Adaptive connectivity control in networked multi-agent systems: A distributed approach.

Nowadays, the efficiency of multiagent systems in wireless sensor networks prompts the researchers to use these emerging mobile software packets in different simulated approaches or real-world applications. Heterogeneous and distributed wireless sensor networks could be integrated with the multiagent systems to map the real-world challenges into the artificial intelligence world. The multiagent systems have been applied from simulated approaches like object detection/tracking, healthcare, control/assistant, and security systems to real-world applications, including medical/human-care systems and unmanned aerial vehicles. Furthermore, the integration of wireless sensor networks with multiagent systems have emerged novel application, which is known as mobile robots. However, the extensive use of mobile agents in wireless sensor networks has posed different challenges for researchers, including security, resource, and timing limitation. In this work, we review recent simulated approaches and real-world applications of multiagent systems in wireless sensor networks, in which a set of common factors about the things that have been studied are extracted and compared to analyze the performance of mobile agent–based systems in the wireless sensor networks, as well. This analysis provides new research directions about multiagent systems in wireless sensor networks for interested researchers. Finally, a novel framework for dealing with the challenges of multiagent-based applications in the wireless sensor networks which have been mentioned is suggested.

Consensus protocols are distributed algorithms in networked multi-agent systems. Based on the local information, agents automatically converge to a common consensus state and the convergence speed is determined by the algebraic connectivity of the communication network. In order to achieve a fast consensus seeking, we propose the multi-hop relay protocols, where each agent can expand its knowledge by employing multi-hop paths in the network. We demonstrate that multi-hop relay protocols can enlarge the algebraic connectivity without physically changing the network topology. Moreover, communication delays are discussed and a tradeoff is identified between the convergence speed and the time delay sensitivity.

The paper deals with the consensus convergence analysis of discrete-time single-integrator multi-agent systems (MASs) in switching networks. Here, we suppose that the switching network is composed of strongly connected digraphs. By adopting a time-varying quadratic Lyapunov function, we give the rigorous proof that the consensus of the single-integrator MASs can be achieved by either a constant-gain protocol or a decaying-gain protocol. This Lyapunov function based consensus analysis method is different from some existed ones for unbalanced switching networks (e.g., the property of products of indecomposable and aperiodic matrices) and provides a new perspective to study the consensus problem of MASs in unbalanced switching networks with uncertainties (e.g., additive noises).

ABSTRACTIn this paper, we first propose a self-tuning distributed observer for a multi-agent system, which is capable of providing the estimation of the leader's signal to various followers without assuming all the followers know the system matrix of the leader. We then further develop a novel adaptive distributed control law to solve the cooperative output regulation problem for linear heterogeneous multi-agent systems. This control law offers two advantages in that it makes less use of the information of the network as well as the information of the leader and external disturbances, and it avoids calculating the observer gain.

Creating a suitable growing environment is necessary to ensure good plant growth in a plant factory, which requires wireless sensor networks (WSNs) to monitor the environment in real time. However, existing WSN clustered routing methods hardly take into account the network unreliability caused by varying link quality among nodes, resulting in reduced stability and accuracy of environmental monitoring. This study proposes a wireless sensor network system strategy for improving network reliability in large-scale reliable wireless sensor networks suitable for plant factory scenarios. Firstly, a hybrid wireless sensor network was designed and built based on Wi-Fi and ZigBee communication protocols. Secondly, a nonlinear link quality prediction model for plant factory scenarios was developed using a function fitting method, taking into account the interference and attenuation caused by the dense concentration of agricultural facilities and plants in plant factories on the wireless signal propagation. Finally, a network coverage optimization scheme was designed by combining a particle swarm optimization (PSO) algorithm and link quality prediction model, and a reliable cluster routing protocol was designed by combining K-means algorithm. The results indicated that the coefficient of determination (R2) for the prediction model was 0.9962. The impact of agricultural facilities and vegetation on link quality was most significant when the node height was 0.7 m. Under the optimal node deployment, the number of nodes was 33, and the network coverage rate (CR) reached 97.512%. Compared with the traditional clustered routing method, the wireless sensor network designed in this study is more applicable to the field of plant factories; it further enhances data transmission effectiveness and link quality, improves the reliability of the network, and realizes the load balancing of the internal transmission of the network, which in turn ensures the accuracy of environmental monitoring and the stability of the system.

In this paper, we investigate two classes of consensus protocols for networked multi-agent systems: linear time-invariant (LTI) systems and linear time-delay systems. Based on the topology of multi-agent systems, the first-order integrator model is developed. The digraph (directed graph) is employed to show the topology of networked systems, and then a consensus convergence criterion is established. For LTI systems, we prove that their consensus value will converge globally asymptotically to the convex hull of initial states. By solving a set of linear equations, we get the convex combinations of equilibria, and we obtain the convex value of continuous system. If the topology is fixed and time-invariant, the consensus value of the linear time-delay system is also the convex hull of the initial states and is identical to the LTI system. Finally, a network of six agents is presented to show the effectiveness of the results of this paper.

This paper investigates an observer-based control strategy for networked multi-agent systems with general linear dynamics and time-varying communication delays in a sampling setting. The communication topology is assumed to be directed fixed. Different from a traditional observer-based controller for a single system, the objects that need to be observed here are the state differences between an agent and its neighbours. Using a system transformation method, some equivalent conditions concerning the consensus of multi-agent systems are established. Moreover, we prove that both the connection weights and the communication topology play an important role in the study of multi-agent systems, and establish a linear matrix inequality based observer design method.

