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In this article, we study the consensus issues of multiagent systems (MASs) without any information of the system model by using the reinforcement learning (RL) method and event-based control strategy. First, we design an adaptive event-based consensus control protocol using the local sampled state information so that the consensus errors of all agents are uniformly ultimately bounded. The validity of the above event-triggered adaptive control protocol is confirmed by excluding the Zeno behavior within finite time. Then, based on the RL approach, we present a model-free algorithm to get the feedback gain matrix, and accomplish constructing the adaptive event-triggered control strategy without the knowledge of model information. Distinct with the existing related works, this RL-based event-triggered adaptive control algorithm only relies on the local sampled state information, irrelevant to any model information or global network information. Finally, we provide some examples to demonstrate the validity of the above adaptive event-based consensus algorithm. [ABSTRACT FROM AUTHOR]

Many existing approaches for coordinating heterogeneous teams of robots either consider small numbers of agents, are application-specific, or do not adequately address common real-world requirements, e.g., strict deadlines or intertask dependencies. We introduce scalable and robust algorithms for task-based coordination from high-level specifications (ScRATCHeS) to coordinate such teams. We define a specification language, capability temporal logic, to describe rich, temporal properties involving tasks requiring the participation of multiple agents with multiple capabilities, e.g., sensors or end effectors. Arbitrary missions and team dynamics are jointly encoded as constraints in a mixed integer linear program, and solved efficiently using commercial off-the-shelf solvers. ScRATCHeS optionally allows optimization for maximal robustness to agent attrition at the penalty of increased computation time. We include an online replanning algorithm that adjusts the plan after an agent has dropped out. The flexible specification language, fast solution time, and optional robustness of ScRATCHeS provide a first step toward a multipurpose on-the-fly planning tool for tasking large teams of agents with multiple capabilities enacting missions with multiple tasks. We present randomized computational experiments to characterize scalability and hardware demonstrations to illustrate the applicability of our methods. [ABSTRACT FROM AUTHOR]

In this article, the distributed finite-time and fixed-time optimization problems are investigated by adopting the zero-gradient-sum (ZGS) framework in multiagent systems. Specifically, when the local convex functions are nonquadratic, a basic optimization protocol is proposed to obtain a finite-time convergence, such that the networked system can cooperatively seek the optimal solution of the global objective, the sum of local objective, in a limited time. By utilizing the property of quadratic functions, a reduced algorithm can remove the dependence of initial conditions in the estimation of the upper bound of settling time and achieve a fixed-time result. Besides, the problem with time-varying topologies is studied by introducing a modified algorithm with an artificial potential function to preserve the network connectivity. Finally, the validity of the protocols is demonstrated via some example simulations. [ABSTRACT FROM AUTHOR]

In this article, we propose two model-free algorithms using state or output feedback for saturated discrete-time multiagent systems (SDTMASs) to attain global containment control. In most previous works, the control input can avoid saturation by utilizing the low gain feedback (LGF) method whereas requiring the knowledge of agent dynamics, and SDTMASs just can attain semi-global containment control. Distinct with the previous works, first, based on the $Q$ -learning (QL) technique, this article defines a $Q$ -function and deduces the corresponding QL Bellman equation, which is the most important part of the QL algorithm. Then, in order to solve the QL Bellman equation, we propose two iterative model-free algorithms using state and output feedback, and the LGF matrix can be acquired from that solution directly. Furthermore, under the state and output feedback control protocols with the feedback matrices obtained from the proposed model-free algorithms, the SDTMASs can achieve global containment control instead of semi-global containment control. Finally, we present some simulations to confirm the validity of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this article, we are concerned with the output synchronization of a group of heterogeneous agents with nonlinear dynamics. It is shown that via dynamic coupling, the output synchronization problem is transformed into a family of output homogenization and internal-model-based synchronization problems. In contrast to conventional approaches, the adopted approach does not rely on the generation of a specific reference trajectory, to be tracked by all agents. Based on this premise, we propose a distributed state-feedback protocol driving all the heterogeneous multiagent individual systems onto a copy of a virtual reference signal. Invoking the results from the theory of output regulation and cooperative control, the state-feedback controller synthesis is conducted to achieve the output synchronization of heterogeneous multiagent networks. Immediate extensions of the obtained results to observer-based control are also discussed. The effectiveness of the proposed output synchronization algorithm is verified through theoretical analysis and case studies. [ABSTRACT FROM AUTHOR]

By using the event-triggered technique, we study the distributed control of a class of nonlinear multiagent systems (MASs) with aperiodic sample information. Taking advantage of the combinational sample measurements, we first present an event-triggered consensus algorithm for the leaderless MAS. Thereafter, we design the event-triggered tracking algorithm for the leader–follower MAS with a leader having bounded input. Furthermore, we prove that both consensus and tracking controllers do not exist Zeno behavior. Finally, a numerical example is given to verify the designed event-triggered consensus algorithm. [ABSTRACT FROM AUTHOR]

This article investigates the fully cooperative consensus problem for heterogeneous nonlinear multiagent systems with a leader of bounded unknown input. In the proposed framework, a novel distributed cooperative consensus algorithm is developed to derive the perfect consensus behaviors, in which the general assumption in existing works that the leader agent must have a direct link to every follower can be removed. With the formulation of some mild assumptions, the sufficient conditions for controller design are proposed to develop a class of integrated consensus protocol, which can friendly synthesize the limited interaction, compensation dynamics of the equilibrant point of the systems, appropriate feedback gains, and distributed estimation of the unknown inputs of the leader. The effectiveness of the proposed approaches are verified by simulations with multiple heterogeneous single-link manipulators. [ABSTRACT FROM AUTHOR]

