This paper focuses on the Nash Equilibrium (NE) seeking problem in cyber-physical systems (CPSs), where malicious nodes can arbitrarily iterate their actions and estimates within constrained bounds and broadcast such information to their neighboring nodes in the IoT network. Consequently, the NE seeking process of legitimate agents is highly susceptible to malicious interferences, as these agents update their actions and estimates based on information received from neighboring nodes and their own local cost functions. To address this critical issue in IoT scenarios, we build on existing research and propose a resilient NE-seeking algorithm based on physical layer trusted observations tailored for multi-agent systems. Specifically, we put forward a resilience-oriented definition of nominal NE that is adapted to IoT environments, and rigorously prove the convergence of both actions and estimation values of legitimate agents. Finally, numerical simulations are conducted to validate the correctness and effectiveness of our theoretical analysis. Furthermore, we carry out a real-world experiment on a quadrotor UAV swarm system, a typical IoT multi-agent application scenario, to translate our theoretical results into practical IoT applications.

The performance of a vision simultaneous localization and mapping (SLAM) system based on hand-crafted features degrades significantly in harsh environments due to unstable feature tracking. With the breakthrough of convolutional neural networks in deep feature extraction tasks, many researchers have tried to incorporate them into SLAM systems. However, it’s challenging to guarantee the real-time performance of the entire SLAM system, and the erroneous usage scenarios limit the superior performance of deep feature extraction methods. To overcome these problems, we propose a visual-inertial SLAM system with multi-level detector and descriptor, called VINS-MLD2. In our framework, we first design an efficient deep feature extraction network that has the same performance as R2D2 by concatenating multi-level features, but runs 3 times faster under the image resolution commonly used in SLAM. Then, based on the camera baseline, we introduce the Matching Fusion, a matching method that fuses deep descriptor matching and optical flow matching results to improve matching accuracy for both short and wide baselines. In addition, an adaptive matching strategy is proposed to balance the running time and accuracy by adaptively adjusting the matching method. Experimental results in unmanned aerial vehicle (UAV) deployments and real-world environments demonstrate that the proposed method tracks features more stably and accurately. The code is public at https://github.com/dongdong-cai/VINS-MLD2.

In this article, we investigate the problem of unmanned aerial vehicles (UAVs) formation tracking in the presence of multiple cyber-threats. In this scenario, UAVs have private, potentially conflicting objectives, and their communication networks and local feedback mechanisms are vulnerable to sabotage and eavesdropping by malicious attackers. To address this real-world challenge, we propose a control scheme based on a twin-network structure. Specifically, we construct a virtual twin layer interconnected with the physical layer to design a resilient estimator that fortifies information exchange among UAVs under threats. In addition, by coupling the states from the twin layer and time-varying signals as masks, we achieve privacy protection for critical information. Furthermore, leveraging reliable data provided by the resilient estimator, we design a cooperative controller based on gradient-optimization to update the UAVs' positions. Using Lyapunov theory, we prove that the position of all UAVs converge to a dynamic Nash equilibrium. Finally, we conduct experimental studies to validate the effectiveness of the proposed control scheme.

A distributed multiagent deep reinforcement learning algorithm (DMADRLA) with theoretical guarantees is proposed for the distributed nonconvex constraint optimization problem. This algorithm provides an innovative theoretical framework for distributed nonconvex optimization problems (DNCOPs) by combining traditional distributed constraint optimization and multiagent deep reinforcement learning methods. This combination eliminates the need for general assumptions on the cost function, enabling a more comprehensive view of distributed nonconvex optimization strategies. It allows for the analysis of both traditional distributed constrained optimization and multiagent deep reinforcement learning methods in one unified approach. Finally, the effectiveness of the algorithm is verified through numerical simulations and experimental verification.

This article investigates noncooperative formation control in the presence of malicious agents who disseminate negative information to their neighbors, resulting in misbehavior. A local cost function for agents is introduced to transform the formation control problem into a noncooperative game problem such that the Nash equilibrium solution corresponds to the agents' decisions in the desired formation. The resilient game strategy is proposed, comprising an event-triggered game algorithm and a threshold-based malicious agents detection algorithm, enabling each agent to determine the malice of their neighbors at each trigger moment and selectively interact with them, which guarantees that all normal agents can achieve Nash equilibrium, and the stability is analyzed by Lyapunov theory. Finally, the effectiveness of the algorithms is verified through a multi-unmanned aerial vehicles formation control experiment.

This article investigates the distributed Nash equilibrium (NE) seeking problem of uncertain multiagent systems in unreliable communication networks. In this problem, the action of each agent is subject to a class of nonlinear systems with uncertain dynamics, and the communication network among agents will be affected by the nonperiodic denial of service (DoS) attacks. Note that, in this insecure network environment, the existence of DoS attacks will directly destroy the connectivity of the network, which leads to performance degradation or even failure of the most existing distributed NE seeking algorithms. To address this problem, we propose a two-stage distributed NE seeking strategy, including the attack-resilient distributed NE estimator and the neuroadaptive tracking controller. The estimator based on the projection subgradient method and the consensus protocol can converge exponentially to virtual NE against DoS attacks. Then, the neuroadaptive tracking controller is designed for uncertain multiagent systems with the output of the estimator as the reference signal such that the actual action of all agents can reach NE. Based on the Lyapunov stability theory and improved average dwell time automaton, the stability of the estimator and the controller is proven, and all signals in the closed-loop system are uniformly bounded. Numerical examples are presented to verify the effectiveness of the proposed strategy.

