Problem Of Multi-agent Systems Research Articles

Optimized Backstepping-Based Containment Control for Multiagent Systems With Deferred Constraints Using a Universal Nonlinear Transformation.

This article investigates an optimized containment control problem for multiagent systems (MASs), where all followers are subject to deferred full-state constraints. A universal nonlinear transformation is proposed for simultaneously handling the cases with and without constraints. Particularly, for the constrained case, initial values of states are flexibly managed to the midpoint between upper and lower boundaries by utilizing a state-shifting function, thus eliminating the initial restriction conditions. By deferred constraints, the state is forced to fall back into the restrictive boundaries within a preassigned time. A neural network (NN)-based reinforcement learning (RL) algorithm is executed under the identifier-critic-actor architecture, where the Hamilton-Jacobi-Bellman (HJB) equation is built in every subsystem to optimize control performance. For actor and critic NNs, updating laws are simplified, since the gradient descent method is performed based on a simple positive function rather than square of Bellman residual error. In view of the Lyapunov stability theorem and graph theory, it is proved that all signals are bounded and the outputs of followers can eventually enter into the convex hull constituted by leaders. Finally, simulations confirm the validity of the proposed approach.

Leader-Following Synchronization Control of Multiagent Systems Under Hybrid Cyber Attacks via Impulsive Control Based on Topology Switching.

This article investigates the leader-following synchronization problem of multiagent systems (MASs) under hybrid cyber attacks, which refers to deception attacks and multichannel independent denial-of-service (DoS) attacks in communication channels. In order to achieve the secure control of MASs under hybrid cyber attacks, a novel impulsive control method based on topology switching is proposed, and a new algorithm for determining impulsive instants is designed. In addition, the cooperative-competitive relationship between agents is also considered, which is more in line with reality. Sufficient conditions for ensuring secure control of MASs and a parametric upper bound on the error vector norm between the agents and the leader are obtained. Finally, the numerical simulation verified the effectiveness of the proposed algorithm.

K-filter-based distributed adaptive bipartite tracking for nonlinear multi-agent systems with unknown control direction

This paper investigates K-filter-based distributed adaptive output-feedback bipartite tracking problem for multi-agent systems with unknown control direction. The agents are partitioned into two groups, where agents belonging to the same group are cooperative while agents belonging to the distinct group are competitive. Firstly, the unmeasurable state variables of the system under unknown control direction are estimated through K-filters. Then, a neighbourhood error is introduced to transform a structurally balanced network into a cooperative network. Based on K-filters, neighbourhood error, Nussbaum-type functions and dynamic surface control, a distributed adaptive output-feedback control protocol is designed to achieve the bipartite tracking. It is shown that all signals in the resulting closed-loop systems are semi-global bounded and the output of each agent can track the reference signal provided that the network is structurally balanced and contains a directed spanning tree. Finally, a numerical example is given to verify the effectiveness of the proposed method.

Event-based finite-time sliding mode consensus tracking control for multiagent systems with actuator failures

This paper investigates the finite-time sliding mode control problem for multiagent systems with actuator failures and external disturbances. Given the presence of unknown nonlinear terms in the controlled multiagent systems, a radial basis function neural network is employed to ensure system robustness. To achieve consensus tracking of sliding mode dynamics within a finite-time, a finite-time integral sliding manifold is proposed. An adaptive law is designed to estimate the unknown fault coefficient, and then a distributed event-triggered adaptive sliding mode fault-tolerant control protocol is developed to deal with external disturbances and actuator faults in multiagent systems, which can effectively reduce the communication bandwidth and enhance reliability against actuator faults. To verify the effectiveness of the proposed control method, a numerical example is provided.

Leader-follower consensus for nonlinear multi-agent systems under directed topology

This paper investigates the consensus problem for multi-agent systems (MASs) under directed topology. The primary objective is to design the distributed control protocol such that all agents can converge to the state of leader. The distributed control protocol is designed, and we derive sufficient conditions by using Lyapunov stability theory to achieve consensus. Theoretical analysis and numerical simulations are provided to verify the effectiveness of the proposed control protocol.

Leader-follower consensus for nonlinear multi-agent systems under directed topology

This paper investigates the consensus problem for multi-agent systems (MASs) under directed topology. The primary objective is to design the distributed control protocol such that all agents can converge to the state of leader. The distributed control protocol is designed, and we derive sufficient conditions by using Lyapunov stability theory to achieve consensus. Theoretical analysis and numerical simulations are provided to verify the effectiveness of the proposed control protocol.

