Event-Triggered Algorithms for Leader-Follower Consensus of Networked Euler-Lagrange Agents

This paper proposes three different distributed event-triggered control algorithms to achieve leader-follower consensus for a network of Euler-Lagrange agents. We firstly propose two model-independent algorithms for a subclass of Euler-Lagrange agents without the vector of gravitational potential forces. By model-independent, we mean that each agent can execute its algorithm with no knowledge of the agent self-dynamics. A variable-gain algorithm is employed when the sensing graph is undirected; algorithm parameters are selected in a fully distributed manner with much greater flexibility compared to all previous work concerning event-triggered consensus problems. When the sensing graph is directed, a constant-gain algorithm is employed. The control gains must be centrally designed to exceed several lower bounding inequalities which require limited knowledge of bounds on the matrices describing the agent dynamics, bounds on network topology information and bounds on the initial conditions. When the Euler-Lagrange agents have dynamics which include the vector of gravitational potential forces, an adaptive algorithm is proposed which requires more information about the agent dynamics but can estimate uncertain agent parameters. For each algorithm, a trigger function is proposed to govern the event update times. At each event, the controller is updated, which ensures that the control input is piecewise constant and saves energy resources. We analyse each controllers and trigger function and exclude Zeno behaviour. Extensive simulations show 1) the advantages of our proposed trigger function as compared to those in existing literature, and 2) the effectiveness of our proposed controllers.Comment: Extended manuscript of journal submission, containing omitted proofs and simulation

The interaction between the PARP10 protein and the NS1 protein of H5N1 AIV and its effect on virus replication

<p>Abstract</p> <p>Background</p> <p>During the process that AIV infect hosts, the NS1 protein can act on hosts, change corresponding signal pathways, promote the translation of virus proteins and result in virus replication.</p> <p>Results</p> <p>In our study, we found that PARP domain and Glu-rich region of PARP10 interacted with NS1, and the presence of NS1 could induce PARP10 migrate from cytoplasm to nucleus. NS1 high expression could reduce the endogenous PARP10 expression. Cell cycle analysis showed that with inhibited PARP10 expression, NS1 could induce cell arrest in G2-M stage, and the percentage of cells in G2-M stage rise from the previous 10%-45%, consistent with the cell proliferation result. Plague forming unit measurement showed that inhibited PARP10 expression could help virus replication.</p> <p>Conclusions</p> <p>In a word, our results showed that NS1 acts on host cells and PARP10 plays a regulating role in virus replication.</p

Interaction of influenza virus NS1 protein with growth arrest-specific protein 8

NS1 protein is the only non-structural protein encoded by the influenza A virus, and it contributes significantly to disease pathogenesis by modulating many virus and host cell processes. A two-hybrid screen for proteins that interact with NS1 from influenza A yielded growth arrest-specific protein 8. Gas8 associated with NS1 in vitro and in vivo. Deletion analysis revealed that the N-terminal 260 amino acids of Gas8 were able to interact with NS1, and neither the RNA-binding domain nor the effector domain of NS1 was sufficient for the NS1 interaction. We also found that actin, myosin, and drebrin interact with Gas8. NS1 and β-actin proteins could be co-immunoprecipitated from extracts of transfected cells. Furthermore, actin and Gas8 co-localized at the plasma membrane. These results are discussed in relation to the possible functions of Gas8 protein and their relevance in influenza virus release

Leader Tracking of Euler-Lagrange Agents on Directed Switching Networks Using a Model-Independent Algorithm

In this paper, we propose a discontinuous distributed model-independent algorithm for a directed network of Euler-Lagrange agents to track the trajectory of a leader with nonconstant velocity. We initially study a fixed network and show that the leader tracking objective is achieved semiglobally exponentially fast if the graph contains a directed spanning tree. By model independent, we mean that each agent executes its algorithm with no knowledge of the parameter values of any agent's dynamics. Certain bounds on the agent dynamics (including any disturbances) and network topology information are used to design the control gain. This fact, combined with the algorithm's model independence, results in robustness to disturbances and modeling uncertainties. Next, a continuous approximation of the algorithm is proposed, which achieves practical tracking with an adjustable tracking error. Last, we show that the algorithm is stable for networks that switch with an explicitly computable dwell time. Numerical simulations are given to show the algorithm's effectiveness

Model-independent trajectory tracking of Euler-Lagrange agents on directed networks

The problem of trajectory tracking of a moving leader for a directed network where each fully-actuated agent has Euler-Lagrange self-dynamics is studied in this paper using a distributed, model-independent control law. We show that if the directed graph contains a directed spanning tree, with the leader as the root node, then a model-independent algorithm semi-globally achieves the trajectory tracking objective exponentially fast. By model-independent we mean that each agent can execute the algorithm with no knowledge of the agent self-dynamics, though reasonably, certain bounds are known. For stability, a pair of control gains for each agent are required to satisfy lower bounding inequalities and so design of the algorithm is centralised and requires some limited knowledge of global information. Numerical simulations are provided to illustrate the algorithm's effectiveness

Bearing-Only Measurement Self-Localization, Velocity Consensus and Formation Control

Self-localization and formation control tasks are considered when each agent in a multiagent formation observes its neighbors but does not communicate. Each agent is restricted to a predefined motion type on a 2-D plane and collects bearing-only measurements over a time interval to localize neighboring agents. The localization process is used by a three agent formation to achieve velocity consensus combined with formation shape control. Simulations are provided and noisy bearing measurements are investigated

Multiagent Self-localization Using Bearing Only Measurements

This paper proposes a two stage approach to solving a simple network localization problem arising in the control of multi-vehicle formation shapes using bearing-only measurements. While it is impossible for one agent to localize, in its own coordinate basis, a second agent undergoing arbitrary plane motion using bearing-only measurements, this paper shows how to use a combination of a Fourier Transform and an overdetermined linear system of equations to allow two agents undergoing plane circular motion to localize each other. It is postulated that each agent only knows the parameters fully describing its own motion and must determine enough parameters of the other agent to localize it. A Fourier Transform of the measured bearing is used by each agent to obtain an approximate magnitude of the other agent's angular velocity and a two-dimensional search grid is used in an overdetermined linear equation system to solve the localization problem. The paper investigates the effect of noise in bearing measurements on the accuracy of the proposed method, offering some potential methods of decreasing the effect of noise

Event-based leader-follower consensus for multiple Euler-Lagrange systems with parametric uncertainties

An adaptive, distributed, event-triggered controller is proposed in this paper to study the problem of leader-follower consensus for a directed network of Euler-Lagrange agents. We show that if each agent uses the proposed controller, the leader-follower consensus objective is globally asymptotically achieved if the directed network contains a directed spanning tree with the leader as the root node. We provide a trigger function to govern the event time; at each event time the controller is updated. In doing so, we also obtain an explicit lower bound on the time interval between events and thus we conclude that the proposed controller does not exhibit Zeno behavior. Simulations are provided which show the effectiveness of the proposed controller. Also shown in the simulations is the piecewise constant nature of the control law; this significantly reduces the number of updates required by each actuator, thereby saving energy resources