Industrial Internet of Things (IIoT) systems are vulnerable to cyber-attacks due to their interconnected nature. While federated learning (FL) offers a privacy-preserving approach to intrusion detection, it struggles with data heterogeneity, class imbalance, and adversarial attacks in practical IIoT settings. To address these challenges, this paper introduces HGDS, a FL-based framework that integrates a federated diffusion-based sampler for class rebalancing with a dynamic hypernetwork grouping mechanism for model personalization. The proposed framework further incorporates robust defense strategies, including gradient clipping and anomaly-based client isolation, to mitigate adversarial attacks. Comprehensive evaluations are conducted on four real world datasets. The results demonstrate the superiority of HGDS, which achieves a performance gain of up to 15.3% in F1-score over state-of-the-art benchmarks under non-IID data conditions. Furthermore, the method maintains resilience against both model poisoning and label flipping attacks.

This paper investigates the secure cooperative control problem of second-order nonlinear multi-agent systems (MASs) under denial-of-service (DoS) attacks based on dynamic event-triggered schemes. First, a dynamic event-triggered control protocol is designed, which can adaptively adjust the triggering threshold according to the state information of MASs, it can also reduce the number of triggering times and save the communication resources. Then, based on the parameters of control protocol, intensity of DoS attacks, dynamics of MASs, and communication topologies, some sufficient conditions are derived for MASs to guarantee consensus under DoS attacks. In addition, it is proved that under the proposed dynamic event-triggered schemes and control protocol, the MASs can achieve consensus, and the event-triggered schemes can exclude Zeno behavior. Finally, numerical simulation example is presented to illustrate the effectiveness of the main results.

This article investigates the distributed estimation problem of an unknown high-dimensional sparse state vector for a stochastic dynamic system. The communication topology randomly switches, and the switching law is governed by a time-homogeneous Markovian chain. By means of the compressed sensing (CS) theory and a diffusion strategy, we propose a compressed distributed Kalman filter (CDKF). That is, each sensor first compresses the original high-dimensional regression data. Then, the covariance intersection fusion rule is utilized to obtain a distributed Kalman filter (DKF) estimate in the compressed low-dimensional space. Afterward, the original high-dimensional sparse state vector can be well recovered by a reconstruction technique. In terms of stability analysis, one of the main difficulties lies in analyzing the product of nonindependent and nonstationary random matrices in the context of time-varying communication topologies. Relying on the stochastic stability theory, the Markov chain theory, and the CS theory, we establish the upper bound for the estimation error under the compressed cooperative excitation condition, which is much weaker than the traditional uncompressed collective observability conditions used in the existing literature. Finally, we provide a simulation example to illustrate the performance of the proposed algorithm.

This paper explores the optimal containment control problem for nonlinear and underactuated quadrotors with multiple team leaders governed by nonlinear dynamics, employing the reinforcement learning. A cascade controller is formulated, comprising a position control component to ensure containment achievement and an attitude control component to govern rotational channel. The proposed optimal control protocols derived from historical data collected from quadrotor systems without requirement for exact knowledge of vehicle dynamics. The simulation illustrates the effectiveness of the proposed controller in managing a quadrotor team with multiple leaders.

This paper considers Stackelberg equilibrium (SE) and Nash equilibrium (NE) computation in a class of non-convex network aggregative games with one leader and multiple followers. The cost function of each follower is influenced by its strategy, the leader’s strategy, and its neighbors’ aggregative strategies. Also, the structured non-convex cost function of the leader is the composition of a canonical function and a vector-valued geometrical operator that relies on its strategy and followers’ strategies. In the leader-follower scheme, when the leader has knowledge of the best responses of the followers in a closed form, the SE strategy will be the optimal choice due to its relatively low cost. When the leader does not know the exact expression of followers’ best responses or the leader’s dominance is threatened, NE will be what all players are committed to achieving. The widespread existence of nonconvexity creates a significant challenge for computing the above equilibria in different circumstances. The results in existing convex games are not directly applicable to such a non-convex case, as they get trapped in local equilibria or stationary points rather than global equilibria. Here, we adopt the canonical transformation to reformulate the non-convex games and present the existence condition based on the canonical duality theory. Then two projection gradient algorithms are designed to pursue the SE and the NE, followed by proving the convergence of the algorithms.