Nearly optimal fixed time sliding mode controller for leader–follower consensus problem with partially unknown nonlinear agents

This paper presents a conceptual framework for addressing the consensus problem in multi-agent systems with unknown nonlinear dynamics using reinforcement learning (RL) based nearly optimal sliding mode controller (SMC). The agents’ dynamics are assumed to have uncertainty and mismatched disturbance. An adaptive fixed-time estimator is introduced to estimate uncertain dynamics and disturbances for each agent at a certain time. The paper proposes two control strategies. In the first strategy, a controller is designed, incorporating adaptive SMC and an optimal controller. Adaption law in SMC estimates the bound of fixed time estimator error before convergence, ultimately achieving asymptotic convergence to the sliding surface and converting the agent’s dynamics to linear. This enables solving a linear consensus problem using an RL-based adaptive optimal controller through an on-policy critic–actor method. The second control strategy enhances the adaptive SMC into a fixed-time controller, reducing the time to reach the sliding surface regardless of initial conditions. Consequently, the convergence time of the consensus error to zero is diminished. This reduction in reaching time results in faster convergence of the consensus error to zero. The effectiveness of both strategies is validated through numerical experiments on two real system models, aligning with the theoretical proofs.

Bipartite containment control of multi-agent systems subject to adversarial inputs based on zero-sum game

In this paper, we investigate bipartite containment control problem of multi-agent systems (MASs) with signed directed graph under adversarial inputs. Firstly, we define the bipartite containment error and establish the equivalence between the bipartite containment error converging to zero and the achievement of bipartite containment control. Subsequently, we prove that the bounded L2-gain bipartite containment problem under adversarial inputs can be reformulated as a multi-player zero-sum differential graphical game problem and can be solved via the solution to the coupled Hamilton-Jacobi-Isaacs (HJI) equation. To address this, we propose a policy iteration (PI) algorithm and prove its convergence under different updating cases. The proposed algorithm is implemented by neural networks (NNs) and a numerical simulation example is provided to show its effectiveness.

Prescribed-time leader-follower consensus and containment control for nonlinear multi-agent systems with event-triggered control protocols

This paper discusses the prescribed-time leader-follower consensus and containment control problems for a class of nonlinear multi-agent systems with external disturbances. First, an event-triggered control protocol with sign function is proposed, which can ensure that all agents achieve consensus within a prescribed time. Second, as the sliding mode control has the advantage of good robustness to external disturbances, an improved event-triggered control protocol is designed by using the integral sliding mode control method and some sufficient conditions are derived for achieving the prescribed-time leader-follower consensus. Moreover, the prescribed-time containment control problem for multi-agent systems with multiple leaders is investigated by using the event-triggered control strategy. It is shown that Zeno behavior can be excluded in the above consensus issues by choosing parameters appropriately. Finally, several numerical examples are given to demonstrate the effectiveness and reliability of the proposed approaches.

Min–max consensus of multi-agent systems in random networks

This paper focuses on the study of the min–max consensus problem for multi-agent systems in random networks. A min–max consensus algorithm is proposed, which incorporates the weighted average of the agent’s state, the maximum value of its neighbors’ states, and the minimum value of its neighbors’ states. The interaction network is composed of a directed random network where edges exist with probability and are independent of each other. Then, the convergence of the min–max consensus algorithm is investigated utilizing max-plus algebra, min-plus algebra, and stochastic analysis theory. Sufficient and necessary conditions are given to ensure that the multi-agent systems achieve min–max consensus almost surely and in mean-square, respectively. Finally, some numerical simulations are conducted to confirm the effectiveness of the proposed consensus algorithm.

Multi-group consensus of multi-agent systems subject to semi-Markov jump topologies against hybrid cyber-attacks

Multi-agent systems are widely used in practice. The existing results on the consensus of multi-agent systems mainly focus on the complete consensus, which can not deal with the situation of the system in the face of multiple tasks. In this paper, the multi-group consensus problem of multi-agent systems with semi-Markov jump topologies against hybrid cyber-attacks is studied. The purpose is to ensure that the multi-agent systems can achieve different consensus goals when they encounter hybrid cyber-attacks, and to solve the problem of time-varying communication topology between agents modeled by the semi-Markov process. Additionally, two types of cyber-attacks are taken into account in a single framework through two independent sets of Bernoulli sequences. Subsequently, a necessary condition is established to guide the system toward achieving the multi-group consensus objective. Lastly, a series of illustrative examples are presented to underscore the validity of the designed controller.

