Pull-Based Distributed Event-Triggered Consensus for Multiagent Systems With Directed Topologies

Maximum Correntropy Filtering for Complex Networks With Uncertain Dynamical Bias: Enabling Componentwise Event-Triggered Transmission

This article is concerned with the maximum correntropy filtering (MCF) problem for a class of nonlinear complex networks subject to non-Gaussian noises and uncertain dynamical bias. With aim to utilize the constrained network bandwidth and energy resources in an efficient way, a componentwise dynamic event-triggered transmission (DETT) protocol is adopted to ensure that each sensor component independently determines the time instant for transmitting data according to the individual triggering condition. The principal purpose of the addressed problem is to put forward a dynamic event-triggered recursive filtering scheme under the maximum correntropy criterion, such that the effects of the non-Gaussian noises can be attenuated. In doing so, a novel correntropy-based performance index (CBPI) is first proposed to reflect the impacts from the componentwise DETT mechanism, the system nonlinearity, and the uncertain dynamical bias. The CBPI is parameterized by deriving upper bounds on the one-step prediction error covariance and the equivalent noise covariance. Subsequently, the filter gain matrix is designed by means of maximizing the proposed CBPI. Finally, an illustrative example is provided to substantiate the feasibility and effectiveness of the developed MCF scheme.

Fully Distributed Event-Driven Adaptive Consensus of Unknown Linear Systems

This article considers the consensus problem of unknown linear multiagent systems (MASs) through adaptive event-driven control in leader-follower and leaderless networks. The proposed event-driven algorithms do not involve any global information related to the network communication structure and rely only on local information exchange to achieve consensus on MASs and are therefore fully distributed. Furthermore, the constraint of continuous communication among the agents is eliminated in terms of control law updates and triggering state monitoring. Another desirable aspect of this article is that the design process of the control algorithms is independent of the parameters of each agent's dynamics and thus does not require precise information about the dynamics of MASs. We further exclude the Zeno behavior of each agent by proving the existence of a strict positive lower bound between any two adjacent events. Finally, the effectiveness of the proposed adaptive event-driven algorithms is verified by a simulation example.

Observer-Based Output Feedback Event-Triggered Adaptive Control for Linear Multiagent Systems Under Switching Topologies

The consensus problem of general linear multiagent systems (MASs) is studied under switching topologies by using observer-based event-triggered control method in this article. On the basis of the output information of agents, two kinds of novel event-triggered adaptive control schemes are designed to achieve the leaderless and leader-follower consensus problems, which do not need to utilize the global information of the communication networks. Finally, two simulation examples are introduced to show that the consensus error converges to zero and Zeno behavior is eliminated in MASs. Compared with the existing output feedback control research, one of the significant advantages of our methods is that the controller protocols and triggering mechanisms do not rely on any global information, are independent of the network scale, and are fully distributed ways.

Quasisynchronization of Heterogeneous Neural Networks With Time-Varying Delays via Event-Triggered Impulsive Controls

Time delays are unavoidable since they are ubiquitous and may have a great impact on the performance of neural networks. Resources efficiency is a common concern in many networked systems with limited resources. This article investigates quasisynchronization of the heterogeneous neural networks with time-varying delays via event-triggered impulsive controls which combine the impulsive control and the event-triggered technique. The centralized and distributed event-triggered impulsive controls are, respectively, presented. The suitable Lyapunov functions are constructed, and the triggering functions are derived, which guarantee that not only are the synchronization errors less than a non-negative bound but also the Zeno behaviors can be eliminated. It is suggested that the distributed one has great superiority in taking up fewer resources compared with the time-triggered impulsive control. Numerical examples are proposed to verify the validity of the centralized and distributed control methods.

Decision and Event-Based Fixed-Time Consensus Control for Electromagnetic Source Localization

This article deals with the problem of electromagnetic source localization (ESL). An evolutionary particle filter, which is first used to make a decision on the positions of electromagnetic sources, has two characteristics. One characteristic is that the number of particles can be significantly reduced while the other characteristic is that the particle diversity can be well improved. On the basis of the estimated positions of electromagnetic sources, the position and velocity of the virtual leader can be determined. Then, an event-based fixed-time consensus control approach is proposed such that the positions and velocities of robots reach consensus with the virtual leader over a fixed-time interval while saving resource consumption by reducing the communication frequencies and updating times of control inputs. Finally, simulation and experimental results show the effectiveness of the proposed decision and event-based fixed-time consensus control approach for ESL.

Quasisynchronization of Heterogeneous Dynamical Networks via Event-Triggered Impulsive Controls

The time-triggered impulsive control of complex homogeneous dynamical networks has received wide attention due to its occasional occupation of the communication channels. This article is devoted to quasisynchronization of heterogeneous dynamical networks via event-triggered impulsive controls with less channel occupation. Two kinds of triggered mechanisms, that is, the centralized event-triggered mechanism in which the control is updated based upon the state information of all nodes, and the distributed event-triggered mechanism where the control is updated according to the state information of each node and its neighboring node, are proposed, respectively, such that the synchronization error between the heterogeneous dynamical networks and a virtual target is not more than a nonzero bound. What is more, the Zeno behavior is shown to be excluded. It is found that the combination method of the event-triggered control and the impulsive control, that is, the distributed event-triggered impulsive control has the advantage of low-energy consumption and takes up many fewer communication channels over the time-triggered impulsive control. Two numerical examples are conducted to illustrate the effectiveness of the proposed event-triggered impulsive controls.