This article addresses the problem of distributed Nash equilibrium seeking over networks for games with finite action sets. Gradient-like and consensus-based methods commonly used for continuous action spaces fail to work for this case. To this end, we propose a utility decoupling method to reformulate the original game into an augmented game, which preserves the Nash equilibrium and weakly acyclic property, yet enjoys a utility coupling network the same as the communication network. In this way, a variety of full-information game-theoretic learning dynamics for the augmented game turns into partial-information Nash equilibrium seeking dynamics for the original game. We proceed to apply the developed utility decoupling method to formulate three types of distributed Nash equilibrium seeking dynamics, including distributed best-response dynamics, distributed fictitious play, and distributed regret matching for weakly acyclic games. In the last, a typical color assignment game is utilized to empirically illustrate the validity and effectiveness of our approach.

The problem of seeking Nash equilibrium (NE) based on aggregative games under quantization constraints is full of challenges. Although the NE seeking algorithm in continuous-time systems has been studied, this problem in discrete-time systems still needs to be solved urgently. To address this problem, three distributed algorithms are first proposed under three quantization cases, adaptive, random, and time-varying quantizations, based on doubly stochastic communication topology networks. Then, the actions of players would eventually converge to NE under the conditions of vanishing step size and strong monotonicity are proved. Moreover, the convergence rate of the three quantization cases are analyzed, respectively. Finally, numerical experiments are implemented on plug-in hybrid electric vehicles (PHEVs) to validate the effectiveness of the proposed distributed algorithms. Comparing the convergence rates of the three proposed algorithms, the convergence effect of the adaptive quantization is better than that of the other two quantization cases.

This paper investigates the coordination control problem for a special nonlinear heterogeneous multi-agent system consisting of tail-sitter unmanned aerial vehicles and unmanned ground vehicles with uncertainties and disturbances. A robust hierarchical pinning control scheme is proposed for the heterogeneous multi-agent system to restrain the uncertainties and disturbances and achieve coordination scenarios. The heterogeneous multi-agent system can realize coordination tasks by selecting proper pinning nodes and estimating coupling strength. The robustness of the whole system is proven utilizing the Lyapunov stability theorem. The effectiveness of the robust hierarchical pining control method is validated by simulation scenarios.

Using reinforcement learning (RL) algorithm to optimize guidance law can address non-idealities in complex environment. However, the optimization is difficult due to huge state-action space, unstable training, and high requirements on expertise. In this paper, the constrained guidance policy of a neural guidance system is optimized using improved RL algorithm, which is motivated by the idea of traditional model-based guidance method. A novel optimization objective with minimum overload regularization is developed to restrain the guidance policy directly from generating redundant missile maneuver. Moreover, a bi-level curriculum learning is designed to facilitate the policy optimization. Experiment results show that the proposed minimum overload regularization can reduce the vertical overloads of missile significantly, and the bi-level curriculum learning can further accelerate the optimization of guidance policy.

This paper investigates the formation control problem for linear multi-agent systems under switching directed topologies. Based on absolute or relative outputs, we propose two distributed observer-based event-triggered control schemes. Both schemes can guarantee the boundedness of formation errors under sufficient conditions. The schemes can also avoid Zeno behaviors by giving an estimation for the lower bound of sampling intervals. Finally, simulations and experiments validate the proposed approaches.

With the rapid development of swarm intelligence, the consensus of multiagent systems (MASs) has attracted substantial attention due to its broad range of applications in the practical world. Inspired by the considerable gap between control theory and engineering practices, this article is aimed at addressing the mean square consensus problems for stochastic dynamical nonlinear MASs in directed networks by designing proportional-integral (PI) protocols. In light of the general algebraic connectivity, consensus underlying PI protocols for a directed strongly connected network is investigated, and due to the M -matrix approaches, consensus with PI protocols for a directed network containing a spanning tree is studied. By constructing appropriate Lyapunov functions, combining with the stochastic analysis technique and LaSalle's invariant principles, some sufficient conditions are derived under which the stochastic dynamical MASs realize consensus in mean square. Numerical simulations are finally presented to illustrate the validity of the main results.

