Position Consensus Research Articles

PDE-Based Boundary Adaptive Consensus Control of Multiagent Systems With Input Constraints.

The leader-follower adaptive consensus control problem is addressed for partial differential equations (PDEs) multiagent systems (MASs), and these agents are composed of flexible manipulator systems with input nonlinearity, boundary uncertainties, and time-varying disturbances. Because of the spatial variables in the model, the design of adaptive protocols is more difficult than that of ordinary differential equation (ODE) MASs. By designing the Lyapunov function, a novel distributed boundary control (BC) protocol is constructed, which not only ensures the consensus of angular positions but also suppresses the boundary vibration of each agent. The hybrid effects of dead zones and input saturation on flexible manipulator systems are addressed using the approximation properties of neural networks (NNs). In addition, the disturbance adaptive laws are proposed to provide a control solution for bounded and time-varying disturbances. Furthermore, by applying the Lyapunov stability theory, the uniformly bounded stability of the multiflexible manipulator can be ensured. Finally, the feasibility of the presented control approach is verified using numerical examples.

Data-Driven Optimal Bipartite Consensus Control for Second-Order Multiagent Systems via Policy Gradient Reinforcement Learning.

This article investigates the optimal bipartite consensus control (OBCC) problem for unknown second-order discrete-time multiagent systems (MASs). First, the coopetition network is constructed to describe the cooperative and competitive relationships between agents, and the OBCC problem is proposed by the tracking error and related performance index function. Based on the distributed policy gradient reinforcement learning (RL) theory, a data-driven distributed optimal control strategy is obtained to guarantee the bipartite consensus of all agents' position and velocity states. In addition, the offline data sets ensure the learning efficiency of the system. These data sets are generated by running the system in real time. Besides, the designed algorithm is an asynchronous version, which is essential to solve the challenge caused by the computational ability difference between nodes in MASs. Then, by means of the functional analysis and Lyapunov theory, the stability of the proposed MASs and the convergence of the learning process are analyzed. Furthermore, an actor-critic structure containing two neural networks is used to implement the proposed methods. Finally, a numerical simulation shows the effectiveness and validity of the results.

Scaled Position Consensus of High-Order Uncertain Multiagent Systems Over Switching Directed Graphs.

We investigate the scaled position consensus of high-order multiagent systems with parametric uncertainties over switching directed graphs, where the agents' position states reach a consensus value with different scales. The intricacy arises from the asymmetry inherent in information interaction. Achieving scaled position consensus in high-order multiagent systems over directed graphs remains a significant challenge, particularly when confronted with the following complex features: 1) uniformly jointly connected switching directed graphs; 2) complex agent dynamics with unknown inertias, unknown control directions, parametric uncertainties, and external disturbances; 3) interacting with each other via only relative scaled position information (without high-order derivatives of relative position); and 4) fully distributed in terms of no shared gains and no global gain dependency. To address these challenges, we propose a distributed adaptive algorithm based on a acrlong MRACon scheme, where a linear high-order reference model is designed for every individual agent employing relative scaled position information as input. A new transformation is proposed which converts the scaled position consensus of high-order linear reference models to that of first-order ones. Theoretical analysis is presented where agents' positions achieve the scaled consensus over switching directed graphs. Numerical simulations are performed to validate the efficacy of our algorithm and some collective behaviors on traditional consensus, bipartite consensus, and cluster consensus are shown by precisely choosing the scales of the agents.

Bipartite leaderless position consensus of heterogeneous uncertain multi-agent systems under switching directed graphs

Bipartite leaderless position consensus of heterogeneous uncertain multi-agent systems under switching directed graphs

Distributed cooperative search method for multi-UAV with unstable communications

In search-attack and search-rescue tasks performed by multiple unmanned aerial vehicles (multi-UAV), cooperative search plays an important part. The majority of approaches in use today assume that the UAV swarm’s communication network is complete. However, these links are susceptible to environmental changes or adversary interference. Aiming at cooperative search in search-attack and search-rescue tasks, we propose a distributed cooperative search method for multi-UAV with unstable communications (DCS-UC), which is developed based on ant colony optimization (ACO). The proposed method presents three algorithms to enable the UAV swarm to conduct online cooperative search efficiently and safely. First, a pheromone matrix consensus update technique for usage in ACO is designed under switching and connected topology graphs. This approach can help each UAV’s pheromone matrix achieve consensus, improving the UAV swarm’s cooperative efficiency. Then, a position consensus update algorithm is presented where the position vector, including the positions of all the UAVs in each UAV, can achieve consensus under switching and connected topology graphs. Finally, a collision avoidance algorithm is developed using the determined consistent positions of all the UAVs to address the issue of collision avoidance for all the UAVs. This approach enables the UAV swarm to conduct cooperative search safely. Results from extensive physical simulations performed in Gazebo confirm the benefits of the proposed multi-UAV cooperative search method.