Resilient interval observer-based coordination control for multi-agent systems under stealthy attacks

In this paper, the coordination control problem of multi-agent systems (MASs) subjected to stealthy attacks and uncertainties is considered, where the uncertainties refer to unknown external disturbances and initial states. We consider a scenario in which the sensors are maliciously attacked, rendering the traditional interval observer’s bounds ineffective for estimating the system states. By solving the linear programming problem, the maximum destructive attack sequences are generated. A distributed resilient interval observer composed of a resilient framer and a control protocol is designed, in which the construction of control protocol for each agent depends on the framer information of its own and its neighbors. It is demonstrated by the cooperativity theory and the Lyapunov stability theory that the distributed resilient interval observer can not only realize interval-valued state estimation for each agent but also drive the cooperative behavior of MASs. In addition, the observer gain matrix is selected and optimized by solving a semi-definite programming problem to provide tighter bounds for each agent. Meanwhile, the bounds of stealthy attacks are calculated. Finally, through a simulation example, the superiority of the distributed resilient interval observer and the effectiveness of the resilient interval observer-based consensus control algorithms are validated.

Adaptive expected-time tracking consensus control for multiagent systems with external disturbances and time delays

This paper studies the expected-time tracking control problem for multiagent systems with disturbances and time delays. The expected-time control scheme can ensure that control objectives can be completed within the desired time. In the other words, the output signal can track the reference signal in an expected time. Based on a special error and the adaptive technology, the expected-time tracking consensus control scheme is achieved. Moreover, by using the neural networks technique, the unknown nonlinear functions which often exist in the dynamic of the system are handled. According to the Lyapunov stability theorem and the Lyapunov–Krasovskii function, it is proved that the consensus errors are semi-globally uniformly ultimately bounded. At last, the feasibility of the control method is verified by a simulation example.

Distributed event-triggered collaborative control for multiagent systems against DoS attacks

This paper deals with the event-triggered collaborative control problem of multiagent systems (MASs) under denial-of-service (DoS) attacks. For large-scale MASs, it is difficult to implement fully distributed collaborative control by tuning the coupling strengths of all edges due to the limitation of computing resources in practical applications. To overcome this difficulty, an edge-pinning-based adaptive strategy for coupling strengths is established, under which only the coupling strengths of partial edges are tuned instead of all ones. Under this strategy, the edge-pinning-based resilient distributed event-triggered synchronous and asynchronous adaptive laws are designed, both of which ensure the implementation of security consensus against DoS attacks and the elimination of Zeno behavior. It should be pointed out that the designed two resilient consensus strategies are fully distributed. Additionally, under these event-triggered strategies, the updating of control protocols and the monitoring of triggering functions do not require continuous communications, thus reducing the communication burden among agents. Finally, simulations conducted on a network composed by six aircrafts are provided to illustrate the theoretical results.

Dynamic Multi-Target Self-Organization Hunting Control of Multi-Agent Systems

In this paper, we present a novel coordinated method tailored to address the dynamic multi-target hunting control problem in multi-agent systems, offering significant practical value. Our approach encompasses several key components: initially, we introduce a task allocation model that integrates a fuzzy inference system with a particle swarm optimization algorithm. This hybrid model efficiently allocates hunting tasks for scattered evading targets, effectively transforming the dynamic multi-target hunting problem into multiple dynamic single-target-hunting problems. This transformation enhances the speed and efficacy of task allocation. Subsequently, we propose an attraction/repulsive model grounded in potential field theory. This model facilitates the coordinated hunting of each target by organizing agents into subgroups. Relying solely on relative position and velocity information between agents and targets, our model simplifies computation, while maintaining effectiveness. Furthermore, the coordination of hunting activities for each target is achieved through a series of agent subgroups, guided by our proposed motion model. This systematic approach ensures a cohesive and efficient hunting strategy. Finally, we validate the effectiveness and feasibility of our proposed method through simulation results. These results provide empirical evidence of the method’s efficacy and potential applicability in real-world scenarios.

