Dynamic Consensus Research Articles

Droop-Free Distributed Cooperative Control Framework for Multisource Parallel in Seaport DC Microgrid

In this article, a novel distributed cooperative control framework is proposed to solve the problems of unbalanced load current sharing and bus voltage deviation in seaport DC microgrid. This framework without droop control, and a novel droop-free cooperative controller is constructed to achieve current sharing and voltage regulation goals. Each distributed generator (DG) transmits information based on a sparse communication network, and uses a discrete dynamic consensus algorithm (DCA) to complete the accurate estimation of a virtual average state variable containing voltage and current information. In addition, a current limiting function adapted to DCA is designed to avoid control failure and prevent the converter output currents from exceeding the rated value. Finally, the influence of boundary weight, communication delays, and calculation delays on the system stability is analyzed, and a seaport DC microgrid simulation model and StarSim HIL experimental platform are built. The results show that the proposed control scheme can achieve average bus voltage restoration and proportional load current sharing within the allowable range of communication delays and calculation delays, and has prominent plug-and-play capabilities.

On consensus control of nonlinear multiagent systems satisfying incremental quadratic constraints

In this paper, we investigate the problem of leaderless consensus control for the multiagent systems whose nonlinear dynamics satisfying incremental quadratic constraints. A distributed dynamic consensus protocol, decided by communication among neighboring agents, is presented to render nonlinear agent consensus with appropriate coupling weights. Next, an observer-based distributed protocol is considered to ensure consensus of nonlinear system without knowing full state information. Further, extensions to consensus strategies with nonlinear dynamics for the leader-following fashion are also addressed. By comparison to the traditional nonlinear consensus control methodologies, the proposed approach generalizes the Lipschitz nonlinearity as well as the combined nonlinearity of one-sided Lipschitz condition and quadratic inner-boundness condition towards a more generalized type of nonlinearity, which shows us a less conservative result in the Lyapunov proof. Finally, the numerical simulations for six agents are illustrated to show the feasibility and performance of the proposed control protocol with or without the presence of the observer.

Distributed Dynamic Consensus Algorithm for Attitude Estimation

This paper proposes a new dynamic consensus algorithm for the attitude estimation of a rigid body using n Measurement Units (MUs) spatially distributed on the body. The MUs are considered as agents in a sensor network, which is modeled using graph theory. It is assumed that the nth MU shares attitude information only with its neighbor agents. Then, a dynamic consensus algorithm is developed to obtain an average quaternion that describes the general attitude of the rigid body. The objective is to get a more accurate and reliable estimation of the attitude, further providing a fault-tolerant system in the event of failure sensors. This new dynamic consensus algorithm is validated in simulation using three MUs in the network. Results show a better precision and demonstrate that the proposed algorithm provides a good estimation of attitude even in the scenario of possible failure of any of the sensors.

Dynamic consensus of large group emergency decision-making under dual-trust relationship-based social network

Trust relationship is an effective basis for reducing the dimensionality of decision makers (DMs) for group decision making (GDM) problems in the context of social networks, and have received extensive attention in the past few years. However, most existing studies describe the trust relationship only from the single perspective of the interpersonal relationships between DMs, which easily leads to inaccurate decision-making results. In this paper, we propose a dynamic consensus method using a dual-trust relationship-based social network (DTRSN) to solve large group emergency decision-making (LGEDM) problems. First, to more accurately reflect the trust relationship among DMs, a DTRSN based on familiarity-based trust and similarity-based trust is developed, which can comprehensively model the interpersonal relationships and opinion similarity between DMs simultaneously. Next, a DTRSN-based clustering method that considers dual-trust relationships is introduced to divide the large-group DMs into several clusters. Subsequently, to improve the efficiency of LGEDM, the exit-delegation mechanism is modified and introduced into our study, which can speed up consensus by excluding some subgroups whose opinions differ greatly from other subgroups and reserve their influence. Subsequently, a dynamic consensus method based on the modified exit-delegation mechanism is proposed to help DMs reach an agreement quickly. Finally, a case study regarding emergency alternative selection to deal with heavy rainstorms in Zhengzhou, China, is conducted to verify the efficiency of our study, and several comparison and simulation analyses are also conducted to demonstrate the feasibility and practicality of the proposed method.