Leaderless Time-To-Go Protocols for Same-Time Position Consensus

We propose three time-to-go consensus protocols to effect the same-time position consensus of multiple agents (or vehicles) to a destination. We prove same-time position consensus to occur through the use of control generated using each of the three protocols viz max-time, min-time, and achievable-time protocols using a backstepping-inspired speed modulation approach for a system of double integrators. Next, we formulate sufficient conditions on the graph communication network topology for consensus to occur through each protocol and propose a tokenized algorithm to effect same-time consensus in systems with arbitrary connected graph communication topologies using the achievable-time protocol. Finally, we verify our conclusions through numerical simulations to further ascertain the relevance of these protocols to diverse scenarios.

A Switched Systems Approach to Multiagent System Consensus: A Relay–Explorer Perspective

The rendezvous or position consensus problem is a fundamental topic within the multiagent system (MAS) literature and has numerous engineering applications. The majority of recent results that solve the position consensus problem rely on communication networks and each agent's ability to obtain position information via direct measurements and communication. Distributed coordination strategies for MASs that are network free and have agents that cannot directly measure position information are scarce. In this work, we develop a relay–explorer strategy to achieve position consensus at a common, desired location. In particular, a group of explorer agents, lacking global position sensors, use open-loop estimators of their position to independently dead reckon toward the desired location. To prevent the difference between the estimated and true position of each explorer from growing beyond a user-defined threshold, a mobile information service provider (relay agent) that is capable of measuring its position in the global coordinate frame, intermittently visits each explorer to provide position information as determined by a maximum dwell-time condition. The relay agent has a position estimator for each explorer, and each estimator is synchronized with the corresponding explorer. The relay agent uses these position estimators to locate each explorer, maneuver to them, and provide position feedback. The contribution of this work over our precursory results is the consideration of uncertain explorer agent dynamics, which are estimated online using recurrent neural networks and integral concurrent learning. The estimated dynamics serve as feed-forward model approximations in the position estimators used by the explorers and relay agent, which generate more accurate position estimates once a finite excitation condition is satisfied. The MAS is modeled as a switched system, and a Lyapunov-based analysis is used to derive a maximum dwell-time condition for each explorer, prove the MAS is exponentially regulated to the desired location, and show the error between the estimated and true explorer dynamics is uniformly ultimately bounded. Experiments and multiple simulation examples are provided to verify the theoretical development, explore the scalability and learning performance of the approach, and shed light on future extensions.

MAIN TRENDS IN PUBLIC ADMINISTRATION DEVELOPMENT IN THE DYNAMICS OF MODERN INFORMATION-TECHNOLOGICAL AND SOCIAL-POLITICAL CHANGES

The article is devoted to the definition of the main trends in the development of public administration in the dynamics of those informationtechnological and socio-political changes that took place at the turn of the 20th–21st centuries in the vast majority of European countries, as well as in Ukraine. Based on the methodology of public administration, the results of the reassessment of the principles and theoretical concepts characteristic of the last century are summarized, which led to the search for a new universal approach to reform in the field of public administration in an interdisciplinary discourse. Ambiguous positions of researchers regarding the capacity of the modern state as a guarantor of security, productivity and sustainability of society are emphasized. It is proven that, thanks to the involvement of the public in the development and implementation of policies, the improvement of the efficiency of public administration, the optimization of the terms and quality of making qualified management decisions demonstrate an important positive trend in the development of public administration both in Ukraine and in many European countries. At the same time, the formation of a tendency towards populism, the erosion of the foundations of democracy and trust in the authorities was emphasized. The ability of management systems to self-regulate and change their internal structure in conditions of aggressive external influences or a critical state is considered in close connection with the practices of social interaction and social partnership. The support of the people and the involvement of citizens in the activities of political institutions ensures a higher level of social legitimation of the authorities and a higher level of the ability of democratic processes to influence the effectiveness of governing bodies. Determining the strengths and weaknesses of deliberative democracy should be considered as a further search for opportunities for decision-making based on the consensus of individual positions in political and management processes. In the context of deliberative democracy approaches, social partnership is defined as a process that strengthens the democratic legitimacy of the political system in general, and the public administration system in particular. Mastering modern communication technologies, professional training of public servants and social interaction can level negative trends and strengthen positive ones in favor of improving the quality of management activities and society's life in general.

