Event-triggered Communication Research Articles

Adaptive fixed-time containment control of MIMO nonlinear multiagent systems via dynamic event-triggered communication

Adaptive fixed-time containment control of MIMO nonlinear multiagent systems via dynamic event-triggered communication

Fixed-Time Fuzzy Control for Uncertain Nonlinear Systems With Prescribed Performance and Event-Triggered Communication

Fixed-Time Fuzzy Control for Uncertain Nonlinear Systems With Prescribed Performance and Event-Triggered Communication

Preface

Hosted by Nanjing Tech University, China, the 2023 2nd International Conference on Aerospace and Control Engineering (ICoACE 2023) was held from 15th to 17th December 2023 in Nanjing, China. With ICoACE 2023, the conference series International Conference on Aerospace and Control Engineering (ICoACE) completed its second edition.The general topics of ICoACE 2023 fall into mainly four categories: 1) Aerospace Materials; 2) Aerospace Science and Technology; 3) Control Engineering; 4) Aerospace and Mechanics. Apart from the general topics, ICoACE 2023 attracted a number of papers related to aerospace and control engineering. Topics at the Conference include but are not limited to: Materials Failure Analysis and Prediction Prevention, Spacecraft Thermal Control Technology, Aerospace Computing, Combustion and Energy Conversion, Electric Automation, Drive Motor and Control Technology, etc.All papers were exhaustively reviewed by program committee members and peer-reviewers, who took into account the breadth and depth of the research topics that fall under the scope of ICoACE. The most promising and ICoACE mainstream-relevant contributions were selected from all the submissions for presentation and inclusion in this volume, which presents innovative original ideas or results of general significance supported by clear and rigorous reasoning and compelling new evidence, as well as methods.The Conference included three days of technical parts consisting mainly of keynote speeches, oral presentations, poster presentations and academic discussion. We were delighted and honored to invite Prof. Yan Wang (Nanjing University of Aeronautics and Astronautics, China), Prof. Mouquan Shen (Nanjing Tech University, China), and Prof. Wenfeng Hu (Central South University, China) to share their insightful ideas and mind-blowing researches as keynote speakers. Based on the research The Cooperative Control of Multi-Agent Systems with High Comprehensive Performance, Prof. Wenfeng Hu first introduced the event-triggered control strategies for multi-agent systems to reduce the communication burden among agents and talked about the brought challenging issues. Then, he addressed the consensus of homogeneous multi-agent systems and cooperative output regulation of heterogeneous multi-agent systems with event-triggering communication. Moreover, he presented the developed reset control strategies for multi-agent systems with second-order dynamics. Finally, an outlook on the new topics and trend for the cooperative control of networked control systems theory and techniques were given.We would like to thank all the keynote speakers, authors, program committee members and anonymous reviewers for their efforts in making ICoACE 2023 a Conference of the highest standard. We are also grateful to Journal of Physics: Conference Series, for its help in publishing this Proceedings of the Conference.The Committee of ICoACE 2023List of Committee Member is available in this pdf.

Remote path-following control for a holonomic Mecanum-wheeled robot in a resource-efficient networked control system

This paper introduces a novel resource-efficient control structure for remote path-following control of autonomous vehicles based on a comprehensive combination of Kalman filtering, non-uniform dual-rate sampling, periodic event-triggered communication, and prediction-based and packet-based control techniques. An essential component of the control solution is a non-uniform dual-rate extended Kalman filter (NUDREKF), which includes an h-step ahead prediction stage. The prediction error of the NUDREKF is ensured to be exponentially mean-square bounded. The algorithmic implementation of the filter is straightforward and triggered by periodic event conditions. The main goal of the approach is to achieve efficient usage of resources in a wireless networked control system (WNCS), while maintaining satisfactory path-following behavior for the vehicle (a holonomic Mecanum-wheeled robot). The proposal is additionally capable of coping with typical drawbacks of WNCS such as time-varying delays, and packet dropouts and disorder. A Simscape Multibody simulation application reveals reductions of up to 93% in resource usage compared to a nominal time-triggered control solution. The simulation results are experimentally validated in the holonomic Mecanum-wheeled robotic platform.

