Event-triggered distributed consensus in multi-agent cooperative control

This chapter overviews consensus algorithms in cooperative control. The motivation for information consensus in cooperative control is given. A literature review on consensus algorithms is provided. Theoretical results regarding consensus-seeking under both time-invariant and dynamically changing communication topologies are summarized. A few specific applications of consensus algorithms to multivehicle cooperative control are described. The organization of the monograph is also introduced.KeywordsConsensus AlgorithmCooperative ControlAlgebraic ConnectivityCommunication TopologyAverage ConsensusThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In the field of systems and control, many cooperative control problems of multi-agent systems have been actively studied in the past two decades. This article aims to organize extensive existing work on different cooperative control problems into three categories, based on three different types of graph Laplacian matrices involved. A Laplacian matrix is an important representation of graph topology, and depending on the field of its entries, there are three types: ordinary Laplacian (non-negative diagonal entries and non-positive off-diagonal entries), signed Laplacian (arbitrary real entries), and complex Laplacian (arbitrary complex entries). Each type of graph Laplacian is useful in modeling and solving a different set of cooperative control problems. In particular, their algebraic properties are fundamental in characterizing stability and performance of the respective solution algorithms. To our best knowledge, organizing the literature on multi-agent cooperative control through the lens of different graph Laplacians is new.

Nowadays, the energy-saving optimization problem has gradually become a hot spot in the subject of high-speed train control. Traditional static train trajectory planning is designed offline according to a preplanned timetable, but ignored are the uncertainties of parameters because of line conditions and other factors in the process of operation. Thus, the previous operation strategy may not suit the remaining section. The cooperative control strategy for multiple trains was investigated with the aim of conserving energy between successive stations under the constraints of safety and punctuality. First, combined with the train operation strategy under the quasi-moving block system of the Chinese Train Control System, a train cooperative interaction mechanism based on multiagent input is proposed to obtain the real-time running characteristics of trains on the railway via the Radio Block Center (RBC) agent. In the case of unanticipated events of railway line conditions, train operation is often out of accord with the timetable. Therefore, a resilience set related to trip-time deviation and headway variation is introduced to evaluate the robustness of the railway system and achieve balance in the train group. Also, a multigroup parallel differential evolution algorithm is developed to solve the online optimization problem of a high-speed train group according to real-time information. The proposed approach is analyzed by using operational data from the Wuhan–Changsha highspeed railway line in China to assess energy savings and punctuality. Results of the case study show superior performance and effectiveness of the model, and the methodology is illustrated.

This paper focuses on the cooperative control of multiple robots with unknown control directions. The commonly used Nussbaum-gain approach may result in large overshoot and oscillations. Therefore, we propose a new switching adaptive control method to solve the leaderless and leader-following consensus problem for multiple robots. The proposed switching strategy can make the controller quickly adapt to the unknown control directions, resulting in a better transient control performance. Moreover, the presented controller can steer multiple robots to a common value in finite time regardless of the heterogeneous unknown control directions and fully unknown nonlinearities in the robot dynamic models. This implies that the controller can achieve fast convergence rate and have strong robustness. In order to make the controller distributed, new local supervisory functions are constructed to design the logic-based switching rule. These supervisory functions are local, meaning that they are only dependent on local state information. No neigborhood/global information are needed. Simulations and experiments verify the obtained results.

Internet of Vehicle (IoV) has become a hot topic in the last years due to its significant benefits in transportation systems in terms of quality, costs, and traffic management capability. Indeed, accurate and real-time information acquisition and diffusion are required to reach an efficient traffic management system according to the smart city paradigm. In this context, this article provides a novel IoV-based distributed double layer control architecture to address the coordination control problem of connected autonomous vehicles (CAVs) multiple platoons negotiating the access to a road section restriction in the presence of heterogeneous communication time-varying delays. To this aim, leveraging the IoV paradigm and the equivalent virtual formation concept, we propose a novel distributed diffusive longitudinal protocol and a distributed lateral potential-function-based control strategy, where both of them are able to cope with communication delays and ensuring the multiplatoon coordination in finite time, i.e., before accessing the road section restrictions, so to avoid vehicles collisions. The finite-time stability of both the cooperative controllers is analytically proved by exploiting the Lyapunov–Krasovskii theory and the derived delay-dependent stability conditions, expressed as a set of linear matrix inequalities (LMIs), allows the proper tuning of the controllers gains. Numerical analysis confirms the theoretical derivation and discloses the effectiveness and the robustness of the proposed cooperative multiplatoon control in ensuring the safe crossing of the road section restriction in finite time.

Parameter uncertainty is one of the key factors that affect the performance of train systems. In order to obtain good tracking and cooperation performances and to improve line utilization, this paper proposes a sliding mode surface-based cooperation control scheme for multiple high-speed trains subject to parameter uncertainty. Based on the single-mass point model, multiple high-speed trains can be modeled as a quasi-multi-agent system with the leader–follower model. Considering the parameter uncertainty of the system, the non-singular terminal sliding mode surface is applied and adaptive control laws are designed to estimate the unknown parameters and external disturbances. A dual jitter suppression mechanism-based cooperation control drive strategy is presented in order to achieve the following objectives: (1) the leading train can track the desired trajectory very well; (2) one train can follow the adjacent train in front at the desired safe interval; and (3) the cooperation performances can be obtained for the quasi multi-agent system. Lyapunov functions are defined to prove the stability of the system, and simulation experiments show that the proposed cooperation control scheme is feasible and effective. According to the presented control scheme applied for multiple high-speed trains, the line utilization rate can be greatly improved.

