This study proposes a novel synchronization framework for memristive chaotic systems (MCSs) through an enhanced deep reinforcement learning (DRL) approach, featuring an improved proximal policy optimization (PPO) algorithm. Distinguished from traditional linear/nonlinear control paradigms that necessitate precise mathematical modeling, our DRL-based methodology operates without prior knowledge of system dynamics or analytical model requirements. The developed data-driven control strategy demonstrates significant advantages by reducing the required control forces from four to three dimensions, thereby substantially decreasing control complexity and operational costs compared to conventional item-by-item control methods. Through systematic optimization of the reward function architecture in classical PPO algorithms, we achieve accelerated synchronization convergence rates for MCSs, in which an optimal exponential parameter is obtained accordingly. Finally, the practical efficacy of our DRL-driven synchronization framework is successfully validated in image encryption applications. Comprehensive numerical simulations and comparative analyses demonstrate that the proposed methodology not only maintains robust performance under Gaussian noise perturbations but also achieves synchronization efficiency improvements.

This article investigates the distributed platoon control problem of connected automated vehicles (CAVs) under hybrid cyber attacks, which include false data injection (FDI) and eavesdropping attacks simultaneously. To mitigate the impact of hybrid cyber attacks on vehicle information exchange, an event-triggered dual-layer control strategy with a hidden layer and competitive interconnection structure is proposed. Specifically, to address malicious data injection from FDI attacks, the proposed framework achieves the objective of vehicle platooning through dynamic interaction between the physical and hidden layers only at triggered time instants. Meanwhile, FDI attacks detection and privacy preservation can also be realized through this strategy. Furthermore, a Zeno-free event-triggered mechanism (ETM) is employed to improve the communication efficiency and reduce resource consumption by avoiding unnecessary state transmissions. Sufficient conditions for control parameters are derived to guarantee resilient platoon control objectives under hybrid cyber attacks. Finally, numerical simulations validate the effectiveness of the proposed approach.

This article presents an approach to ensure the robust forward invariance of safe sets for sampled-data input nonlinear dynamical systems with model uncertainties. We first design a continuous-time composite controller structure for the uncertain system by integrating an uncertainty compensation term and a state feedback term. The uncertainty compensation term is generated by a nonlinear observer, while the feedback term is subject to linear constraints on a high order control barrier function (HOCBF) which effectively mitigates the adverse effects of the uncertainty observation error on the safety constraints. Then, inspired by the continuous-time controller, a sampled-data controller is proposed where the feedback control term is obtained by solving a new quadratic program (QP) problem with modified HOCBF constraints to address the challenges posed by sampled-data input. Sufficient conditions are derived to guarantee the robust forward invariance of the safe sets for the sampled-data nonlinear dynamical system. From the simulation experiments, it is demonstrated that the proposed method successfully ensures the safety of the sampled-data input dynamical systems with model uncertainties.

This article investigates the global predefined-time consensus (PTC) of multiagent system (MAS) via constructing a duplex communication network. Unlike the traditional finite-/fixed-time convergence, our method allows the upper-bound of settling-time to be an explicit constant, which is tunable and can be set beforehand without relating with the network information, controlling parameters, and initial conditions. In particular, our approach uses a smooth, nonchattering consensus scheme that avoids conventional discontinuous functions like signum and absolute value functions. By the Lyapunov stability analysis, the sufficient criterion is deduced for ensuring the PTC of MAS. Finally, simulations confirm the effectiveness of our proposed nonchattering scheme.

Learning distributed cooperative policies for large-scale multirobot systems remains a challenging task in the multiagent reinforcement learning (MARL) context. In this work, we model the interactions among the robots as a graph and propose a novel off-policy actor-critic MARL algorithm to train distributed coordination policies on the graph by leveraging the ability of information extraction of graph neural networks (GNNs). First, a new type of Gaussian policy parameterized by the GNNs is designed for distributed decision-making in continuous action spaces. Second, a scalable centralized value function network is designed based on a novel GNN-based value function decomposition technique. Then, based on the designed actor and the critic networks, a GNN-based MARL algorithm named graph soft actor-critic (G-SAC) is proposed and utilized to train the distributed policies in an effective and centralized fashion. Finally, two custom multirobot coordination environments are built, under which the simulation results are performed to empirically demonstrate both the sample efficiency and the scalability of G-SAC as well as the strong zero-shot generalization ability of the trained policy in large-scale multirobot coordination problems.

