Problem Of Multi-agent Systems Research Articles

Distributed Model-Predictive Control Using Exponentially Weighted Laguerre Functions and Periodic Time Triggering for Dynamic Consensus of Linear Multiagent Systems

Model Predictive control (MPC) is becoming increasingly popular in addressing the consensus problem of multi-agent systems (MAS) due to its ability to manage constraints in a multivariable control framework and to perform online optimization. However, in order to achieve the desired closed loop performance, longer prediction and control horizons are essential for conventional MPC. This increases the computational complexity and causes the numerical instability of the optimization problem. This concern is addressed in this article by introducing an exponential-data-weighted Laguerre-based control design that minimises computational burden, improves numerical conditioning, and provides a specified degree of closed loop stability of the MAS. The proposed design which incorporates an embedded integrator model, rejects constant disturbances, avoiding the need for a separate disturbance model. Furthermore, periodic time-triggered control (PTTC) is incorporated with the consensus algorithm, enabling a relaxation in the over-usage of resources. An explicit formula for computing the upper bound of triggering interval is also derived. The proposed algorithm outperforms current methods in terms of numerical conditioning, stability margins, and rejection of deterministic disturbances. Validation of the proposed strategy is performed through simulation of dynamic consensus on a cluster of grounded vehicles.

Application-Oriented Homogeneous Control Protocol Design for Multi-Agent Systems Under Input Constraints

An application-oriented homogeneous control protocol is proposed to solve the consensus problem of multi-agent system (MAS) modeled by higher-order integrator. This nonlinear control protocol is able to homogenize the linear system with a special degree called homogeneity degree. This homogeneous control protocol ensures asymptotically/finite-time stable multi-agent systems (MASs) (or fixed-time attractive to compact sets containing the origin) by selecting different homogeneity degrees. If the linear control protocol for each agent is provided, the proposed nonlinear homogeneous control protocol in this paper can be easily implemented without requiring any tuning of the control parameters. A bounded homogeneous control protocol, which is a special form of the controller proposed, is also introduced to address the same problem with input constraints. Finally, numerical simulations are conducted to demonstrate the effectiveness of the proposed approach.

Multiple Subformulae Cooperative Control for Multiagent Systems Under Conflicting Signal Temporal Logic Tasks

This paper studies the temporal logic problem for multi-agent systems, where each agent is subject to signal temporal logic (STL) tasks with multiple sub-tasks. In the distributed framework, due to the existence of coupling sub-tasks, the satisfaction of the conjunction of all sub-tasks may be conflicting. A two-step distributed model predictive control (DMPC) strategy is proposed to maximize the amount of the satisfied sub-tasks and minimize the violation degree of those failed sub-tasks. In step one, a novel robustness metric of STL is proposed to measure whether each sub-task is satisfied or not, and is directly incorporated into the DMPC optimization problem to determine the satisfiability of each sub-task. Based on the planning results of step one, a DMPC optimization problem with a short planning horizon is designed in step two to minimize the violation degree of the unsatisfiable sub-tasks while ensuring the satisfaction of those satisfiable ones. All agents solve their two-step DMPC problems sequentially to realize the satisfaction verification of the coupled sub-tasks at each time instant. Further, the soundness and feasibility of the two-step DMPC algorithm are formally guaranteed. Simulations and experiments illustrate the effectiveness of the proposed algorithm.

Transmission-Constrained Consensus of Multiagent Networks

This paper studies the consensus problem for multiagent systems with transmission constraints. A novel model of multiagent systems is proposed where the information transmissions between agents are disturbed by irregular distortions or interferences (named transmission constraint functions), and this model is universal which can be applied in many cases, such as interval consensus and discarded consensus. In the transmission-constrained consensus problem, we obtain the necessary and sufficient condition that agents can converge to state consensus. Furthermore, a more general case is studied in which the system reaches an equilibrium. Based on some techniques of stability theory, the existence, uniqueness and stability of the system equilibrium point can be proven, which means the system can reach an asymptotically stable equilibrium. Moreover, the state values of the equilibrium are only decided by the network structure and transmission constraint functions, but not the agents' initial states. Finally, numerical simulations are presented to illustrate the proposed theorems and corollaries.