Event-triggered predictive control for cooperation-competition multi-agent systems under DoS attacks

This paper concerns the bipartite consensus problem of multi-agent systems(MASs) with competitive- cooperative network topology under denial-of-service (DoS) attacks. Firstly, this work extensively analyzes the competitive phenomena that may exist in the information interchange of agents in contrast to the single cooperative behavior between agents. Based on this, some necessary conditions are provided for the system to attain the bipartite consensus. In addition, the event-triggered mechanism (ETM) effectively lowers unnecessary information sharing between agents and eliminates Zeno behavior. Furthermore, the predictive method provides the system with exceptional resistance against common energy-limited DoS attacks and the ability to compensate for information loss caused by DoS attacks. Finally, a numerical simulation proves that the proposed approach is feasible.

Robust Distributed Containment Control with Adaptive Performance and Collision Avoidance for Multi-Agent Systems

This paper deals with the containment control problem for multi-agent systems. The objective is to develop a distributed control scheme that leads a sub-group of the agents, called followers, within the convex hull that is formed by the leaders, which operate autonomously. Towards this direction, we propose a twofold approach comprising the following: (i) a cyber layer, where the agents establish, through the communication network, a consensus on a reference trajectory that converges exponentially fast within the convex hull of the leaders and (ii) a physical layer, where each agent tracks the aforementioned trajectory while avoiding collisions with other members of the multi-agent team. The main contributions of this work lie in the robustness of the proposed framework in both the trajectory estimation and the tracking control tasks, as well as the guaranteed collision avoidance, despite the presence of dynamic leaders and bounded but unstructured disturbances. A simulation study of a multi-agent system composed of five followers and four leaders demonstrates the applicability of the proposed scheme and verifies its robustness against both external disturbances that act on the follower model and the dynamic motion of the leaders. A comparison with a related work is also included to outline the strong properties of the proposed approach.

Guaranteed-performance consensus for multi-agent systems with Lipschitz nonlinearity

This paper mainly addresses the guaranteed-performance consensus problem of multi-agent systems (MASs), under the energy constraints and Lipschitz nonlinear dynamics. First, to reach consensus under energy constraints, the consensus protocol and the quadratic performance function are proposed. Afterward, by using the properties of nonsingular transformation matrix and Lipschitz’s nonlinear representation, the consensus function conditions are expressed. Moreover, considering the guaranteed-performance cost, this paper designs the quadratic performance function derived from the Riccati inequality and Lyapunov–Krasovskii theorem. Eventually, numerical simulations are implemented to validate the theoretical results.

Reward function design method to achieve system-level objectives in ambulance diversion problem

This study addresses a reward function design problem in multi-agent systems, using ambulance diversion (AD) problem in emergency medical services (EMS) as an application. While Emergency Departments (EDs) may declare AD to alleviate overcrowding, excessive use of this strategy by individual EDs can have negative consequences, such as limited access and delayed care for emergency patients. To eliminate negative consequences caused by individual decisions, we propose a reward function design method to align individual agents’ decisions with socially optimal strategies. This method ensures that an equilibrium status is achieved under socially optimal operation by removing incentives for deviations from socially optimal behavior while maximizing system welfare without extra investment. The proposed method offers an exact solution for designing reward function of a stochastic game model with finite state and action spaces. We conduct numerical experiments for a 2-ED system and demonstrate that the designed reward function properly guides the EDs toward a socially optimal AD strategy.

Containment control for second-order multi-agent systems with time-varying delays via variable-augmented-based free-weighting matrices

This paper investigates the containment control problem for multi-agent systems (MASs), where the time-varying communication delays are considered. The aim of this paper is to introduce new methods for the containment control research of MASs with time-varying delays. To obtain better results on maximum admissible upper bounds (MAUBs) of the delays to ensure the containment control, the variable-augmented-based free-weighting matrices (VABFWMs) method is introduced to avoid the higher-order terms of time-varying delays in estimating the derivative of the Lyapunov-Krasovskii functionals (LKFs). So our method no longer requires the quadratic function negative-determination lemma. In addition, by utilizing the proposed method, we design a containment controller to ensure that all followers are included into the convex hull formed by leaders. Finally, a numerical example is presented to illustrate the feasibility and superiority of our method.