Distributed Competition of Multi-Robot Coordination Under Variable and Switching Topologies

This paper investigates a distributed competition behavior in multi-robot coordination under variable communication topology and switching one. In terms of multi-robot competition-based coordination, a winner-take-all (WTA) strategy is leveraged to address this issue with inevitable environmental barriers incorporated. Moreover, an innovative control theory stimulated gradient neural network (CTSGNN) algorithm is proposed to realize the WTA with prominent robustness and convergence over the traditional ones. Besides, to adapt to diversified local communication modes among multi-robot systems, fast variable and low switching topologies are constructed to establish two dynamic consensus estimators, accompanied by the proposed distributed control schemes. Traditional algorithms are introduced and served as a contrast. Afterward, the global convergence of the proposed algorithm in dealing with multi-robot competitive coordination, the universality of the application scenario, as well as the weaknesses of traditional methods are substantiated theoretically. The effectiveness and superiority of the proposed CTSGNN algorithm and the resultant distributed control schemes by integrating consensus estimators are further sustained via simulations. Note to Practitioners—The motivation of this paper is the coordination operation of multi-robot systems, but it is also applicable to other fields adopting multi-agent systems. Most of the existing researches on multi-robot coordination only exploit their collaborative behavior, which usually leads to the system redundancy and overflow of control costs. To this end, an innovative control algorithm is proposed for this competitive coordination and remains to be perfect in terms of stability and accuracy. Then, this paper establishes new distributed control schemes for multi-robot systems to compete for optimal dynamic task allocation. In this sense, under the premise of ensuring a successful task execution, only a few individuals with strong abilities and advantages are assigned. It is promising to maximize resource utilization, increase efficiency, and be extended to multi-objective scenarios. Note that the design of this scheme takes into account the environmental constraints and physical constraints of the robot itself. Theoretical analysis and preliminary simulation experiments prove the high efficiency of the control scheme. In ongoing research, the competitive coordination tasks of the mobile robot systems and communication delays or fault in control scheme design are explored to expand the operation scope and system extensibility.

Integrating model explanations and hybrid priors into deep stacked networks for the "safe zone" prediction of acetabular cup.

Existing state-of-the-art "safe zone" prediction methods are statistics-based methods, image-matching techniques, and machine learning methods. Yet, those methods bring a tension between accuracy and interpretability. To explore the model explanations and estimator consensus for "safe zone" prediction. We collected the pelvic datasets from Orthopaedic Hospital, and a novel acetabular cup detection method is proposed for automatic ROI segmentation. Hybrid priors comprising both specific priors from data and general priors from experts are constructed. Specifically, specific priors are constructed based on the fine-tuned ResNet-101 convolutional neural networks (CNN) model, and general priors are constructed based on expert knowledge. Our method considers the model explanations and dynamic consensus through appending a SHapley Additive exPlanations (SHAP) module and a dynamic estimator stacking. The proposed method achieves an accuracy of 99.40% and an area under the curve of 0.9998. Experimental results show that our model achieves superior results to the state-of-the-art conventional ensemble classifiers and deep CNN models. This new screening model provides a new option for the "safe zone" prediction of acetabular cup.