Improving Sample Efficiency of Multiagent Reinforcement Learning With Nonexpert Policy for Flocking Control

Control algorithms of a multi-agent system (MAS) have been applied to many Internet-of-Things devices, such as unmanned aerial vehicles and autonomous underwater vehicles. Flocking control is a crucial problem in MAS to enhance the safety and cooperativity of agents, which requires the agents to maintain the flock when navigating to a target position and avoiding collisions. In comparison with traditional algorithms, methods based on multi-agent reinforcement learning (MARL) can solve the problem of flocking control more flexibly and adapt to more complex environments. However, MARL-based methods demand a huge number of interactions between agents and the environment, resulting in the problem of sample inefficiency. In this paper, we propose Non-expert Policy Aided MARL (NPA-MARL) to improve sample efficiency, which utilizes a fundamental MARL algorithm and a prior policy whose performance can be non-expert. Before online MARL training, NPA-MARL generates demonstrations by the non-expert policy to pretrain agents, while preventing overfitting demonstrations. During online training, NPA-MARL instructs agents to imitate the non-expert policy if the non-expert policy is better in agents’ recognition. We leverage NPA-MARL to solve the problem of flocking control. Experimental results show that NPA-MARL improves sample efficiency and policy performance in flocking control. Besides, NPA-MARL has the scalability of more agents and the flexibility of choice of the non-expert policy and fundamental MARL algorithm.

Team-based fixed-time containment control for multi-agent systems with disturbances

We investigate the fixed-time containment control (FCC) problem of multi-agent systems (MASs) under discontinuous communication. A saturation function is used in the controller to achieve the containment control in MASs. One difference from using a symbolic function is that it avoids the differential calculation process for discontinuous functions, which further ensures the continuity of the control input. Considering the discontinuous communication, a dynamic variable is constructed, which is always non-negative between any two communications of the agent. Based on the designed variable, the dynamic event-triggered algorithm is proposed to achieve FCC, which can effectively reduce controller updating. In addition, we further design a new event-triggered algorithm to achieve FCC, called the team-trigger mechanism, which combines the self-triggering technique with the proposed dynamic event trigger mechanism. It has faster convergence than the proposed dynamic event triggering technique and achieves the tradeoff between communication cost, convergence time and number of triggers in MASs. Finally, Zeno behavior is excluded and the validity of the proposed theory is confirmed by simulation.

Distributed fixed-time and prescribed-time average consensus for multi-agent systems with energy constraints

This paper is devoted to the investigation of the fixed-time and prescribed-time average consensus problem for multi-agent systems (MASs) with energy constraints. For the purpose of the fixed-time and prescribed-time average consensus of MASs, two control algorithms are firstly developed in this paper on the basis of the fixed-time technique and the prescribed-time technique. Then, the time integral of the quadratic function is constructed to calculate the energy consumption by the error between each agent and the reference state. And the sufficient conditions for the stability of the Lyapunov function are provided by designing the parameters and the gain matrix. Ultimately, the theoretical results are effectively and correctly verified by utilizing simulations.

Fully Distributed Algorithm for Resource Allocation Over Unbalanced Directed Networks Without Global Lipschitz Condition

This note investigates the distributed optimal resource allocation problem of multi-agent systems over unbalanced directed networks under the relaxed condition that the gradients of local cost functions are locally Lipschitz. The objective is to cooperatively drive the decision variables of the agents to the optimal solution, which minimizes the sum of the local cost functions, while ensuring that the network resource constraints and local feasibility constraints are satisfied. A novel distributed algorithm is developed over unbalanced directed network topologies based on the topology balancing technique and adaptive control approach. The developed algorithm is fully distributed in the sense that it depends on neither the global Lipschitz continuity of the gradients nor prior global information about the network connectivity. By regarding the proposed algorithm as a perturbed system, its input-to-state stability with a vanishing perturbation is first established, and asymptotic convergence of the decision variables toward the optimal solution is then proved. The effectiveness of the proposed fully distributed algorithm is illustrated with two examples.

Distributed Fault-Tolerant Containment Control Protocols for the Discrete-Time Multiagent Systems via Reinforcement Learning Method.

This article investigates the model-free fault-tolerant containment control problem for multiagent systems (MASs) with time-varying actuator faults. Depending on the relative state information of neighbors, a distributed containment control method based on reinforcement learning (RL) is adopted to achieve containment control objective without prior knowledge on the system dynamics. First, based on the information of agent itself and its neighbors, a containment error system is established. Then, the optimal containment control problem is transformed into an optimal regulation problem for the containment error system. Furthermore, the RL-based policy iteration method is employed to deal with the corresponding optimal regulation problem, and the nominal controller is proposed for the original fault-free system. Based on the nominal controller, a fault-tolerant controller is further developed to compensate for the influence of actuator faults on MAS. Meanwhile, the uniform boundedness of the containment errors can be guaranteed by using the presented control scheme. Finally, numerical simulations are given to show the effectiveness and advantages of the proposed method.

