Event-Triggered Consensus Output Tracking Strategy for Multiagent Systems Utilizing Model-Free Adaptive Control

This article studies the fuzzy adaptive event-triggered (ET) consensus control issue of nonlinear multiagent systems (NMASs) under jointly connected switching networks. Since the leader and its high-order derivatives are unknown under jointly connected switching networks, a novel distributed ET reference generator equipped with an ET mechanism is constructed to estimate them. Meanwhile, the continuous information transmission among agents is avoided and the network channel utilization is optimized. Subsequently, fuzzy logic systems (FLSs) are employed to approximate unknown dynamics, and a fuzzy adaptive ET consensus control algorithm only using intermittent communication is designed by backstepping control methodology. It is demonstrated that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB), with the tracking errors converging to a small neighborhood around zero. Finally, we apply the developed fuzzy adaptive ET consensus control algorithm to unmanned surface vehicles (USVs), and the simulation results verify the effectiveness of the proposed ET consensus control algorithm.

This paper addresses the event-triggered consensus tracking for general linear multi-agent systems with nonlinear uncertainty dynamics under directed communication topology. The proposed event-triggered consensus strategy relaxes the assumptions in existing works that the control gain and local inputs must be continuous. With the consensus tracking condition being satisfied, the cooperative control law with bounded parameter is developed based on the intermittent samples. It shows that if the sampling condition, in terms of LMIs, is satisfied, the resulting closed-loop general linear multi-agent uncertain systems will achieve the consensus. Simulation results are provided to verify the proposed approach.

This paper investigates the periodic event-triggered asymmetric bipartite consensus problem of multi-agent systems (MASs) under structurally balanced undirected signed graphs. In order to reduce the consumption of communication resources and continuous detection in the control process of MASs, a novel periodic detection event-triggered asymmetric bipartite consensus control protocol is proposed. In particular, each agent samples only in a fixed period. For each agent, two event conditions are given, and only when state error satisfies this event condition can the event be triggered. A new type of Laplacian matrix is constructed to achieve asymmetric bipartite consensus. The control method can guarantee the performance of MASs and have less communication resources. Based on the Lyapunov stability theorem and algebraic graph theory, the proposed periodic event-triggered control strategy is able to implement the asymmetric bipartite consensus when the topology is structurally balanced undirected signed graphs. The proposed method in this paper can reduce the restriction on the sampling period and automatically exclude Zeno behavior. Finally, the validity of the conclusion is verified by numerical simulation.

This article investigates the adaptive fuzzy distributed optimal event-triggered (ET) control problem for high-order nonlinear multiagent (NMA) system under weight-unbalanced directed graph. An optimal signal generator with ET mechanism is first established to estimate optimization point and optimal network channel utilization between agents and neighbors. Second, since the ET mechanism is introduced in optimal signal generator, the first-order derivative of optimal virtual signals is not continuous at the trigger instant, a high-order filter is established to estimate high-order derivatives of optimal virtual signals. Based on the optimal signal generator with ET mechanism and high-order filter, an adaptive fuzzy distributed optimal ET control scheme is proposed by backstepping control design technology. It is shown that controlled NMA system is asymptotically stable and the global cost function is minimized. Finally, we apply the proposed distributed optimal ET control approach to marine surface vehicle systems, the simulation and comparison results verify its effectiveness.

This paper proposes a novel event-triggered (ET) adaptive neural control scheme for a class of discrete-time nonlinear systems in a strict-feedback form. In the proposed scheme, the ideal control input is derived in a recursive design process, which relies on system states only and is unrelated to virtual control laws. In this case, the high-order neural networks (NNs) are used to approximate the ideal control input (but not the virtual control laws), and then the corresponding adaptive neural controller is developed under the ET mechanism. A modified NN weight updating law, nonperiodically tuned at triggering instants, is designed to guarantee the uniformly ultimate boundedness (UUB) of NN weight estimates for all sampling times. In virtue of the bounded NN weight estimates and a dead-zone operator, the ET condition together with an adaptive ET threshold coefficient is constructed to guarantee the UUB of the closed-loop networked control system through the Lyapunov stability theory, thereby largely easing the network communication load. The proposed ET condition is easy to implement because of the avoidance of: 1) the use of the intermediate ET conditions in the backstepping procedure; 2) the computation of virtual control laws; and 3) the redundant triggering of events when the system states converge to a desired region. The validity of the presented scheme is demonstrated by simulation results.

This article addresses the event-triggered consensus problem of multiagent systems (MASs) with packet losses. The packet losses are not restricted to be identical, and a hybrid systems framework is proposed to handle the considered problem with or without leader in a unified framework. Distributed control law is constructed based on an estimator, whose dynamics are related to whether packet losses occur. A novel hybrid event-triggering mechanism (ETM) is proposed, and a lower bound of the interevent times is enforced to guarantee Zeno-freeness. For stability analysis, a unified hybrid model is constructed to describe the dynamics of MAS with or without leader. In accordance to this model, how to design the hybrid ETM and consensus analysis are formally developed, and the maximum allowable successive packet losses can be explicitly computed. Moreover, even if there exist nonlinearities and disturbances in system dynamics, and the leader has nonzero input, the proposed method can still be applied. Finally, examples are provided to illustrate the superiority of the proposed method.