A new linguistic preference relation-based approach for failure mode and effect analysis with dynamic consensus reaching process

In the literature, a lot of alternative failure mode and effect analysis (FMEA) models have been developed to overcome the deficiencies and enhance the performance of traditional FMEA method. However, majority of them evaluated the risk of failure modes directly and cannot take the consensus levels among experts into account. Thus, this article aims to propose a new approach for FMEA via integrating hesitant linguistic preference relations (HLPRs) and an extended dynamic consensus model. Based on the HLPRs, the risk evaluations of experts are described via pairwise comparison of the identified failure modes. An extended dynamic consensus model is proposed to measure the weights of FMEA experts and revise the individual preference information until reaching an acceptable consensus. Finally, the applicability and effectiveness of the proposed FMEA are verified with a practical case study on the risk analysis of supercritical water gasification systems. It was shown that the new approach being proposed provides a practical and flexible way for the risk assessment and prioritization of failure modes in FMEA.

Leader-Following Consensus for a Class of Nonlinear Multiagent Systems Under Event-Triggered and Edge-Event Triggered Mechanisms.

Considering that there are many systems with limited network bandwidth in practice, this article studies the leader-following consensus problem for a class of nonlinear multiagent systems (MASs). The purpose of this article is to reduce unnecessary information transmission between any pair of adjacent agents including the leader in the MASs through intermittent communication. The novel event-triggered and asynchronous edge-event triggered mechanisms are designed for the leader and all edges, respectively. The static and dynamic consensus protocols under these mechanisms are proposed to address the leader-following consensus problem for MASs with Lipschitz dynamics, and the systems will not exhibit Zeno behavior under these two control schemes. Note that the dynamic consensus protocol does not rely on any global values of MASs, it is a fully distributed way. Finally, a practice simulation example is introduced to illustrate the theoretical results obtained.

Fault-tolerant finite-time dynamical consensus of double-integrator multi-agent systems with partial agents subject to synchronous self-sensing function failure

This paper investigates fault-tolerant finite-time dynamical consensus problems of double-integrator multi-agent systems (MASs) with partial agents subject to synchronous self-sensing function failure (SSFF). A strategy of recovering the connectivity of network topology among normal agents based on multi-hop communication and a fault-tolerant finite-time dynamical consensus protocol with time-varying gains are proposed to resist synchronous SSFF. It is proved that double-integrator MASs with partial agents subject to synchronous SSFF using the proposed strategy of network topology connectivity recovery and fault-tolerant finite-time dynamical consensus protocol with the proper time-varying gains can achieve finite-time dynamical consensus. Numerical simulations are given to illustrate the effectiveness of the theoretical results.

Classical dynamic consensus and opinion dynamics models: A survey of recent trends and methodologies

Consensus reaching is an iterative and dynamic process that supports group decision-making models by guiding decision-makers towards modifying their opinions through a feedback mechanism. Many attempts have been recently devoted to the design of efficient consensus reaching processes, especially when the dynamism is dependent on time, which aims to deal with opinion dynamics models. The emergence of novel methodologies in this field has been accelerated over recent years. In this regard, the present work is concerned with a systematic review of classical dynamic consensus and opinion dynamics models. The most recent trends of both models are identified and the developed methodologies are described in detail. Challenges of each model and open problems are discussed and worthwhile directions for future research are given. Our findings denote that due to technological advancements, a majority of recent literature works are concerned with the large-scale group decision-making models, where the interactions of decision-makers are enabled via social networks. Managing the behavior of decision-makers and consensus reaching with the minimum adjustment cost under social network analysis have been the top priorities for researchers in the design of classical consensus and opinion dynamics models.

Modeling and Analysis of Matthew Effect Under Switching Social Networks via Distributed Competition

Dear editor, This letter models and analyzes the Matthew effect under switching social networks via distributed competition. A competitive strategy is leveraged to trace the evolution of individuals in social systems under the Matthew effect. In addition, a consensus filter is utilized to decentralize the model with undirected graphs used to describe the interactions among individuals in social networks. Further, a term is added to the dynamic consensus filter to compensate for the influence of the switching of communication networks on the model. In this letter, the convergence of the proposed Matthew effect model is theoretically proved, and simulative experiments are conducted to verify the availability of the model. In particular, this letter points out the state and evolution of each individual in social systems with distributed competition, and the influence of the social environment on the development of individuals is also intuitively displayed.