This article is devoted to solving the leaderless and leader-following consensus tracking problems for a class of disturbed second-order multiagent systems (MASs) under the influence of actuator attacks. To achieve this, a two-step control strategy is developed, where the effects of disturbances and actuator attacks on the achievement of consensus tracking are addressed in distinct stages. In the first step, a reference system model is constructed for each agent. Upon which a sliding mode control (SMC) protocol is constructed and utilized to resolve the consensus tracking problem of disturbed second-order MASs in the absence of actuator attacks, facilitating the design of a baseline control term for the MASs under consideration. In the second step, a secure control policy is trained using an off-policy soft actor-critic algorithm, aiming at achieving secure consensus tracking in the presence of actuator attacks. Both numerical simulations and a multipendulum consensus example verify that the designed control structure has better control performance than using only the SMC method and also effectively improves the training efficiency over the traditional reinforcement learning (RL) alone method.

In this work, we consider the safe deployment problem of multiple robots in an obstacle-rich complex environment. When a team of velocity and input-constrained robots is required to move from one area to another, a robust collision-avoidance formation navigation method is needed to achieve safe transferring. The constrained dynamics and the external disturbances make the safe formation navigation a challenging problem. A novel robust control barrier function-based method is proposed which enables collision avoidance under globally bounded control input. First, a nominal velocity and input-constrained formation navigation controller is designed which uses only the relative position information based on a predefined-time convergent observer. Then, new robust safety barrier conditions are derived for collision avoidance. Finally, a local quadratic optimization problem-based safe formation navigation controller is proposed for each robot. Simulation examples and comparison with existing results are provided to demonstrate the effectiveness of the proposed controller.

A new class of distributed multiagent reinforcement learning (MARL) algorithm suitable for problems with coupling constraints is proposed in this article to address the dynamic economic dispatch problem (DEDP) in smart grids. Specifically, the assumption made commonly in most existing results on the DEDP that the cost functions are known and/or convex is removed in this article. A distributed projection optimization algorithm is designed for the generation units to find the feasible power outputs satisfying the coupling constraints. By using a quadratic function to approximate the state-action value function of each generation unit, the approximate optimal solution of the original DEDP can be obtained by solving a convex optimization problem. Then, each action network utilizes a neural network (NN) to learn the relationship between the total power demand and the optimal power output of each generation unit, such that the algorithm obtains the generalization ability to predict the optimal power output distribution on an unseen total power demand. Furthermore, an improved experience replay mechanism is introduced into the action networks to improve the stability of the training process. Finally, the effectiveness and robustness of the proposed MARL algorithm are verified by simulation.

With the proposal of the intelligent transportation system, vehicular ad-hoc networks have been widely concerned and well-developed. Vehicular ad-hoc network is recognized as a major innovation of the Internet of Things technology. It can collect and analyze traffic data uploaded by vehicles through the network, monitor vehicle state in real-time, provide better driving routes and real-time traffic decisions for vehicles, and improve vehicles’ overall level of intelligent driving. To make vehicles quickly join the vehicular ad-hoc networks through the authentication of identity legitimacy, take into account security and efficiency, and further reduce the calculation and communication consumption in authentication, this paper designs a mutual anonymous authentication and key agreement scheme based on an elliptic curve for the vehicular ad-hoc networks. To complete vehicle authentication and session key establishment, we work with lightweight operations like hash, XOR, and connection in conjunction with the elliptic curve discrete logarithm problem to guarantee the confidentiality of communication data. In the scheme, identity authentication is divided into two types: initial authentication and subsequent authentication. When the vehicle is just on the road, it will use the first roadside unit it encounters for initial authentication. All roadside units that cars on the road come across are then authenticated after the accomplishment of the initial authentication with the first roadside unit. Subsequent authentication is lighter and less computationally complex than initial authentication. Of course, subsequent authentication is based on initial authentication. This paper also analyzes the scheme’s security and uses BAN logic analysis and Proverif simulation to verify the scheme’s security. Additionally, performance analysis is used to demonstrate the scheme’s superiority.

In this article, we consider the distributed formation navigation problem of second-order multiagent systems subject to both velocity and input constraints. Both collision avoidance and connectivity maintenance of the network are considered in the controller design. A control barrier function method is employed to achieve multiple control objectives simultaneously while satisfying the velocity and input constraints. First, a nominal distributed leader-following formation controller is proposed which satisfies the velocity and input constraints uniformly and handles switching communication graphs. A nonsmooth analysis is employed to prove the global convergence of the controller. Then, a topology-based connectivity maintenance strategy using a new notion of the formation-guided minimum cost spanning tree is proposed and the corresponding barrier function-based constraints are derived. The barrier function-based collision-avoidance conditions are also developed. All barrier function-based constraints are then combined to formulate a quadratic programming problem which modifies the nominal controller when necessary to achieve both collision avoidance and connectivity maintenance. Simulation results demonstrate the effectiveness of the proposed control strategy.