In this article, we investigate the event-triggered consensus problem for general linear multiagent systems with bounded communication network delay. To reduce the communication frequency, we propose some dynamic periodic event-triggering strategies, under which an auxiliary dynamic variable and a weighting parameter are introduced for each agent to build its event-triggering condition. Then, a distributed adaptive consensus protocol, which includes a time-varying coupling weight for each edge, is designed based on the proposed event-triggering strategies. With the well-designed updating laws of dynamic variables, coupling weights and weighting parameters, we can theoretically guarantee the consensus of the concerned multiagent systems. Our event-triggering strategies do not require agents to continuously verify the event-triggering conditions or read their neighbors’ real-time state information. Moreover, Zeno behavior is inherently excluded as the interevent intervals are bounded from below by one period in the periodic event-triggering framework. Two numerical examples are provided to verify the effectiveness of the proposed event-triggered consensus protocols.

Adaptive MLP neural network controller for consensus tracking of Multi-Agent systems with application to synchronous generators

This paper investigates the fixed-time-synchronized consensus tracking problem for multi-agent systems with undirected topology. A nonlinear distributed control law for first-order multi-agent systems is proposed based on norm-normalized sign function, with which all the elements of all the agents' states track the leader's state at the same time and in fixed time. Then, the corresponding results are extended to second-order multi-agent systems by adopting the terminal sliding mode control method. Finally, numerical simulations are performed to verify the theoretical results in this paper.

This paper proposes a robust distributed event-triggered approach for consensus in linear multi-agent systems (MAS) with uncertain network topologies. To achieve consensus, each agent transmits its information only when a certain event-triggering condition is fulfilled. The connection weights in the network are uncertain and hence the information received by each agent is unreliable. In such an uncertain topology, the objective is to co-design robust consensus parameters (namely, the state transmission threshold and local control gains) that collectively ensure an exponential rate for consensus convergence. An objective function incorporating the transmission load and control effort is minimized to compute the design parameters. Numerical simulations quantify the effectiveness of the proposed event-triggered consensus approach in a second-order MAS.

Fully distributed dynamic event-triggered reliable COR of nonlinear MASs with communication link faults and actuator faults for directed graphs

This paper primarily addresses the design of distributed optimal cooperative controllers and the utilization of a reinforcement learning (RL)-based event-triggered mechanism for multi-agent systems (MASs) with unknown dynamics. By setting an extra compensator, the augmented system is constructed to overcome the dependence for system dynamics. Then, to address the issue of computational burden, we utilize an event-triggered mechanism based on reinforcement learning (RL) and neural networks (NNs) to implement the adaptive dynamic programming (ADP) algorithm. Additionally, we take into consideration the trade-off between computational burden and achieving consensus control by introducing a weighting factor in the reward design for MASs. With this reward design, we present an algorithm based on the deep deterministic policy gradient (DDPG) algorithm to learn the event-triggered condition for MASs and achieve a balance between these two factors. The event-triggered mechanism of our algorithm can also identify constraints such as time limitations or computational resource restrictions, aiming to achieve consensus control without violating these constraints. We demonstrate the absence of Zeno behavior and the uniform ultimate boundedness (UUB) of both local consensus error and weight estimation error. Finally, simulation results illustrate the effectiveness of the control algorithm and the weighting factor.

This paper studies the adaptive consensus tracking problem for multi-agent systems modeled as high-order integrators with uncertain nonlinear dynamics under directed communication graphs. By parameterizing the unknown dynamics of the agents and the unknown input of the leader, two decentralized tracking control laws are designed using the state information and only the output information, respectively. With state information, globally uniformly ultimately bounded consensus tracking with arbitrarily small tracking errors can be achieved and when only output information is available, based on high-gain observers, semi-global result can be obtained. Numerical examples are provided to illustrate the effectiveness of the controllers.

This paper investigates the output consensus tracking problem of a class of heterogeneous open multi-agent systems (oMASs) under actuator attacks. The main challenges in solving this problem stem from the presence of actuator attacks and the dimensionality mismatch of the error signals caused by the dynamic entry and exit of followers. To address such challenges, a novel analytical method is developed to characterize the error evolution at switching instants via switching-instant error-coupling inequalities, which describe how error signals evolve in response to changes in the number of followers. Based on this method, a full-order attack-free state observer is designed to estimate the states of each follower without estimating the exact value of the attack. In addition, a reduced-order attack-free observer is developed to reduce the estimation burden and computational complexity, while still ensuring the effective estimation of the states. Building on the observer architectures, both full-order and reduced-order observer-based distributed resilient control protocols are proposed to achieve output consensus tracking while ensuring the tracking error remains uniformly ultimately bounded (UUB) for heterogeneous oMAS under actuator attacks, by employing the output regulation approach. The effectiveness of both types of control protocols is demonstrated through numerical simulation.

For discrete-time linear parameter varying systems with distributed time delays, the problem of fault detection under event triggering mechanism is studied. Based on the parameter dependent Lyapunov function method, the sufficient conditions for the system to be asymptotically stable and meet the H∞ performance criterion are given. The parameter matrix of fault detection filter is obtained by solving LMI technology. In addition, the event triggering strategy is introduced to save network resources.