Momentum-Aware Trajectory Optimization and Control for Agile Quadrupedal Locomotion

In this letter, we present a versatile hierarchical offline planning algorithm, along with an online control pipeline for agile quadrupedal locomotion. Our offline planner alternates between optimizing centroidal dynamics for a reduced-order model and whole-body trajectory optimization, with the aim of achieving dynamics consensus. Our novel momentum-inertia-aware centroidal optimization, which uses an equimomental ellipsoid parameterization, is able to generate highly acrobatic motions via “inertia shaping”. Our whole-body optimization approach significantly improves upon the quality of standard DDP-based approaches by iteratively exploiting feedback from the centroidal level. For online control, we have developed a novel convex model predictive control scheme through a linear transformation of the full centroidal dynamics. Our controller can efficiently optimize for both contact forces and joint accelerations in single optimization, enabling more straightforward tracking for momentum-rich motions compared to existing quadrupedal MPC controllers. We demonstrate the capability and generality of our trajectory planner on four different dynamic maneuvers. We then present one hardware experiment on the MIT Mini Cheetah platform to demonstrate the performance of the entire planning and control pipeline on a twisting jump maneuver.

Distributed constrained aggregative games of uncertain Euler-Lagrange systems under unbalanced digraphs

In this paper, the constrained Nash equilibrium seeking problem of aggregative games is investigated for uncertain nonlinear Euler-Lagrange (EL) systems under unbalanced digraphs, where the cost function for each agent depends on its own decision variable and the aggregate of all other decisions. By embedding a distributed estimator of the left eigenvector associated with zero eigenvalue of the digraph Laplacian matrix, a dynamic adaptive average consensus protocol is employed to estimate the aggregate function in the unbalanced case. To solve the constrained Nash equilibrium seeking problem, an integrated distributed protocol based on output-constrained nonlinear control and projected dynamics is proposed for uncertain EL players to reach the Nash equilibrium. The convergence analysis is established by using variational inequality technique and Lyapunov stability analysis. Finally, a numerical example in electricity market is provided to validate the effectiveness of the proposed method.

Discrete Laguerre-based model predictive control for dynamic consensus of a vehicle platoon with time delay

Dynamic consensus of linear time-invariant multi-agent systems (MASs) using distributed model predictive control under the influence of input delay is addressed in this technical note. Model predictive control (MPC) is well suited to solve the consensus problem with its ability to handle multivariable systems and constraints. However, the increased computational complexity of MPC restricts its area of applications. An attempt to bridge this gap is made by proposing a Laguerre-based MPC design to bring down the computational load and make it viable for implementation. The effect of input delay on consensus is examined and the stability margin of a MAS with input delay is computed using the location of the closed-loop poles. A comparison of the proposed algorithm with conventional MPC establishes its superiority in convergence time, robustness to input delay and smoothness of the trajectories. The effectiveness of the proposed design is validated through the simulation of dynamic consensus on the benchmark problem of platoon configuration of vehicles. Abbreviations DLQR: discrete linear quadratic regulator; DMPC: distributed model predictive control; LMI: linear matrix inequality; LTI: linear time invariant; SISO: single-input single-output; MAS: multi-agent system; MIMO: multi-input multi-output; MPC: model predictive control

REDCHO: Robust Exact Dynamic Consensus of High Order

This article addresses the problem of average consensus in a multi-agent system when the desired consensus quantity is a time varying signal. Recently, the EDCHO protocol leveraged high order sliding modes to achieve exact consensus under a constrained set of initial conditions, limiting its applicability to static networks. In this work, we propose REDCHO, an extension of the previous protocol which is robust to mismatch in the initial conditions, making it suitable to use cases in which connection and disconnection of agents is possible. The convergence properties of the protocol are formally explored. Finally, the effectiveness and advantages of our proposal are shown with concrete simulation examples showing the benefits of REDCHO against other methods in the literature.