Strengthening stability with centralized event-triggered control system with the disturbances and artificial time delay in wireless connected vehicle platooning (CVSs)

This paper addresses the difficulties with connected vehicle systems (CVSs), particularly with vehicle platooning, are examined in this paper. For leader and follower-connected vehicles, the control protocol (which includes artificial delays, disturbances and proportional gains) is implemented. With tracking error systems, system dynamics are modelled while taking outside influences into consideration. Using creative thinking, a centralized event-triggered control system is implemented to maximize fleet wide communication updates. System stability is guaranteed by this centralized method in combination with quadratic form Lyapunov stability analysis. The risk of zeno behaviour is reduced by an event-triggered communication condition that is activated when a threshold is exceeded. The effectiveness of the centralized event-triggered system in improving stability and resilience in connected vehicle platooning scenarios is evaluated numerically through simulations.

Distributed optimization via dynamic event-triggered scheme with metric subregularity condition

In this paper, we present a continuous-time algorithm with a dynamic event-triggered communication (DETC) mechanism for solving a class of distributed convex optimization problems that satisfy a metric subregularity condition. The proposed algorithm addresses the challenge of limited bandwidth in multi-agent systems by utilizing a continuous-time optimization approach with DETC. Furthermore, we prove that the distributed event-triggered algorithm converges exponentially to the optimal set, even without strong convexity conditions. Finally, we provide a comparison example to demonstrate the efficiency of our algorithm in communication resource-saving.

A collective neurodynamic approach to distributed resource allocation with event-triggered communication

AbstractTo solve a distributed optimal resource allocation problem, a collective neurodynamic approach based on recurrent neural networks (RNNs) is proposed in this paper. Multiple RNNs cooperatively solve a global constrained optimization problem in which the objective function is a total of local non-smooth convex functions and is subject to local convex sets and a global equality constraint. Different from the projection dynamics to deal with local convex sets in the existing work, an internal dynamics with projection output is designed in the algorithm to relax the Slater’s condition satisfied by the optimal solution. To overcome continuous-time communication in a group of RNNs, an aperiodic communication scheme, called the event-triggered scheme, is presented to alleviate communication burden. It is analyzed that the convergence of the designed collective neurodynamic approach based on the event-triggered communication does not rely on global information. Furthermore, it is proved the freeness of the Zeno behavior in the event-triggered scheme. Two examples are presented to illustrate the obtained results

Distributed Event-Triggered Algorithm Designs for Resource Allocation Problems via a Universal Scalar Function-Based Analysis.

In this article, we are concerned with distributed algorithm designs for resource allocation problems via event-triggered communication. The target is to search an optimal resource allocation scheme such that the summation of objective functions is minimized. Due to communication efficiency and privacy concerns, distributed algorithms with event-triggered communications are proposed in this article. The communication is only permitted or triggered if variation of gradient of the local objective function exceeds a threshold. By constructing a novel technical lemma and a universal scalar function, the convergence and linear convergence rates are established under some mild assumptions. Extensive numerical experiments on the IEEE 118-bus power system demonstrate that: Compared to the periodic algorithms, such as ADMM and Mirror-P-EXTRA, the proposed algorithms not only remarkably reduce the communication times but also have competitive convergence speed. The latter is striking that it implies there exist useless communications in the periodic algorithms that are censored by the proposed event-triggered strategy.

Distributed Nonconvex Optimization With Event-Triggered Communication

Distributed Nonconvex Optimization With Event-Triggered Communication

Output Feedback-Based Consensus for Nonlinear Multiagent Systems: The Event-Triggered Communication Strategy.

The current investigation explores the leader-following consensus problem for nonlinear multiagent systems under the output feedback control mechanism and the event-triggered communication mechanism. Owing to the physical instrument constraints, a significant portion of the state variables is not readily available. Therefore, this article put forward a distributed event-based leader-following consensus protocol only using agents' relative output measurements and underlying neighbors. Furthermore, this article develops two event-triggered mechanisms simultaneously, one is the event-triggered communication mechanism in the sensor-to-controller channel, and another is the event-triggered controller update in the controller-to-actuator track. Besides that, it is proven that the developed event-triggered control protocol can settle the leader-following consensus problem of the nonlinear multiagent systems, and the Zeno behavior is excluded in both the channels. Finally, we perform two simulation examples to illustrate the efficacy of the obtained results.