Consensus of networked double integrator systems under sensor bias

SummaryA novel distributed control law for consensus of networked double integrator systems with biased measurements is developed in this article. The agents measure relative positions over a time‐varying, undirected graph with an unknown and constant sensor bias corrupting the measurements. An adaptive control law is derived using Lyapunov methods to estimate the individual sensor biases accurately. The proposed algorithm ensures that position consensus is achieved exponentially in addition to bias estimation. The results leverage recent advances in collective initial excitation‐based results in adaptive estimation. Conditions connecting bipartite graphs and collective initial excitation are also developed. The algorithms are illustrated via simulation studies on a network of double integrators with local communication and biased measurements.

Adaptive Appointed-Time Consensus Control of Networked Euler–Lagrange Systems With Connectivity Preservation

With consideration of motion control performance and efficient information communication, the synchronization problem on communication connectivity preservation and guaranteed consensus performance for networked mechanical systems has attracted considerable attention in recent years. Different from the existing works, this article investigates a brand-new appointed-time consensus control approach for uncertain networked Euler-Lagrange systems on a directed graph via exploring the prescribed performance control structure. First, a two-layer prescribed performance envelope is formulated via using an appointed-time convergent function for position-related and velocity-related consensus errors, respectively. Then, a simple state-feedback virtual controller with online adaptive performance adjustment is developed to preserve the communication connectivity. Moreover, to guarantee the velocity consensus of the networked systems and improve the position consensus accuracy, an appointed-time adaptive controller is designed by applying the norm inequality to the system uncertainties and external disturbances. Compared to the existing consensus control approaches, the prime advantage of the proposed one is that the constraints generated from the communication ranges are approximated by a time-varying contractive performance envelope, wherein, the appointed-time convergence and steady-state tracking accuracy are preassigned a priori. Meanwhile, no repeated logarithmic error transformations are required in the relevant controller design, which implies that the complexity of the devised control laws has decreased dramatically. Finally, two groups of illustrative examples are organized to validate the effectiveness of the proposed consensus control approach.

The concept of naturalness (affinity) of law in the context of universal-historical paradigm

The problem of finding a theoretical consensus of jusnaturalist theoretical and legal positions, which has always been relevant, is now becoming one of the most pressing, and the main hopes for its solution are traditionally associated with the philosophy of law. In the article the author formulates the following thesis: the degree of development of human nature, which is a direct source of natural human rights and legal systems derived from these rights, directly depends on the volume and level of cultural development as a process of cultivating one's own ancestral strength.
 The author proposes to understand the accumulated mass of cultural achievements inherited and cultivated by a certain (local or global) human community, which together with the existing natural conditions form the natural basis of the necessary historical form of common way of life the level of cultural and tribal development achieved by them.
 Under the level of cultural development, the author proposes to understand the relationship between a given human community as a subject of nature-transforming practice, based on the available volume of cultural development, and the natural environment as an object of such nature-transforming practice. materialist historiosophical tradition is called the level of development of the productive forces of society.
 Anthropocultural dimension of a kind of "golden section", formed by the ratio of two relations - the level of cultural development (as the ratio between the existing volume of ancestral development of a particular human community and "first" nature) and a set of modifications (or classes) of human types as carriers of certain fragments essence will be a historical type of sociality, which in the framework of the approach proposed by the author will be nothing more than a way to distribute among people the historically accumulated volume of their ancestral development.
 The natural carrier of the naturalness of both social relations and the legal mechanisms of their regulation is a certain human type, the priority of social will over the social will of other human types is a necessary prerequisite for maximizing the pace of cultural development of the community. Natural (or related) will always be the type of legality that guarantees the priority of the social will of the human type, the guarantee of social dominance which ensures the ascending development of society.

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.

