Fuzzy Adaptive Event-Triggered Consensus Control for Nonlinear Multiagent Systems Under Jointly Connected Switching Networks.

This study considers the decentralised periodic event‐triggered consensus problem for continuous‐time linear multi‐agent systems (MASs) under general directed graphs. The authors discretise the continuous‐time MASs based on zero‐order hold, and propose an event‐triggered consensus control (ETCC) algorithm rendering the states of agents reach consensus. They prove that under the proposed ETCC implemented in multiple steps, there is no Zeno‐like behaviour exhibited in the sense of sampling. To relax the requirement of the periodic monitoring in the ETCC, it is further proposed a self‐triggered consensus control algorithm, where the next triggering instant is predetermined by each agent at the previous triggering instant. For the case where the states of MASs are unavailable, the authors further propose an observer‐based ETCC algorithm. Simulation results are given to illustrate the theoretical results.

This paper investigates the design and development of event-triggered simultaneous fault detection and consensus control (SFDCC) problem for a network of linear agents. Each agent is equipped with a distributed SFDCC unit that uses the information from the relative measurement sensors and data transmission facilities. It is assumed that the data is transmitted from an agent to its neighbours whenever a specific event condition is satisfied. With the proposed methodology each agent can detect not only its own fault but also the faults occurred in its neighbors and at the same time, all the agents can track the output of a reference model. The parameters of the SFDCC module are designed such that a mixed H ∞ /H − performance index is satisfied based on sufficient conditions in terms of extended linear matrix inequalities (LMIs). It is shown that by using event-triggered data transmission, the amount of data transmitted from an agent to its neighbors and the actuator updates are reduced. Simulation results are presented for fault detection and control of a group of unmanned underwater vehicles to demonstrate and verify the effectiveness of the proposed methodology.

In this article, a model-free-adaptive-control-based (MFAC-based) event-triggered (ET) consensus output tracking problem for multiagent systems (MASs) is investigated. The dynamic models of agents are unknown, and only a subset of agents can acquire the reference trajectory. The consensus tracking algorithm is designed by the real-time input/output data and pseudo-partial-derivative (PPD), which is an important parameter of MFAC approach. An output observer is built to design the centralized ET mechanism. Then, the boundedness analysis that the tracking error is uniformly ultimately bounded (UUB) is given. Finally, a simulation experiment is provided to verify the feasibility of the ET consensus output tracking strategy for MASs.

<p indent="0mm">The tracking consensus of multiagent systems based on distributed model predictive control (DMPC) is a current research hotspot. However, as a research difficulty, effective solutions to deal with external disturbances and reduce computational resource consumption are lacking. This paper proposes a new constraint tightening scheme and establishes a new distributed optimization problem. Moreover, an adaptive event-triggering mechanism is designed using the idea of variable prediction horizon, and an adaptive event-triggered DMPC approach for the tracking consensus of multiagent systems is proposed, effectively solving the problems of the unsatisfaction of constraints and computational resource consumption in the tracking consensus of multiagent systems. The effectiveness of the proposed approach is validated by a multivehicle system simulation.

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.

Autonomous driving technology for agricultural machinery can maximise crop yield, reduce labour costs, and alleviate labour intensity. In response to the current low degree of automation and low tracking accuracy of driving paths in agricultural equipment, this research proposes an unmanned agricultural machinery operating system based on an improved fuzzy adaptive PD control algorithm. Firstly, mechanical kinematic models and fuzzy adaptive control algorithms are introduced to achieve autonomous driving, and parameter settings and speed adjustments are made while considering errors. Secondly, in the autonomous driving operation system, taking a certain rice machine as an example, perception information, trajectory design, dynamic control, operation supervision, and remote control design are carried out. The experimental results show that the improved fuzzy algorithm exhibits smaller deviation results in driving path tracking, with an average error between the actual path and the expected path of less than 0.001 m. In different testing scenarios, compared with the actual control results, the maximum deviation of the control system platform in straight sections is less than 2.8 m, which is more stable. More than 95% of the lateral deviation results in the road sections are within 0.11 m. And the tracking distance error of the proposed method in the straight and curved segments is relatively small, far smaller than other comparative algorithms. The unmanned agricultural machinery operation system proposed in this study can significantly improve the efficiency and accuracy of agricultural machinery work, promote the development of intelligent and modern agricultural machinery, and provide reference value and important contributions to social and economic development as well as the progress and promotion of related technologies.

This article investigates adaptive control problems for unknown second-order nonlinear multiagent systems (MASs) via an event-triggered approach. An adaptive event-triggered consensus controller is given to second-order MAS with unknown nonlinear dynamics. We prove that the proposed consensus controller is free from Zeno behavior. Next, an adaptive event-triggered tracking controller is developed for leader-follower MAS with the leader having bounded nonzero control input. Both consensus and tracking controllers are fully distributed, which means that event-triggered controllers only use local cooperative information. Finally, an unknown second-order nonlinear MAS is used to verify the given event-triggered controllers.