Event-Triggered Federated Learning for Fault Diagnosis of Offshore Wind Turbines With Decentralized Data

Rapid developments of offshore wind industry offer a strong demand opportunity for offshore wind turbine remote diagnosis. As offshore wind turbines are often located in harsh and communication-constrained environments, the collection and transmission of data is severely restricted, which poses a serious challenge to the conventional centralized diagnostic paradigm that relies on data aggregation. To address this challenge, we propose a novel event-triggered federated learning framework for decentralized fault diagnosis of offshore wind turbines. Specifically, federated learning is first employed to learn decentralized local knowledge from geographically distributed offshore wind turbines, so that the communication objects are transformed from massive raw data into learned parameters, thereby relieving the communication burden. Then, we design an event-triggered communication mechanism and incorporate it into federated learning, the core of which is to modify the communication requirement from uploading all trained parameters periodically to communicating only when necessary. The proposed framework is verified by a real-world offshore wind turbine dataset from six large wind farms in China. An ablation study shows that the proposed framework can maintain high diagnostic performance while reducing communication costs. A comprehensive comparison based on three benchmark models demonstrates that the proposed framework can reduce the communication burden by up to 63% while obtaining better diagnostic performance. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This study was motivated by the problem of collaborative diagnosis of distributed offshore wind turbines under the constraints of data privacy and communication overhead. The method employs a federated learning-based fault diagnosis framework, which permits to obtain global fault diagnosis knowledge without aggregating raw data scattered in each end device, thus avoids the risk of data leakage. Moreover, a strategy integrating parameter variation and accuracy gain is designed to avoid communication redundancy for collaborative training. The practicability and superiority of our proposed framework is demonstrated using extensive experiments against actual industrial data collected from six offshore wind farms.

Distributed Optimization of Islanded Microgrids Integrating Multi-Type VSG Frequency Regulation and Integrated Economic Dispatch

The question of how to simultaneously perform frequency regulation and integrated economic scheduling for microgrids with low-inertia islanding operation under communication constraints is a difficult problem that needs to be solved for many current applications. To this end, this paper establishes a microgrid scheduling control model containing a virtual synchronous generator (VSG) with multiple types of power sources and proposes a distributed optimization algorithm that integrates frequency regulation and comprehensive economic scheduling to simultaneously realize frequency regulation and economic scheduling in a microgrid. Firstly, a distributed economic dispatch problem is proposed based on a comprehensive consideration of the costs and benefits of various types of power VSGs, as well as the overall inertia and standby capacity requirements of the microgrid, which minimizes the integrated costs incurred by the participation of each type of VSG in the frequency regulation and improves the stable operation of the microgrid in terms of frequency under perturbation. Then, the optimal scheduling problem is solved by reconstructing the optimization problem based on considering the dynamic characteristics of microgrid inverters and using event-triggered communication to sense and compensate for the supply-demand imbalance online. The proposed method can avoid inter-layer coordination across time scales, improve the inertia, frequency regulation capability, and economy of the system, and enhance its robustness to short-term communication failures. Finally, simulation results are used to verify the effectiveness of the method.

Practical fixed-time event-triggered output feedback containment control for second-order nonlinear multi-agent systems with switching topologies

This paper studies the fixed-time containment of the second-order nonlinear multi-agent system (MAS) accompanied by switching topologies and event-triggered communication. The agent is subjected to unknown nonlinear dynamics. A fixed-time RBF observer was constructed based on information obtained from neural networks and event-triggered communication. By combining the controller with the identification information from the observer, it is demonstrated that the system can achieve practical fixed-time output feedback containment and exclude Zeno behavior during the control process. The effectiveness of the proposed event-triggered control method for fixed-time output feedback containment control of the second-order nonlinear MAS is verified by simulation examples.

Quantized and event-triggered modeling and fault detection for networked fuzzy systems

This article concentrates on the issue of fault detection in the networked system. Quantized output and event-triggered communication scheme are absorbed into the system analysis, and consequently, by designing a new quantizer with event triggering mechanism, a smooth communication environment without undesired system data is guaranteed. And the imperfect information interaction strategy induced asynchronous membership functions between system and fault detection observer are handled via a differential mean value theorem based approach. Combining the methods of Lyapunov stability theory and linear matrix inequality, we obtain the stability criterion of the T-S fuzzy systems closed by the predefined network channel. In the end, simulation examples demonstrate the validity of the fault detection method.

Optimal leader-following consensus under switching topologies based on event-triggered NN-based observer

This paper proposes a novel control strategy for multi-agent systems (MASs) to achieve optimal leader-following consensus under jointly connected topologies. The devised approach incorporates two key components: an event-triggered neural network (NN)-based leader observer and an NN-based optimal controller. The proposed leader observer allows followers to locally reconstruct the leader’s dynamics, even in scenarios where the communication topology is jointly connected. The implementation of an event-triggered communication protocol significantly reduces the communication burden, enhancing the overall efficiency of information exchange. As the observer’s state converges to the actual leader’s state, the simplification of achieving leader-follower consensus becomes evident by directly setting the state of the leader observer as the tracking goal. An NN-based optimal controller is utilized to approximate the solution to the Hamilton–Jacobi–Bellman (HJB) equation, thus enabling optimal tracking of the leader by followers. Finally, the performance of the proposed control strategy is verified by simulations.