UAV Formation Obstacle Avoidance Control Algorithm Based on Improved Artificial Potential Field and Consensus

Aiming at the problem of UAV formation's obstacle avoidance and the consensus of position and velocity in a 3D obstacle environment, a novel distributed obstacle avoidance control algorithm for cooperative formation based on the improved artificial potential field (IAPF) and consensus theory is proposed in this paper. First, the particle model of the UAV and the dynamic model of the second-order system are established, and the topological structure of the communication network of the system is described with the knowledge of graph theory. Second, the attractive potential field function containing the coordination gains factor, the repulsive potential field function containing the influence factor of the repulsive force and the planning angle, and the potential field function between the UAVs containing the communication weight are defined. Then, the variables of position and velocity in the consensus protocol are improved by the reference vector of the formation center and the expected velocity, respectively, and a new formation obstacle avoidance control protocol is designed by combining the IAPF and the theory of consensus. Finally, the Lyapunov function is used to prove the stable convergence of the algorithm. The simulation results show that this method can not only prevent the UAV from colliding with each other while avoiding static and dynamic obstacles but also enable the UAV to quickly restore the expected formation and achieve the consensus of the relative distance, relative height, and velocity.

Finite-time velocity-free observer-based consensus tracking for heterogeneous uncertain AUVs via adaptive sliding mode control

In this study, a finite-time velocity-free position consensus tracking control method is investigated for multiple autonomous underwater vehicle (AUV) systems with heterogeneous uncertain dynamics. First, two distributed finite-time observers are developed for each follower AUV to estimate the state information of the leader and follower-AUVs’ own. Subsequently, via the sliding mode control technique, a consensus tracking control algorithm based on the estimated information is designed, such that each follower AUV can track the leader’s position trajectory within a finite-time, even in the absence of velocity measurement. Second, for the situation involving uncertain model parameters, a parameter adaptive sliding mode-based control algorithm is proposed to improve the performance of the closed-loop AUV system. The two controllers only require the position information of the AUVs. Finally, two numerical simulations are carried out to validate the effectiveness of the two proposed control protocols.

Cardiac causes of death in Moscow and Moscow Region

Rationale: Cardiovascular disorders play a leading role in populational mortality, but they are not synonymous to cardiac pathology. The city of Moscow and the Moscow Region are one of the biggest urban agglomeration worldwide.Aim: Analysis of cardiac-related mortality in Moscow and the Moscow Region.Materials and methods: We calculated standardized mortality coefficients (SMC) for 23 death causes given as separate lines in the Short List of Death Causes by the Russian Agency of Statistics (Rosstat) in 2019.Results: In 2019, SMC for cardiac causes was 171.2 per 100 000 of the population in Moscow (27.6% of all causes) and 248.7 per 100 000 of the population in the Moscow Region (26.9% of all causes). More than 60% of deaths both in Moscow and in the Moscow Region were caused by chronic variants of ischemic heart diseases, whose criteria are not well-defined or are absent. In Moscow, SMC for myocardial infarction is significantly lower, whereas that for unspecified cardiomyopathy (the code by International Classification of Diseases and Health-related Conditions, version 10 (ICD10) I42.9) is higher. SMC from the groups of death causes related to chronic illness possibly unrelated to atherosclerosis, are comparable: 29.64 in Moscow and 24.59 in the Moscow Region. SMC from arterial hypertension (ICD10 I10-13) are 8.7 and 5.6 per 100000 of the population, respectively.Conclusion: Despite higher total cardiac mortality in the Moscow Region, SMCs for individual death causes in Moscow are significantly higher. SMC variability is mostly related to different algorithms for determination of primary death cause. ICD10 and the Rosstat the Short List of Death Causes seem to be suboptimal sources to understand the contribution of cardiac causes into the mortality structure. Any correct comparison would become possible after a consensus of expert positions by specialists in Moscow and Moscow Region on the criteria of diagnosis, choice of primary death cause and corresponding disease code.

Neurodynamic Formation Maneuvering Control With Modified Prescribed Performances for Networked Uncertain Quadrotors

This article investigates a neurodynamic formation maneuvering control problem with guaranteed performances for networked uncertain quadrotors. A modified prescribed performance control (MPPC) with asymmetric behavior boundaries is proposed to enable a better regulation ability for formation synchronization error without any overshoots, and the requirement on obtaining the precise value of initial error can be removed compared with the existing PPC. To deal with uncertainties appearing in individual quadrotors, a state estimator-based minimal learning parameter (SE-MLP) observer capable of reducing online computational burden without offline training is developed, such that a chattering-free transient learning process against disturbances can be ensured. Additionally, by introducing a high-order sliding mode (HOSM) differentiator to replace the linear filter in recursive back-stepping design, fast and accurate time-derivatives of virtual control signals can be generated to further improve the control performance. By fusion of MPPC, SE-MLP, HOSM as well as Lyapunov stability analysis, an enhanced neural guaranteed performance formation maneuvering control scheme for networked uncertain quadrotors is obtained such that position consensus without any overshoots can be achieved. Simulation results are offered to verify the effectiveness of the proposed approach.

