Multi-agent Systems In Presence Research Articles

Cooperative Identification of Multi-Agent Systems in Presence of Integral Action: Insights from a Two-Agent Framework

This article addresses the parameter inference problem on dynamical systems in a cooperative setting. The dynamical systems contain integral action, which is of practical interest within cyber-physical systems. Given this dynamical characteristic of the problem, existing statistical results on system Identification are not straightforwardly applicable and hence adaptations are required. To this end, a formulation of the problem is presented and results highlighting the challenges of the given setup are discussed. Insights from a two-agent framework are used to formulate results and the next research steps on the topic, where the benefit of performing open-loop Identification on the variance of the parameter is highlighted and bias issues are discussed.

Consensus Tracking of Multi-Agent Systems in Presence of Uncertain Dynamics and Communications Faults

Consensus Tracking of Multi-Agent Systems in Presence of Uncertain Dynamics and Communications Faults

Cloud-based self-triggered coordination for nonlinear multi-agent consensus

This paper addresses the practical tracking consensus problem for first-orderheterogeneous nonlinear multi-agent systems in presence of external disturbances under directed graphs. Agents with limited communication capability interact with each other through a cloud repository, where the information exchange is asynchronous and indirect. The system model is more general than the ones in existing results, moreover, locally Lipschitz nonlinearities and adaptive control schemes are taken into account. It is proven that under the designed self-triggered protocol, agents can track a virtual leader with bounded error and Zeno behaviour is excluded for real implementation. Finally, a numerical simulation demonstrates the effectiveness of the theoretical results.

Scale-free collaborative protocol design for state synchronization of multi-agent systems in presence of unknown nonuniform and arbitrarily large communication delays

In this paper, we study state synchronization problem for homogeneous networks of multi-agent systems subject to unknown, nonuniform and arbitrarily large communication delays. A scale-free design framework utilizing localized information exchange has been adopted. The protocol design is solely based on agent models such that we do not need any information about the communication networks and the number of agents. Moreover, the necessary and sufficient solvability conditions are established.

Distributed event-triggered formation control for multi-agent systems in presence of packet losses

This paper proposes an event-triggered approach for the distributed formation control problem of an Euler–Lagrange multi-agent system with state perturbations, when communications between agents are prone to losses. To evaluate its control input, each agent maintains estimators of its own state and of the states of its neighbors. Each estimator accounts for a different packet-loss hypothesis. Each agent is then able to compute the expected value of the estimation error of its own state as evaluated by its neighbors. A communication triggering condition (CTC) exploiting this expected error is then proposed. An analysis of the behavior of the system with this CTC is performed using stochastic Lyapunov functions. Simulations confirm the effectiveness of the proposed approach.

Adaptive MLP neural network controller for consensus tracking of Multi-Agent systems with application to synchronous generators

In this study a novel cooperative controller design is developed to tackle the consensus tracking problem of Multi-Agent systems (MAS) based on multilayer perceptron neural network (MLPNN), applied on a distributed synchronous generator (SG) Multi-Agent system in presence of model uncertainties and external disturbances. Application of MLPNN in controller design can lead to smoother system response and can neutralize the impacts of model uncertainties. Furthermore, the proposed method benefits from a novel algorithm formerly known as error backpropagation (BP) algorithm to update and to regulate the weights of MLPNN adaptively based on the principles of consensus error. The proposed strategy can be very effective in control of the distributed SG Multi-Agent system due to its ability for system identification, parameter estimation, and disturbance approximation. Moreover, the utilization of neural networks can meet the criterion to make the consensus error uniformly ultimately bounded. Ultimately, simulation results illustrate the applicability and effectiveness of the novel MLPNN controller to model the system uncertainties and to deal with external disturbances of the distributed SG Multi-Agent system.

Event-triggered flocking control of multi-agent systems in presence of cyber attacks

We propose an event-triggered control system for multi-agent systems with the double-integrator network to achieve resilient flocking behavior in the presence of cyberattacks. The method can manage connectivity and increase the robustness of the topology graph via a three-state hybrid control. Each state of hybrid control has a distinct triggering condition. The developed event-triggered update rules can mitigate the influence of the noncooperative agents using the weighted mean subsequence reduced algorithm and reduce unnecessary communication among them. As a result, the performance of the system can effectively improve. Convergence of the velocity and orientation of the agents is guaranteed by using this type of control structure and resilient consensus protocol. We assume that the bound on the number of noncooperative agents in the neighbor sets of cooperative agents is known. The proposed scheme with the event-triggering rules can avoid Zeno behaviors inherently. Finally, a simulation example is worked out to demonstrate the effectiveness of the proposed approach.