Distributed Quantized Consensus With Recursive Channel Filters Over Directed Networks

This brief investigates the quantized consensus in discrete-time multi-agent systems over directed networks with limited bandwidths of communication channels and limited capacities of physical memories. A dynamic consensus protocol based on the first-order recursive channel filter is proposed. Several conditions for preserving the asymptotic consensus are obtained, which are dependent on the filter parameters, the sampling period and the topologies of the communication networks. Then, by describing the closed-loop multi-agent system via a higher-order state-space model, the effects of the probabilistic quantization are analyzed. We prove that the quantized consensus is reached under the proposed protocol with quantized communications. Moreover, the distributed PI based protocol is discussed as a special case of the proposed dynamic protocol.

Strict Lyapunov Functions for Dynamic Consensus in Linear Systems Interconnected Over Directed Graphs

We study dynamic consensus for general networked (homogeneous) linear autonomous systems, that is, it is only assumed that they are stabilizable. Dynamic consensus pertains to a general form of consensus in which, as a result of the systems’ interactions, they exhibit a rich collective dynamic behavior. This generalizes the classical consensus paradigm in which case all systems stabilize to a common equilibrium point. Our main statements apply to systems interconnected over generic directed connected graphs and, most significantly, the proofs are constructive. Indeed, even though our controllers are reminiscent of others previously used in the literature, to the best of our knowledge, we provide for the first time in the literature strict Lyapunov functions for fully distributed consensus over generic directed graphs.

Dynamic Consensus With Prescribed Convergence Time for Multileader Formation Tracking

This work addresses the problem of distributed formation tracking for a group of follower holonomic mobile robots around a reference signal. The reference signal is comprised of the geometric center of the positions of multiple leaders. This work’s main contribution is a novel Modulated Distributed Virtual Observer (MDVO) for the reference signal. Moreover, the proposed MDVO is based on an exact dynamic consensus algorithm with a prescribed convergence time. In addition, we provide simulation examples showcasing two different application scenarios for the proposal.

Global Optimal Cooperative Control of Multiple DC–DC Converter Systems for Dynamic Consensus

To improve the poor dynamic consensus in multiple dc-dc converter systems caused by parameter differences, this article proposes a global optimal cooperative control strategy. The proposed control strategy achieves dynamic consensus of output currents by minimizing a global performance index, which consists of local errors and neighbor synchronization errors. In this system, each converter is regarded as an agent in the communication digraph, so as to construct a cyber-physical model. Accordingly, a cooperative control method based on the linear quadratic regulator (LQR) is proposed, with the help of nonlinear coordinate transformation techniques. Different from conventional LQR, integral action is introduced to eliminate steady-state errors and improve coordinated performance. Meanwhile, the proof of the stability and consensus of the closed-loop system is given using Lyapunov approach. Moreover, the design guidelines to tune the dynamic response are provided through simulations. The experimental results further demonstrate the effectiveness and superiority of the proposed control method in a multiconverter prototype.

A Unified Framework for Adaptive Leaderless Consensus of Uncertain Multiagent Systems Under Directed Graphs

Due to the nonsymmetric property of the associated Laplacian matrix and the existence of uncertainties in the agent dynamics, the leaderless consensus problem of uncertain multiagent systems under general directed graphs is challenging. Motivated by the classical model reference adaptive control, in this article, we propose a simple yet efficient scheme, called the model reference adaptive consensus, by arranging each agent a reference output to track, where the output is generated by a linear reference model with the relative state measurements as input. We consider two typical agent dynamics, namely, the general linear dynamics with matching uncertainties and the second-order dynamics with extended matching uncertainties. Different linear reference models are designed for the above uncertain agent dynamics. For the first one, dynamical consensus is achieved under a fixed directed graph containing a directed spanning tree. For the latter one, position consensus is achieved under switching uniformly jointly connected graphs. In the proposed algorithms, only the relative states are interacted among the agents and there is no requirement on the communication of virtual signals.