Over the past decades, the synchronization of complex networks with directed topologies has received considerable attention owing to its extensive applications in the realistic world. Design of proportional-integral-derivative (PID) control protocols for achieving synchronization with directed networks is known to be a challenging task. The purpose of this paper is to establish a connection between the PID control protocols and synchronization of complex dynamical networks with directed topologies. Based on the classical complex network model, we investigate global synchronization with PD controller of a balanced strongly connected directed network and global synchronization with PI controller of a strongly connected directed network, and a directed network containing a spanning tree, respectively. Several sets of sufficient conditions are established under which the network reaches global synchronization. The simulation examples are presented to verify the efficiency of the theoretical results.

Over the course of the past two decades, a great deal of researchers has investigated synchronization of networked systems with determined node dynamics. However, in many realistic situations, noise is inevitable in man-made and naturally occurring networked systems. Therefore, dynamical networked systems with stochastic perturbations have gained substantial attention and have been extensively studied both in theoretical research and in practical applications. The primary goal of this paper is focused on the novel synchronization protocol design problem of nonlinear stochastic dynamical networked systems. Sufficient conditions are given to select protocol parameters. By employing stochastic analysis techniques and selecting appropriate Lyapunov functions, we proved that global synchronization of nonlinear stochastic dynamical networked systems can be reached in mean square. Numerical examples are depicted to demonstrate the efficiency of the theoretical results.

With the development of swarm intelligence in last decades, consensus problem of multi-agent systems has been attracting much attention. To reveal the inherent mechanism of leader-following consensus in multi-agent systems with stochastic dynamics, PI control protocols are designed in this paper. Owing to the stochastic analysis techniques, algebraic graph theory, and constructing appropriate Lyapunov function, it is proved that the leader-following consensus of nonlinear stochastic dynamical multi-agent systems can be reached in mean square. Sufficient condition are deduced to select the PI control protocol parameters. Finally, the theoretical results are demonstrated through a simulation example.

This article addresses the leader-following consensus of a class of multi-agent systems (MASs) subjected to saturation. Unlike previous literature, the followers are with heterogeneous dynamics. To solve this problem, we employ the low-gain feedback technique and the parameterized algebraic Riccati equations to design the controllers. For the fixed and switching network topologies, sufficient conditions are put in place to guarantee the semiglobal stability of the consensus error system. Numerical results are also provided to validate the effectiveness of the control design.

Based on the classical network model, this paper investigates the PD and PI control for synchronization of the complex dynamic directed network with delayed nodes. We obtain some sufficient conditions for global synchronization. In particular, using derivative and integral protocols are better than using traditional protocols in achieving synchronizaiton. Finally, some simulation examples are given to theoretical results.

Over the last two decades, synchronization, as a typical collective behavior in complex networks, has received an increasing attention. To reveal the inherent mechanism of synchronization in complex networks with delayed nodes, this paper aims at developing a novel synchronization approach by using the PD control strategy. Based on a classical network model, we investigate the synchronization of complex networks with delayed nodes under PD control strategy and obtain several sufficient conditions for global synchronization. In particular, the addition of derivative action may make the network achieve synchronization better than the traditional strategies. Finally, a simulation example is provided to verify the effectiveness of the proposed theoretical results.

Complex networks are ubiquitous in nature and society. The functions and features of complex networks are various when these networks have different nodal dynamics and network topologies. Reconstructing networks with high-order nodal dynamics or different system parameter vectors from limited measurable information is a fundamental problem for using and controlling these networks. Based on the Lasso method, we present an efficient and feasible, completely data-driven approach to predict the structures of complex networks in the presence or absence of noise when the systemic parameter is uncertain, that is, the node dynamical parameter vector of network can vary. The numerical simulations indicate that, networks structures can be fully reconstructed even only few information available under the conditions of the systemic parameter vector is varying and in the presence or absence of noise, this method is effective and robust.