A biologically-inspired reinforcement learning based intelligent distributed flocking control for Multi-Agent Systems in presence of uncertain system and dynamic environment

In this paper, we investigate the real-time flocking control of Multi-Agent Systems (MAS) in the presence of system uncertainties and dynamic environment. To handle the impacts from system uncertainties and dynamic environment, a novel reinforcement learning technique, which is appropriate for real-time implementation, has been integrated with multi-agent flocking control in this paper. The Brain Emotional Learning Based Intelligent Controller (BELBIC) is a biologically-inspired reinforcement learning-based controller relying on a computational model of emotional learning in the mammalian limbic system. The learning capabilities, multi-objective properties, and low computational complexity of BELBIC make it a very promising learning technique for implementation in real-time applications. Firstly, a novel brain emotional learning-based flocking control structure is proposed. Then, the real-time update laws are developed to tune the emotional signals based on real-time operational data. It is important to note that this data-driven reinforcement learning approach relaxes the requirement for system dynamics and effectively handle the uncertain impacts of the environment. Using the tuned emotional signals, the optimal flocking control can be obtained. The Lyapunov analysis has been used to prove the convergence of the proposed design. The effectiveness of the proposed design is also demonstrated through numerical and experimental results based on the coordination of multiple Unmanned Aerial Vehicles (UAVs).

Distributed fixed time super-twisting sliding mode leader–follower tracking protocol design for nonlinear networked flexible joint robots

A fixed time super-twisting sliding mode controller is proposed for the leader–follower consensus protocol of a canonical high order affine nonlinear multi-agent system (MAS), in this paper. The proposed methodology is designated to reach the leader–follower consensus in determined convergence time independent of any initial conditions. The focal features of the proposed methodology are as: (1) the robustness of the MAS in presence of both the uncertainties and the external disturbances, (2) the stability of the closed loop MAS in sense of Lyapunov, (3) the finite time tracking of the leader by the followers, (4) the convergence of the sliding surface to zero in finite time, and (5) the chattering reduction. Simulation results perform to authenticate the efficiency of the presented approach.

A biologically-inspired distributed fault tolerant flocking control for multi-agent system in presence of uncertain dynamics and unknown disturbance

In this paper, the problem of flocking for Multi-Agent Systems (MAS) in presence of system uncertainties and unknown disturbances is investigated. A biologically-inspired novel distributed resilient controller based on a computational model of emotional learning in the mammalian brain is proposed. The methodology, known as Brain Emotional Learning Based Intelligent Controller (BELBIC), integrates a resilience mechanism with multi-objective properties into the distributed flocking control strategy. The developed strategy adopts the learning capabilities of BELBIC with the flocking that makes it a very promising approach, especially while dealing with system uncertainties and/or unknown disturbances. Furthermore, the low computational complexity of the designed method makes it very suitable for practical implementation in real-time applications. Eventually, the effectiveness of the developed intelligent resilient distributed flocking control approach is demonstrated through the several simulation scenarios.

A Decentralized Markovian Jump ${\cal H}_{\infty}$ Control Routing Strategy for Mobile Multi-Agent Networked Systems

This paper presents a Markovian jump linear (MJL) system framework for developing routing algorithms in mobile ad hoc networks (MANETs) that encounter changes in the number of nodes and/or the number of destinations. A unified H∞ control strategy is proposed by representing the dynamically changing destination nodes as singular switching control systems. A decentralized routing scheme is proposed and designed for the networked multi-agent system in presence of unknown time-varying delays. To solve the corresponding optimization problem the physical constraints are expressed as linear matrix inequality (LMI) conditions. The resulting decentralized H∞ routing control schemes for both regular and singular MJL systems are shown to formally achieve the desired performance specifications and requirements. Simulation results are presented to illustrate and demonstrate the effectiveness of our proposed novel routing control strategies.