In this paper, an adaptive event-triggered consensus control scheme is designed for nonlinear multi-agent systems suffering from actuator faults and false data injection (FDI) attacks. To counteract the corruption of input states caused by sensor-level FDI attacks, a novel coordinate transformation is proposed, enabling accurate state reconstruction. By modifying local control laws and adaptive laws, the negative effects of actuator faults are counteracted. Unknown time-varying attack weights are addressed using Nussbaum-type functions. In addition, an adaptive event-triggering mechanism based on coordinate transformation is established to reduce the communication frequency of the network. The validity of the proposed control protocol is evidenced by two simulation examples.

In this paper, an adaptive event-triggered consensus problem considering the time delay of the communication network is studied for heterogeneous multi-agent systems. An event-triggered interval is here considered as a specific delay and unified round trip time (RTT) delay. Furthermore, an efficient optimal predictive-based coordination control strategy is introduced for balancing the non-ideal behaviors of communication channels. In order to evaluate the efficiency of the proposed method for controlling network-based multi-agent systems with coupled subsystems, two stages are studied. In the first stage, the very method is implemented on two coupled continuous stirred tank reactors while in the second one, it is used for controlling the voltage and current of a DC microgrid consisting of several distributed generation units. To prevent the unessential utilization of communication resources, the transfer of information will actually occur in this mechanism if a specific event is triggered. The simulation results show the fact that in spite of being non-ideal and time-delayed communication channels, the proposed technique is capable for improving the performance of power grids.

One of the seventeen Sustainable Development Goals (SDGs) is to provide zero hunger, achieve food security, enhance nutritional quality, and promote sustainable agriculture. To achieve these goals, food production has to be increased by adopting modern techniques in agriculture, such as hydroponic farming and the Internet of Things (IoT). Effective handling of nutrient concentrations, specifically electrical conductivity (EC) values, is crucial for optimizing plant health and yield. To reduce the challenge posed by high overshooting of nutrient concentration and also due to the diffusion of the regulating liquid fertilizer through the pipeline, the EC value control system demonstrates features such as time-delayed for the nutrient concentration to reach the setpoint. The paper aims to provide a comprehensive study that evaluates three distinct control strategies for regulating nutrient concentration in a hydroponic environment: proportional-integral-derivative (PID) control, fuzzy logic control, and a consensus control algorithm. The stability and performance of these control systems were investigated through simulation analysis and comparative evaluations. The findings indicate that the PID control system is faster in reaching the setpoint, while the fuzzy control system exhibits the least overshoot. Moreover, the consensus algorithm emerges as the most optimal control system in terms of time to reach the setpoint and minimize the overshoot value.

This paper investigates the consensus problem in discrete multi-agent systems characterized by both first-order and second-order agent dynamics, addressing the challenges posed by input delays and disturbances. To optimize data exchange and information sharing, an innovative event-triggered control system with communication delays is developed for the consensus control strategy. The materials employed in this study include a diverse set of heterogeneous agents designed to mirror real-world dynamics and delay characteristics. The methods involve the formulation of the consensus condition using Linear Matrix Inequalities (LMIs) and Kronecker product techniques, complemented by Lyapunov functional approaches for rigorous system stability analysis. The uniquely distributed control mechanism relies solely on local neighbor communication, enhancing its practical applicability. The theoretical framework is validated through comprehensive simulations, demonstrating its robustness and effectiveness in achieving consensus among agents under the specified dynamic conditions.

Distributed Non-Convex Optimization over Multi-Agent Networks based on Event-Triggered Consensus Control

This brief exploits the adaptive dynamic event-triggered (ADET) consensus problem for multi-agent systems (MASs). Firstly, we propose a novel ADET protocol which improves the existing dynamic event-triggered (DET) protocol and adaptive event-triggered (AET) protocol based on sampling control. The possibility that linear matrix inequalities (LMIs) have no feasible solution is reduced by presetting an adaptive maximum threshold. The asymptotic stability criterion is obtained. Subsequently, the controller gain and ADET parameters are obtained. Finally, the validity of the theoretical results in this brief is verified by a 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.

In this article, the leader-following consensus problem via the event-triggered control technique is studied for the nonlinear strict-feedback systems with unmeasurable states. The follower's nonlinear dynamics is approximated using the fuzzy-logic systems, and the fuzzy weights are updated in a nonperiodic manner. By introducing a fuzzy state observer to reconstruct the system states, an observer-based event-triggered adaptive fuzzy control and a novel event-triggered condition are designed, simultaneously. In addition, the nonzero positive lower bound on interevent intervals is presented to avoid the Zeno behavior. It is proved via an extension of the Lyapunov approach that ultimately bounded control is achieved for the leader-following consensus of the considered multiagent systems. One remarkable advantage of the proposed control protocol is that the control law and fuzzy weights are updated only when the event-triggered condition is violated, which can greatly decrease the data transmission and communication resource. The simulation results are provided to show the effectiveness of the proposed control strategy and the theoretical analysis.