This article focuses on the consensus control problem for a class of time-varying stochastic nonlinear time-delay multiagent systems (MASs) attacked by deception attacks. The stochastic deception attack is considered in the procedure of propagating measurement information among agents. To solve the consensus control problem for addressed MASs under stochastic deception attacks, a definition of quasi-consensus is put forward. The objective of our investigation is to devise a consensus protocol to drive all agents to stay within an allowable range despite the existence of stochasticity and external malicious attacks. With the help of recursive linear matrix inequality and stochastic analysis methods, sufficient conditions are acquired to guarantee that all agents are constrained in the desirable range. Subsequently, an optimization algorithm is presented, which is to seek the locally optimal allowable distance among agents. Finally, a simulation example is presented to demonstrate the availability of our proposed algorithm. [ABSTRACT FROM AUTHOR]

This article studies the consensus of hybrid second-order multiagent systems (MASs), where the hybrid MAS is constituted by the continuous-time second-order (CTSO) and discrete-time second-order (DTSO) dynamic agents. First, a hybrid consensus algorithm is proposed, where a sample-data control is considered in the CTSO subsystems. Under the proposed hybrid consensus algorithm, the convergence of the hybrid second-order MAS matrix is analyzed. Second, a sufficient and necessary condition is proposed, which indicates that the controller parameters, such as coupling gains and the sampling interval, and eigenvalues of network topology have a significant impact on the system consensus. Finally, several simulation examples are presented to prove the validity of the results. [ABSTRACT FROM AUTHOR]

Parafoil airdrop is an important way to deliver goods and materials to area where road vehicles are not easy to reach. However, it is difficult to deliver large quantities of goods and materials to a given location with only one parafoil. Airdropping multiple parafoils is an effective choice for transporting large quantities of goods and materials. To realize the cooperative airdrop of multiple parafoils, a cooperative guidance framework is proposed. First, a trajectory planning algorithm is designed to plan the multiphase trajectory for the parafoil group. Then, a trajectory tracking algorithm is developed for the pilot parafoil in the parafoil group to reliably follow the planned trajectory. Finally, a cooperative formation guidance strategy is designed based on the leader–follower consensus theory. Under this strategy, the position and speed of the follower parafoil can be consensus with those of the leader parafoil. Lyapunov’s theorem proves the stability of this strategy. We evaluate the effectiveness of this framework through simulations. The results demonstrate that our algorithms can realize the precise airdrop of massive goods and materials with upwind landing using multiple parafoils. In addition, the parafoils could be gradually gathered to a desired formation, and safe distances could be maintained between parafoils during the airdrop process. Note to Practitioners—This article was motivated by the problem of airdropping massive goods and materials. Existing methods usually adopt a single heavy parafoil, or use centralized multiparafoil systems. Both these methods have their limitations. For the former, there is an upper limit of the load capacity for a single parafoil. For the latter, the parafoils in the centralized system lack fully autonomous ability. Distributed multiparafoil systems could solve the problem effectively. However, compared to single-parafoil systems, there are still some challenges, for example, the multiparafoil gathering, collision avoidance and cooperative formation, as well as the upwind landing. Fortunately, existing parafoils are equipped with sensors, communication, and control devices, so they could be viewed as agents with autonomous capabilities. In this article, a formation guidance framework for multiple autonomous parafoils is proposed. First, we plan a trajectory for the pilot parafoil. Then, we show how to effectively track the planned trajectory. Finally, we demonstrate how multiple parafoils could coordinate with each other to accomplish airdrop tasks. The simulation results confirm the feasibility of this strategy. [ABSTRACT FROM AUTHOR]

This article considers integral sliding-mode control (ISMC) for multiagent systems with matched disturbances through adaptive dynamic programming (ADP) method. A distributed disturbance observer (DDO) is designed for each agent to generate the disturbance estimation. Using the disturbance estimation, an ISMC scheme is proposed to reject the input disturbance and obtain the equivalent local neighbor consensus sliding-mode dynamics. Then, a discount performance function is designed for each agent, and ADP technique is utilized to address the optimal consensus control for the equivalent sliding-mode dynamics online. Based on the gradient descent algorithm, we propose the adaptive weight tuning laws for the critic-actor neural networks (NNs) to carry out the ADP method. Furthermore, the local neighbor consensus errors and the weight estimation errors for the critic-actor NNs are proved to be uniformly ultimately bounded (UUB). Finally, the practical example is applied to validate the effectiveness of our results. [ABSTRACT FROM AUTHOR]

This article investigates the privacy-preserving average consensus problem for the general continuous-time multiagent network systems (MANSs) with time delay via the event-triggered communication scheme. In order to avoid disclosing the initial states of the network agents and at the same time achieve the agents’ consensus for MANSs with time delay, a novel consensus control algorithm based on a new privacy-preserving approach is proposed. The new privacy-preserving approach is that we construct an output mask to make agents’ internal states indiscernible by others, which is different from the existing privacy-preserving methods adding random noises to the update law of agents’ states. Compared with the existing privacy-preserving methods, our approach makes all agents in MANSs exactly converge to the average value of initial states instead of its mean square value. Based on the proposed algorithm, we carry out the detailed theoretical consensus analysis of the network agents, from which, it is shown that the upper bound of the communication time delay between neighbors’ agents can be estimated approximately. Moreover, the Zeno-behavior of event-triggered time sequences for each agent is excluded. Finally, two simulation examples are performed to demonstrate the effectiveness of our theoretical results. [ABSTRACT FROM AUTHOR]