On impact of disturbance in the deployment problem of multi-agent system

On impact of disturbance in the deployment problem of multi-agent system

Leader-Follower Quasi-Consensus of Multi-Agent Systems with Packet Loss Using Event-Triggered Impulsive Control

This paper focuses on the leader–follower quasi-consensus problem of multi-agent systems, considering the practical communication scenarios which involve packet loss. The phenomenon of packet loss is described in terms of the packet loss rate. A novel hybrid event-triggered impulsive control strategy is proposed, the Lyapunov stability theory is employed to derive sufficient conditions for realizing the leader–follower quasi-consensus, and the exclusion of Zeno behavior is demonstrated. Finally, a numerical simulation example is provided to verify the effectiveness of the proposed approach. The simulation results indicate that the packet loss rate is closely related to the control gain and the maximum triggered interval, specifically because as the packet loss rate increases, the trigger frequency also increases.

Average Consensus Problem in Multi-Agent System in an Environment with Obstacles

Multi-agent systems (MASs) are composed of multiple autonomous agents, and the overall behavior of an MAS is determined by the local interactions between its agents. In recent years, research on MASs aimed at their application in real-world environments has garnered considerable interest. In particular, consensus control in environments with constrained communication range of agents and obstacles is being extensively investigated. In this study, the consensus problem of MASs is examined by considering the effects of obstacles. In particular, an obstacle avoidance algorithm is developed as a control method for real-world autonomous machines such as autonomous mobile robots. In conventional control methods, obstacles are provided as coordinate points; however, in this study, they are introduced as variously shaped polygons. Furthermore, the graph topology is dynamic, which enables more realistic situations. Finally, a simulation study is performed, and the effectiveness of the developed algorithm for applying to the MAS consensus problem is demonstrated.

Distributed Resilient Tracking of Multiagent Systems Under Actuator and Sensor Faults.

The distributed resilient tracking problem for multiagent systems (MASs) is investigated in the presence of actuator/sensor faults over directed topology. Both actuator fault and sensor fault are taken into account. Meanwhile, using the local information, the fault compensators are introduced. Then, based on the fuzzy-logic systems (FLSs) and modification technique of adaptive law, a novel distributed adaptive resilient control protocol is developed, which can compensate the effect of faults on the actuator and sensor. It turns out that all signals of MASs are bounded, while the tracking errors enter an adjustable bounded region around the origin. Toward the end, two simulations are provided to validate the effectiveness of the theoretical results.

Hybrid Event-Triggered Bipartite Consensus Control of Multiagent Systems and Application to Satellite Formation

This work studies event-triggered bipartite consensus problem of multi-agent systems (MAS) with structurally balanced signed graph. A hybrid system approach is proposed to deal with the leaderless and leader-following bipartite consensus problems in a unified framework, and a novel hybrid event-triggering mechanism (ETM) is proposed to reduce the usage of communication resources. By appropriately defining closed-loop states, a unified hybrid model is constructed for both leaderless and leader-following MAS. Then, stability analysis and ETM design results are given under hybrid systems framework. It is worth noting that the designed hybrid ETM is in decentralized form rather than distributed form. Moreover, the lower bound of triggering intervals can be pre-selected, i.e., Zeno-freeness is guaranteed, that is important for control implementation. The proposed method is applied to satellite formation system in simulation results to show the effectiveness. <i>Note to Practitioners</i>&#x2014;This paper considers bipartite consensus control problem of MAS, which can be applied to some practical systems, e.g., bidirectional formation of unmanned air vehicles, since the control objective in these applications can be transformed into bipartite consensus control problem of MAS. Compared with the existing results, a hybrid system approach is proposed for consensus analysis of MAS with or without leader, and a novel event-triggering mechanism is proposed, which is more easier to be implemented, so, the proposed approach is more friendly for control engineers. The obtained results are applied to satellite formation control problem and the effectiveness is verified, and it is expected that the proposed approach can be extended to MAS with more realistic network-induced constraints and applied to more practical engineering systems.

Lag Group Consensus of High-Order Multiagent Systems in Directed Network Settings

This article studies lag group consensus problems of multiagent systems with directed information transformations. Agents in the network are divided into finite groups, and modeled by high-order systems. Distributed consensus protocols with constant lags are presented to realize the lag group consensus: the states of the agents in a group approach to a consensus value asymptotically, while there exist given ratios between the final values of different groups. Necessary and sufficient criteria for the lag group consensus are obtained by the Laplace transformation. Finally, theoretical results are proved to be effective by several simulation results.