Cooperative Tracking Control for Nonlinear MASs Under Event-Triggered Communication.

The neural network-based adaptive backstepping method is an effective tool to solve the cooperative tracking problem for nonlinear multiagent systems (MASs). However, this method cannot be directly extended to the case without continuous communication. It is because the discontinuous communication results in discontinuous signals in this case, the standard backstepping method is inapplicable. To solve this problem, a hierarchical design scheme that involves distributed cooperative estimators and neural network-based decentralized tracking controllers is proposed. By introducing a dynamic event-triggered mechanism, cooperative intermediate parameter estimators are first designed to estimate the unknown parameters of the leader. By using the interpolation polynomial method, these estimators are extended to smooth estimators with high-order derivatives to guarantee that the backstepping method is applicable. Based on the state of the smooth estimators, a backstepping-based decentralized neural network tracking controller is designed. It is shown that the tracking errors are asymptotically convergent and all the signals in the closed-loop systems are bounded. Compared with the existing cooperative tracking results for nonlinear MASs with event-triggered communication, a more general class of MASs is considered in this article and a better performance in terms of asymptotic tracking is achieved. Finally, a simulation example is given to show the effectiveness of our developed method.

SMO-Based Distributed Tracking Control for Linear MASs With Event-Triggering Communication

SMO-Based Distributed Tracking Control for Linear MASs With Event-Triggering Communication

Distributed Stochastic Consensus Optimization Using Communication-Censoring Strategy

In this paper, a novel communication-efficient distributed stochastic algorithm (referred as CO-DSA) is proposed for solving large-scale consensus optimization problems. As compared to the existing relevant work where only a sublinear convergence rate is obtained for strongly convex and smooth objective functions, CO-DSA achieves a linear convergence rate even in the presence of an event-triggered based communication-censoring strategy. Moreover, by properly setting the threshold function of the event-triggered communication scheme, CO-DSA maintains the same convergence rate as the algorithm without event-triggered communication. This means CO-DSA theoretically yields communication efficiency for free. Numerical experiments verify the theoretical findings and also show the excellent communication saving effect of CO-DSA in large distributed networks.

Finite-Time Consensus of Second-Order Multi-Agent Systems via Event-Triggered Impulsive Control

In this study, the finite-time consensus problem of second-order multi-agent systems via event-triggered impulsive control is investigated. We leverage a novel approach that combines impulsive control, event-triggered communication, and finite-time stability theory. This successful combination achieves finite-time consensus while minimizing communication. In addition, the event-triggered condition determines both the impulsive instants and the update times for the finite-time control. Furthermore, it is shown that the Zeno-behavior and chattering phenomenon can be eliminated under our work. Numerical examples and simulations are provided to validate the effectiveness of the proposed control strategy.

Dynamic Event-Triggered-Based Adaptive Finite-Time Neural Control for Active Suspension Systems With Displacement Constraint.

In this article, we give an event-based control algorithm for nonlinear active suspension systems (ASSs) with the vertical displacement constraint. For in-vehicle communication networks, the controller area network (CAN) is a widely used standard for interconnecting electronic control units (ECUs). The main task in this work is, thus, to reduce the communication burden on the CAN. To this end, we develop a dynamic event-triggered communication mechanism in ASSs. Meanwhile, in practice, the vehicle safety is degraded when the body vibration exceeds the allowable maximum. Thus, the vertical displacement of ASSs should be constrained within a reliable range. For this purpose, we present a novel finite-time integral barrier Lyapunov function (FTIBLF), which not only guarantees that the vertical displacement constraint bounds are not violated, but also enables the position of the suspension to be stabilized in the neighborhood of a desired position in finite settling time. Furthermore, neural networks (NNs) are utilized to identify the unknown nonlinear characteristics in ASSs subjected to the modeling error and unknown mass. As a result, we propose an adaptive neural control technique based on the dynamic event-triggered condition, which helps to improve the ride comfort while ensuring the driving safety and handling stability. Finally, simulation results are provided to verify the validity of the presented methodology.