On the consensus of nonlinear agents in unknown cluttered environments using random planning

The consensus of multi-agent dynamic systems is a metaphor for many different tasks involving group agreement. However, ensuring consensus in real-world scenarios, with non-convex obstacles and kinodynamic motion constraints, proves to be a hard task, since it is quite difficult to model such a problem analytically. Therefore, this paper studies the problem of state agreement for Multi-Robot Systems (MRS) in unknown cluttered complex environments. Here, we propose and analyze a distributed consensus algorithm combined with a Rapidly-exploring Random Tree-based planner, which allows linear and nonlinear systems to reach a common target on their states inside bi- or three-dimensional spaces filled with static obstacles. We demonstrate that, with enough time, our planning strategy ensures probabilistic completeness convergence independently of the topological communication network employed, since some connectivity constraints are observed. Simulated results with linear and nonlinear models are provided, showing the effectiveness of our proposed method in comparison with the state-of-the-art literature for the specific case of position consensus (rendezvous) missions.

Leader-Following Consensus and Formation Control of VTOL-UAVs with Event-Triggered Communications †.

This article presents the design and implementation of an event-triggered control approach, applied to the leader-following consensus and formation of a group of autonomous micro-aircraft with capabilities of vertical take-off and landing (VTOL-UAVs). The control strategy is based on an inner–outer loop control approach. The inner control law stabilizes the attitude and position of one agent, whereas the outer control follows a virtual leader to achieve position consensus cooperatively through an event-triggered policy. The communication topology uses undirected and connected graphs. With such an event-triggered control, the closed-loop trajectories converge to a compact sphere, centered in the origin of the error space. Furthermore, the minimal inter-sampling time is proven to be below bounded avoiding the Zeno behavior. The formation problem addresses the group of agents to fly in a given shape configuration. The simulation and experimental results highlight the performance of the proposed control strategy.

Georgians of Turkey: the Problem of Identity Searching in the Conditions of the Kurdish-Turkish Conflict

The purpose of the research is to scientifically interpret the processes that took place in modern Turkey and were associated with the destruction (collapse) of a typical for totalitarian ideology system (“all Muslims are Turks”); a comprehensive study of the factors that put non- Turkish ethnic groups into the necessity of replacing the old ideology with a new one.The general scientific principles of objectivity, consistency and comprehensiveness, as well as comparative historical, historical typological, problem-chronological and structural-functional methods became the basis of the research.The scientific novelty of the results is due to the fact that for the first time a thorough study of the socio-economic, socio-political and cultural development of the multiethnic population of Turkey against the background of confrontation between compact ethnic groups and other ethnic communities is being carried out. Conclusions. The ways of solving this situation, which consist in the convergence and consensus of ethnic positions and moral values of ethnic groups, are proposed. Among the Georgian, Armenian and Turkish populations, there are differences in the views on the question autochthony question of the population living in Tao-Klarjeti, in particular the Georgians. The position of the Turks and Armenians is obvious, but the lack of awareness of a certain part of the Georgians about this problem remains incomprehensible. In particular, the positions of Georgians living here differ from each other. Despite these trends, the multiethnic population of Turkey has gained a rich experience of living together with the Turkish people, which needs to be effectively used in order to protect the interests of ethnic mobilization and the preservation of national identity

Pose Consensus of Multiple Robots with Time-Delays Using Neural Networks

SummaryThis paper proposes a novel control scheme based on Radial Basis Artificial Neural Network to solve the leader–follower and leaderless pose (position and orientation) consensus problems in the Special Euclidean space of dimension three (SE(3)). The controller is designed for robot networks composed of heterogeneous (kinematically and dynamically different) and uncertain robots with variable time-delays in the interconnection. The paper derives a sufficient condition on the controller gains and the robot interconnection, and using Barbalat’s Lemma, both consensus problems are solved. The proposed approach employs the singularity-free, unit-quaternions to represent the orientation of the end-effectors in theSE(3). The significance and advantages of the proposed control scheme are that it solves the two pose consensus problems for heterogeneous robot networks considering variable time-delays in the interconnection without orientation representation singularities, and the controller does not require to know the dynamic model of the robots. The performance of the proposed controller is illustrated via simulations with a heterogeneous robot network composed of robots with 6-DoF and 7-DoF.