In this article, we investigate the problem of seeking Nash equilibrium (NE) in multiagent systems within cooperation–competition networks. Each agent aims to optimize a total cost function that accounts for its own interests as well as those of its cooperators, considering both cooperative and noncooperative interactions with other agents. It is worth noting that, unlike in existing N-coalition games, the agents in this study only have knowledge of whether they are cooperative or noncooperative with their neighboring agents; they do not have information about non-neighboring agents who might be cooperators. As a result, due to potential disconnections in the communication topology within the cluster, it is not possible to consider the entire cluster as a virtual player to optimize its objective functions. To address this issue, we developed an algorithm using the singular perturbation technique, which divides the system into two distinct timescales. We propose a novel estimation algorithm to estimate the total cost function of disconnected subnetworks within the fast system. In the slow system, the search for NE is based on a gradient algorithm, while the Lyapunov stability theory is utilized to analyze the convergence of the algorithms. Furthermore, we extend the problem to accommodate scenarios where multiple subnetworks exist within the network. Numerical simulations are conducted to demonstrate their capability for resolving the noncooperative game problem in cooperation–competition networks.

In this article, we investigate the distributed Nash equilibrium (NE) seeking problem for the multi-cluster game under switching communication topologies. Specifically, the communication topology switches between a group of jointly connected digraphs. First, a new distributed NE seeking algorithm for the multi-cluster games is designed by the consensus protocol and gradient play rule under the switching communication topologies. Furthermore, in order to make the algorithm still applicable when the agent only knows part of the decision information, the leader-following consensus protocol is used to generate the estimates for all agents action in the cluster under the assumption that the switching topology between clusters is directed and strongly connected. A more general NE seeking algorithm for the multi-cluster games is designed. For these two algorithms, the results of local convergence and non-local convergence are given, respectively. Two examples verify the validity of the theoretical results.

This paper investigates resource allocation problems, where the cost functions of agents are nonsmooth and the decisions of agents are constrained by heterogeneous local constraints and network resource constraints. We design a distributed subgradient-based algorithm to achieve the optimal resource allocation. Moreover, we analyze the convergence of the algorithm to the optimal solution. The algorithm can solve resource allocation problems with strongly convex cost functions and weight-balanced digraphs, as well as resource allocation problems with strictly convex cost functions and connected undirected graphs. With the algorithm, the decisions of all agents asymptotically converge to the optimal allocation. Simulation examples verify the effectiveness of the algorithm.

In this paper, two aggregative games over weight-balanced digraphs are studied, where the cost functions of all players depend on not only their own decisions but also the aggregate of all decisions. In the first problem, the cost functions of players are differentiable with Lipschitz gradients, and the decisions of all players are coupled by linear coupling constraints. In the second problem, the cost functions are nonsmooth, and the decisions of all players are constrained by local feasibility constraints as well as linear coupling constraints. In order to seek the variational generalized Nash equilibrium (GNE) of the differentiable aggregative games, a continuous-time distributed algorithm is developed via gradient descent and dynamic average consensus, and its exponential convergence to the variational GNE is proven with the help of Lyapunov stability theory. Then, another continuous-time distributed projection-based algorithm is proposed for the nonsmooth aggregative games based on differential inclusions and differentiated projection operations. Moreover, the convergence of the algorithm to the variational GNE is analyzed by utilizing singular perturbation analysis. Finally, simulation examples are presented to illustrate the effectiveness of our methods.

This paper studies the balance problems of neutral-point potential in three level neutral-point clamped inverter. Based on double modulation wave pulse control strategy, a new offset voltage algorithm is proposed. This control strategy can completely eliminate the low-frequency fluctuations in neutral-point potential. The size of neutral-point current is changed by changing the zero-state duty cycle per phase such that the balance of neutral-point potential is achieved. Considering the direction of maximum current, the direction of neutral-point potential fluctuation and the limitation of dynamic critical range, the best compensation is chosen to balance neutral-point potential quickly. The proposed control strategy is verified by the simulation results.

In this paper, the problem of the stability and stabilization analysis for linear neutral time-delay systems is investigated. The time-delays considered here are assumed bounded but no information to be available. Using Lyapunov functional method, both delay-dependent and delay-independent conditions for the stability and stabilization of the systems are presented in terms of linear matrix inequality (LMI). Examples are given to illustrate the main result of this paper and compare with the results presented in literatures.

The problems of robust stability and robust H/sub /spl infin// control for a class of uncertain neutral systems with time-varying delay are investigated. The class describes linear state models with norm-bounded uncertain system parameters and time-varying delay. First, a sufficient condition for robust stability independent of time-varying delay is developed. Then, a sufficient condition for designing a memoryless state-feedback controller which stabilizes the uncertain neutral system under consideration and guarantees an H/sub /spl infin//-norm bound constraint on the disturbance attenuation for all admissible uncertainties is derived. In both problems, the results are expressed in the form of LMI.