Emerging Industrial Internet-of-Things systems require wireless solutions to connect sensors, actuators, and controllers as part of high data rate feedback-control loops over real-time flows. A key challenge is to provide predictable performance and agility in response to fluctuations in link quality, variable workloads, and topology changes. We propose WARP to address this challenge. WARP uses programs to specify a network's behavior and includes a synthesis procedure to automatically generate such programs from a high-level specification of the system's workload and topology. WARP has three unique features: (1) WARP uses a domain-specific language to specify stateful programs that include conditional statements to control when a flow's packets are transmitted. The execution paths of programs depend on the pattern of packet losses observed at runtime, thereby enabling WARP to readily adapt to packet losses due to short-term variations in link quality. (2) Our synthesis technique uses heuristics to improve network performance by considering multiple packet loss patterns and associated execution paths when determining the transmissions performed by nodes. Furthermore, the generated programs ensure that the likelihood of a flow delivering its packets by its deadline exceeds a user-specified threshold. (3) WARP can adapt to workload and topology changes without explicitly reconstructing a network's program based on the observation that nodes can independently synthesize the same program when they share the same workload and topology information. Simulations show that WARP improves network throughput for data collection, dissemination, and mixed workloads on two realistic topologies. Testbed experiments show that WARP reduces the time to add new flows by 5 times over a state-of-the-art centralized control plane and guarantees the real-time and reliability of all flows.

This paper, provides a new control scheme, to solve consensus problem in multi agent networked system. This controller design is based on adaptive neuro-fuzzy inference system. Because of changing network topology in wireless sensory networks and existence of noise in harsh industrial environments, it is important to use robust controller to tolerate these conditions. Fast convergence and less sensitivity to model uncertainties are the main benefits of our controller. Simulation results shows flexibility of this controller can decrease effects of noise and disturbance and improve the performance of networked system.

Based on the algebraic graph theory, the networked multi-agent continuous systems are investigated. Firstly, the digraph (directed graph) represents the topology of a networked system, and then a consensus convergence criterion of system is proposed. Secondly, the issue of stability of multi-agent systems and the consensus convergence problem of information states are all analysed. Furthermore, the consensus equilibrium point of system is proved to be global and asymptotically reach the convex combination of initial states. Finally, two examples are taken to show the effectiveness of the results obtained in this paper.

For constructing a decentralized optimal control strategy for networked multi-agent systems that can accomplish consensus in a leaderless structure, we employ the inverse optimal control approach which circumvents the task of solving a HJB equation by introducing a cooperative control Lyapunov function. The decentralized controller we obtain is more flexible than the controller u = Lx in fixed network, because it not only has the advantages to handle systems of high relative degree, but it can also improve the performance of networked multi-agent systems by adjusting a positive definite and symmetrical matrix. On the other hand, the decentralized controller is optimal with respect to a meaningful cost functional which is useful to quantify and improve the performance of networked multi-agent systems. Finally, examples are presented to illustrate both the design process and performance of cooperative controls.

In this paper, dynamic protocols for multi-agent systems are designed and presented to reach consensus on different scenarios. The architecture of such group of agents are with interconnected scattered topology. In static synchrony communication circumstance, a consensus protocol which is depicted in linear dynamic-system form, is developed to make group of agents finally obtain consensus. The consensus pro- posed can be employed to solve average-consensus problem as well. Faster consensus process is usually required, because in real applications agents should interact (or, make decision) swiftly. Aiming at faster consensus progress and reducing the consensus time, a improved consensus protocol for accelerated consensus is also presented, by adding an acceleration design- parameter. Simulation experiments shows the efficacy and efficiency of such consensus protocols.

Observer-based HOFA predictive cooperative control for networked multi-agent systems under time-variant communication constraints

Wireless mesh sensor networks are being deployed today in various monitoring and control applications. Some radio network designs, such as ZigBee, presume that radio connectivity is reasonably consistent over time. Others take the opposite approach of presuming that links are entirely unreliable, and build large degrees of physical redundancy into the network in the hope that a collection of redundant but unreliable individual links will result in a reliable overall system. Surprisingly little work has been done in the middle ground, endeavoring to understand the root cause of link failure in real-world factory environments and applying this knowledge in the design of protocols that adoptively detect and use workable radio channels. In collaboration under a Department of Energy grant for the Industries of the Future, General Electric and Sensicast Systems have studied theoretical and actual performance of 2.4 GHz IEEE 802.15.4 radio transceivers on the lab bench and on the factory floor, with particular attention to jamming from 802.11 and multipath fading. Temporal and frequency variations in link quality are explored. The implications for network reliability and protocol design are discussed

Networked multiagent systems consist of interacting agents that locally exchange information. Since these systems do not in general have a centralized architecture to monitor the activity of each agent, resilient distributed control system design for networked multiagent systems is essential in providing high system performance, reliability, and operation in the presence of system uncertainties. An important set of classes of such system uncertainties that can significantly deteriorate the achievable closed-loop system performance is sensor, actuator, and plant uncertainties, with sensor and actuator uncertainty possibly arising due to adversarial sensor and actuator compromise. This paper presents a novel distributed robust adaptive control architecture for leader-follower networked multiagent systems with undirected communication graph topologies to mitigate the effects of time-varying sensor and actuator attacks, and timeinvariant plant parameter uncertainty. The proposed adaptive control architecture guarantees uniform ultimate boundedness of the controlled closed-loop system. Finally, two numerical examples are provided to illustrate the efficacy of the proposed distributed robust adaptive control architecture.