one of the two subgroups, it is shown that the followers that are in the same subgroup as the leaders gradually enter the convex hull formed by the leaders' states, while the others gradually enter the convex hull formed by the leaders' sign-inverted states. And when both subgroups contain leaders, a sufficient algebraic graph condition is established to ensure that all followers can enter the convex hull consisting of the leaders' states and sign-inverted states together. Moreover, it is also found that the followers keep active after entering the convex hulls. Finally, the bipartite containment performance is verified by a simulation test.

Ionospheric heights provided by ionosondes are vital for over-the-horizon radar (OTHR) target tracking. However, biases contained in the provided ionospheric heights definitely cause degradation of the tracking performance. This paper is concerned with the joint estimation problem of the target state and the ionospheric height bias for OTHR target tracking subject to multipath and cluttered measurements. A Markovian jump ionospheric height bias model is presented by simultaneously considering the intermittent and abrupt ionosphere changes. Meanwhile, a set of stochastic variables are adopted to depict association uncertainties among measurements, clutters, and propagation modes so that association is embedded in the resultant measurement model with random coefficients. Through such modeling transformation from association uncertainties to parameter randomness, the coupling processing of data association and state estimate is equivalent to pure state estimate subject to stochastic parameters. Furthermore, an optimal linear joint estimator containing causality constraints is developed in the minimum mean-squared error sense, and further extended to the case of nonlinear measurement model via iterative optimization. A target tracking example with four resolvable propagation modes illustrates the effectiveness of the proposed estimation scheme.

As a key problem for multisensor attitude determination, Wahba's problem has been studied for almost 50 years. Different from existing methods, this paper presents a novel linear approach to solve this problem. We name the proposed method the fast linear attitude estimator (FLAE) because it is faster than known representative algorithms. The original Wahba's problem is extracted to several 1-D equations based on quaternions. They are then investigated with pseudoinverse matrices establishing a linear solution to n-D equations, which are equivalent to the conventional Wahba's problem. To obtain the attitude quaternion in a robust manner, an eigenvalue-based solution is proposed. Symbolic solutions to the corresponding characteristic polynomial are derived, showing higher computation speed. Simulations are designed and conducted using test cases evaluated by several classical methods, e.g., Shuster's quaternion estimator, Markley's singular value decomposition method, Mortari's second estimator of the optimal quaternion, and some recent representative methods, e.g., Yang's analytical method and Riemannian manifold method. The results show that FLAE generates attitude estimates as accurate as that of several existing methods, but consumes much less computation time (about 50% of the known fastest algorithm). Also, to verify the feasibility in embedded application, an experiment on the accelerometer-magnetometer combination is carried out where the algorithms are compared via C++ programming language. An extreme case is finally studied, revealing a minor improvement that adds robustness to FLAE, inspired by Cheng et al.