This article is devoted to designing distributed adaptive attack-free protocols for the consensus of linear multi-input multioutput multiagent systems under directed graphs, where the appointed-time reduced-order observers are proposed based only upon the relative output information among neighboring agents. One of the distinguishing features of the attack-free protocols lies in the prohibition on information transmission via the communication channel. By viewing the relative control input as the unknown input on the dynamics of each agent, a class of new unknown input observers is introduced with only the relative output measurement involved. The appointed-time estimation of the consensus error is achieved by utilizing jump discontinuity in the observer design and employing the property of the nilpotent matrix. Moreover, a linear transformation is made on the system of consensus error to realize the observer order reduction. Both theoretical analysis and simulation illustration are presented to reveal the effectiveness of the proposed attack-free protocols.

We aim to solve the consensus problem of linear multiagent systems (MASs) with input saturation under directed interaction graphs in this article, where only local output information of neighbors is available for each agent. By introducing the multilevel saturation feedback control approach, a fully distributed adaptive anti-windup protocol is proposed, where a local observer, a distributed observer, as well as an anti-windup observer are separately constructed for each agent to estimate consensus error, achieve consensus for a certain internal state, and provide anti-windup compensator, respectively. A dual protocol is further presented with the distributed observer designed based on the input matrix, which gives a thorough view on the connection between the distributed observer and the anti-windup observer, and provides the opportunity to reduce the order of the controller by designing the integrated distributed anti-windup observer. Then, three types of distributed anti-windup protocols are proposed based on the integrated distributed anti-windup observer, which requires different assumptions. Specifically, the first protocol needs two-hop relay information to generate the local observer to estimate consensus error; the second protocol designs the local observer with absolute output information to estimate the state instead; while the last protocol introduces certain assumption on transmission zero of agents' dynamics to design the unknown input observer to estimate consensus error. All of the protocols are validated by strictly theoretical proof, and are illustrated by performing simulation examples.

In this paper, we study a distributed convex optimization problem with inequality constraints. Each agent is associated with its cost function, and can only exchange information with its neighbors. It is assumed that each cost function is convex and the optimization variable is subject to an inequality constraint. The objective is to make all the agents reach consensus, and meanwhile converge to the minimum point of the sum of local cost functions. A distributed protocol is proposed to guarantee that all agents can reach consensus in finite time and converge to the optimal point within the inequality constraints. Based on the ideas of parameter projection, the protocol includes two decent directions. One makes the cost function decrease, and the other makes agents step forward to the constraint set. It is shown that the proposed protocol solves the problem under connected undirected graphs without using a Lagrange multiplier technique. Especially, all of the agents could reach the constraint sets in finite time and stay in there after. The method could also be used in the centralized optimization problems.

Sequence-to-sequence (Seq2Seq) learning framework has been widely used in many natural language processing (NLP) tasks, including abstractive summarization and machine translation (MT). However, abstractive summarization generates the output in a lossy manner, in comparison with MT which is almost loss-less. We model this by introducing a joint selective mechanism: (i) A selective gate is added after encoding phase of the Seq2Seq learning framework, which learns to tailor the original input information and generates a selected input representation. (ii) A selection loss function is also added to help our selective gate function well, which is computed by looking at the input and the output jointly. Experimental results show that our proposed model outperforms most of the baseline models and is comparable to the state-of-the-art model in automatic evaluations.

In this paper, global leader-following consensus problem is investigated for multiple non-holonomic mobile robots subject to input saturation. A globally bounded distributed controller based on only relative state measurements in local coordinate is designed. Under the assumption that the leader's angular velocity is persistently exciting, sufficient conditions for the achievement of consensus tracking in the closed-loop multi-agent systems are established. Finally, simulation examples are provided to illustrate the analytical results.

This paper studied the role of port state control (PSC) in the integrated management of foreign vessel (IMFV). The integrated management is an important measure for port state authorities to control casualties of foreign vessels. Based on the analysis of IMFV and PSC, the working model of "Port State Control Based on Broad Sense Concept (PSCBSC)" is established and applied into IMFV. The comparative analysis of inspection data shows that: the detention ratio, average deficiencies of PSCBSC are far higher than those of standard PSC, while the characteristics of detentions are significantly different as well. By PSCBSC, the resources of maritime safety can be integrated. It is helpful to improve the situation of IMFV.