Formula omitted] Consensus of multi-agent systems under hybrid cyber attacks via a sampled-data-based dynamic event-triggered resilient consensus protocol

This paper studies the H∞ consensus problem of multi-agent systems (MASs) under external disturbances and hybrid cyber attacks. First, to save limited network resources, a sampled-data-based dynamic event-triggered mechanism is proposed for MASs under hybrid cyber attacks. Different from the existing dynamic event-triggered mechanisms, the negative influences of deception attacks and denial-of-service attacks are considered in the proposed dynamic triggering function. The communication frequencies between agents are reduced by the proposed event-triggered mechanism. Then, based on the proposed sampled-data-based dynamic event-triggered mechanism, a corresponding resilient consensus protocol is designed to ensure that MASs with external disturbances and hybrid cyber attacks can achieve consensus with a weighted H∞ performance. Finally, simulation examples are conducted to verify the effectiveness of the proposed methods.

An RNN-Based Performance Identification Model for Multi-Agent Containment Control Systems

In the containment control problem of multi-agent systems (MASs), the convergence of followers is always a potential threat to the security of system operations. From the perspective of system topology, the inherently non-linear properties of the algebraic connectivity of the follower2follower (F2F) network, combined with the influence of the leader2follower (L2F) topology on the system, make it difficult to design the convergence positions of the followers through mere mathematical analysis. Therefore, in the background of temporary networking tasks for large-scale systems, to achieve the goal of forecasting the performance of the whole system when networking is only completed with local information, this paper investigates the application and effectiveness of recurrent neural networks (RNNs) in the containment control system performance identification, thus improving the efficiency of system networking while ensuring system security. Two types of identification models based on two types of neural networks (NNs), MLP and standard RNN are developed, according to the range of information required for performance identification. Evaluation of the models is carried out by means of the coefficient of determination (R2) as well as the root-mean-square error (RMSE). The results show that each model may produce a better forecasting accuracy than the other models in specific cases, with models based on the standard RNN possessing smaller errors. With the proposed method, model identification can be achieved, but in-depth development of the model in further studies is still necessary to the extent the accuracy of the model.

NNs-observer-based fully distributed consensus control for MASs under deception attacks

This paper proposes a novel resilient intrusion-tolerance control scheme to solve the consensus control problem of multi-agent systems (MASs) under deception attacks. Consider the state unavailability of MASs with unknown nonlinear terms, the unmeasurable state is estimated by designing the neural-networks(NNs)-based observer. Then, a fully distributed intrusion-tolerant adaptive protocol is developed to implement the consensus control task. The designed control strategy only involves the agent states that interact with the neighboring agent and does not relate to the whole communication topology information. The designed controller can ensure that the consensus error of the nonlinear MASs is eventually ultimately bounded under deception attacks. Finally, a numerical simulation is provided to verify the feasibility of the designed scheme.

Distributed Alignment Processes With Samples of Group Average

Reaching agreement despite noise in communication is a fundamental problem in multi-agent systems. Here we study this problem under an idealized model, where it is assumed that agents can sense the general tendency in the system. More specifically, we consider $n$ agents, each being associated with a real-valued number. In each round, each agent receives a noisy measurement of the average value, and then updates its value, which is in turn perturbed by random drift. We assume that both noises in measurements and drift follow Gaussian distributions. What should be the updating policy of agents if their goal is to minimize the expected deviation of the agents' values from the average value? We prove that a distributed weighted-average algorithm optimally minimizes this deviation for each agent, and for any round. Interestingly, this optimality holds even in the centralized setting, where a master agent can gather all the agents' measurements and instruct a move to each agent.We find this result surprising since it can be shown that the total measurements obtained by all agents contain strictly more information about the deviation of Agent $i$ from the average value, than the information contained in the measurements obtained by Agent $i$ alone. Although this information is relevant for Agent $i$, it is not processed by it when running a weighted-average algorithm. Nevertheless, the weighted-average algorithm is optimal, since by running it, other agents manage to fully process this information in a way that perfectly benefits Agent $i$.Finally, we also analyze the drift of the center of mass and show that no distributed algorithm can achieve drift that is as small as the one that can be achieved by the best centralized algorithm. In light of this, we also show that the drift associated with our weighted-average algorithm incurs a relatively small overhead over the best possible drift in the centralized setting.

Bipartite Flocking Control for Multi-Agent Systems With Cooperation-Competition Interactions and Random Packet Dropouts

This paper addresses the bipartite flocking control problem of multi-agent systems with random packet dropouts, in which cooperative and competitive information interactions among agents are described by a signed network. The random packet dropouts are denoted by Bernoulli random variable and the information transmission failure between adjacent agents is possible at every time step. In this case, the unreliable information transmission within multi-agent systems may lead to behavioral dispersion and instability. To solve this problem, a novel fully distributed control scheme is designed. In light of the convergence method for products of infinite sub-stochastic matrices, the bipartite flocking control can be global almost sure to achieve and some essentially algebraic conditions are established. Finally, the effectiveness of the control scheme under random packet dropouts is illustrated by a numerical simulation example.