The inevitable presence of non-ideal communication conditions in open wireless communication networks leads to insufficient coordination and optimization among mixed traffic flows during cloud-based centralized cooperative scheduling and control in intersection systems, thereby compromising traffic safety and operational efficiency. This situation imposes heightened requirements for coordinated operation between mixed vehicle platoons composed of conventional Human-Driven Vehicles (HDVs) and Intelligent Connected Vehicles (ICVs) under imperfect communication conditions. To address these challenges, this paper proposes a multi-vehicle model-free adaptive prediction and cooperative control method for intersection scenarios that considers time-varying delays and false data injection attacks. By employing a compact-form dynamic linearization prediction scheme and an enhanced model-free receding horizon predictive control technique, we construct a networked adaptive prediction and cooperative controller for mixed vehicle clusters. This framework enables the estimation and compensation of multi-vehicle state information within mixed vehicle groups, followed by the distribution of calculated adjustment speeds to target controlled ICVs. The proposed approach guides mixed vehicle platoons to achieve dynamic formation adjustments in response to environmental variations while enhancing coordinated control performance under time-varying delays and false data attacks through regulation of vehicle queue speeds. Simulation results verify that this methodology effectively improves the traffic efficiency at intersections based on safety.

A stochastic adaptive control problem where complete state information is not available to the controller is considered. The system is modeled as a finite stochastic automaton. These models are a slight generalization of the more common partially observable controlled Markov chain models.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

We present a lightweight approach to capture abstract state information that can be used to avoid testing redundant interleavings of multithreaded programs. Our approach is based on modeling states that are observed during the test executions as a Petri net. This model is then used to determine if some execution paths lead to an already explored state. In such cases exploring execution paths from the same state multiple times can be avoided. Our approach does not capture the complete global states of programs but instead it relies on particular commutativity of transitions to determine if they lead to already known abstract states. We have combined this lightweight state capture technique with a dynamic symbolic execution based approach to systematically test multithreaded programs. Experiments show that even without complete state information, the lightweight state capturing technique can sometimes reduce the number of redundant test executions substantially.

In this paper, we investigate the problem of the discrete-time adaptive control design of robotic manipulators by utilizing the characteristic model theory. Different from the traditional plants manipulated by the characteristic model-based control strategy, the Coriolis matrix in the robotic dynamics makes the bound of the characteristic model coefficients rely on the state information, proposing great challenge for the controller design and stability analysis. To conquer this problem, we develop a projection estimation algorithm depending on the state information, and propose an adaptive control law via introducing a discrete-time sliding vector. A new stability analysis framework is proposed by use of the recursive induction methodology, giving rise to the asymptotic convergence of joint positions. Simulations are performed to test the effectiveness of our methodology. Our design extends the characteristic model theory frame by proposing the control design and stability analysis methods for the the characteristic model with the state-relevance coefficients.

The stable operation of the integrated energy system (IES) is affected by the fluctuation of heat or electricity loads, energy or communication problems, and the interaction of different energy networks. To overcome above challenges, firstly, the integrated dynamic models with three-time scales are established for control design, fully considering dynamic behaviors, energy coupling, and constraints of power flow including electrical, heat and gas on network stability. Then, a novel distributed model predictive control (DMPC) consensus algorithm with process control and trajectory control is proposed to achieve stable operation for IES based on second-order cone programming (SOCP). A unified distributed cooperative control mode is formed in IES by implementing the proposed stability control method, which is suitable for different forms of energy. The control design takes into account the different dynamic characteristics of energy equipment and the energy coupling feature, which realizes the multi-scale time cooperation and dynamic process control for IES by tuning the control parameters. The optimal solution is obtained by rolling optimization for achieving frequency/voltage/temperature/pressure/flow regulation and consensus objectives. Finally, the proposed model and method are validated in various simulation scenarios, such as load variation, energy flow variation, nodes removed, and topology change.

General Framework using Affine Transformations to Formation Control Design

Simultaneous arrival of multiple UAVs is one of the classic applications in cooperative control. In this paper, the expected time of arrival (ETA) is selected as the coordination variable, and the consensus algorithm is used to realize the air aggregation and simultaneous arrival of multiple UAVs. Based on the comprehensive consideration of UAV speed constraints, acceleration constraints and target motion model, according to the different starting point, flight direction and distance, the control algorithm is simulated and verified. The simulation results show that the algorithm is effective, robust and flexible.

The scheme of Quality of Service (QoS) routing algorithms based on local state information has recently been proposed as an alternative approach to the traditional QoS routing algorithms. By implementing this localized QoS routing algorithm, each source node predetermines and maintains a set of candidate paths for each destination. These sets of paths will help the source node to decide the most appropriate path for a connection request. Hence, it helps to avoid the problems associated with the maintenance of the global network state information. In this paper, we propose a new and effective localized QoS routing algorithm, compare its performance with those of other localized algorithms and a traditional QoS routing algorithm under the same type of network topology, QoS requirements and traffic patterns. The simulations results show that our proposed algorithm can perform better than other routing algorithms. DOI: 10.32913/rd-ict.vol3.no14.258

In this paper we consider the control of a reentrant manufacturing system with setup times, as introduced by Kumar and Seidman. In most literature on control of a network of servers with setup times, first a policy is introduced and then the resulting network behavior is analyzed. In manufacturing systems the network typically is fixed and givena priori. Furthermore, optimal steady state behavior is desired. Therefore, this paper follows a different approach. First optimal steady state network behavior is determined, then a feedback controller is presented which makes the network converge towards this desired steady state behavior. The resulting controller is a non‐distributed controller: each server needs global state information. For a manufacturing system this is not a problem, since global information typically is available. Finally it is shown that a distributed controller (each server needs only local state information) can also be used to achieve the same result. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society