Leader-Following Event-Triggered Adaptive Practical Consensus of Multiple Rigid Spacecraft Systems Over Jointly Connected Networks

In this article, we study the leader-following practical attitude consensus problem of a group of multiple uncertain rigid spacecraft systems over jointly connected networks by a distributed event-triggered control law. We first establish a lemma that allows the problem to be converted to a distributed practical stabilization problem of a well-defined uncertain dynamical system. Then, we combine the adaptive distributed observer technique and the adaptive control technique to design an event-triggered adaptive control law and an event-triggered mechanism to solve our problem. The effectiveness of our design is illustrated by a numerical example.

Synchronization for stochastic coupled networks with Lévy noise via event-triggered control

This paper addresses the realization of almost sure synchronization problem for a new array of stochastic networks associated with delay and Lévy noise via event-triggered control. The coupling structure of the network is governed by a continuous-time homogeneous Markov chain. The nodes in the networks communicate with each other and update their information only at discrete-time instants so that the network workload can be minimized. Under the framework of stochastic process including Markov chain and Lévy process, and the convergence theorem of non-negative semi-martingales, we show that the Markovian coupled networks can achieve the almost sure synchronization by event-triggered control methodology. The results are further extended to the directed topology, where the coupling structure can be asymmetric. Furthermore, we also proved that the Zeno behavior can be excluded under our proposed approach, indicating that our framework is practically feasible. Numerical simulations are provided to demonstrate the effectiveness of the obtained theoretical results.

Neural-Network-Based Consensus Control for Multiagent Systems With Input Constraints: The Event-Triggered Case

In this paper, the neural-network (NN)-based consensus control problem is investigated for a class of discrete-time nonlinear multiagent systems (MASs) with a leader subject to input constraints. Relative measurements related to local tracking errors are collected via some smart sensors. A local nonquadratic cost function is first introduced to evaluate the control performance with input constraints. Then, in view of the relative measurements, an NN-based observer under the event-triggered mechanism is designed to reconstruct the dynamics of the local tracking errors, where the adopted event-triggered condition has a time-dependent threshold and the weight of NNs is updated via a new adaptive tuning law catering to the employed event-triggered mechanism. Furthermore, an ideal control policy is developed for the addressed consensus control problem while minimizing the prescribed local nonquadratic cost function. Moreover, an actor-critic NN scheme with online learning is employed to realize the obtained control policy, where the critic NN is a three-layer structure with powerful approximation capability. Through extensive mathematical analysis, the consensus condition is established for the underlying MAS, and the boundedness of the estimated errors is proven for actor and critic NN weights. In addition, the effect from the adopted event-triggered mechanism on the local cost is thoroughly discussed, and the upper bound of the corresponding increment is derived in comparison with time-triggered cases. Finally, a simulation example is utilized to illustrate the usefulness of the proposed controller design scheme.

Products of Generalized Stochastic Matrices With Applications to Consensus Analysis in Networks of Multiagents With Delays

Product theory of stochastic matrices provides a powerful tool in the consensus analysis of discrete-time multiagent systems. However, the classic theory cannot deal with networks with general coupling coefficients involving negative ones, which have been discussed only in very few papers due to the technicalities involved. Motivated by these works, here we developed some new results for the products of matrices which generalize that of the classical stochastic matrices by admitting negative entries. Particularly, we obtained a generalized version of the classic Hajnal inequality on this generalized matrix class. Based on these results, we proved some convergence results for a class of discrete-time consensus algorithms with time-varying delays and general coupling coefficients. At last, these results were applied to the analysis of a class of continuous-time consensus algorithms with discrete-time controller updates in the existence of communication/actuation delays.

Adaptive Leader-Following Consensus for Multiple Euler-Lagrange Systems With an Uncertain Leader System

In this paper, we study the leader-following consensus problem of multiple Euler-Lagrange (EL) systems subject to an uncertain leader system. We first establish an adaptive distributed observer for a neutrally stable linear leader system whose system matrix is not known exactly. Under standard assumptions, this adaptive distributed observer can estimate and pass the leader's state to each follower through the communication network of the system without knowing the leader's system matrix exactly. Under the additional assumption that the leader's state is persistently exciting, this adaptive distributed observer can also asymptotically learn the parameters of the leader's system matrix. On the basis of this adaptive distributed observer, we further synthesize an adaptive distributed control law to solve our problem via the certainty equivalence principle. Our result allows the leader-following consensus problem of multiple EL systems to be solved even if none of the followers knows the system matrix of the leader system exactly.

Event-Triggered Cooperative Global Robust Practical Output Regulation for Second-Order Uncertain Nonlinear Multiagent Systems

In this paper, we study the cooperative global robust practical output regulation problem for a class of second-order uncertain nonlinear multiagent systems via a distributed event-triggered state feedback control strategy. Compared with the existing work, one of the main challenges is that we need to design two distributed internal models to learn both the desired steady-state state and steady-state input for each agent. Moreover, to obtain a directly implementable digital control law, the two distributed internal models of each agent only depend on the sampled states of the neighboring agents and itself. As a result, the resulting augmented system is more complicated, and the control law needs to be recursively designed. To overcome the difficulty, we propose a novel distributed event-triggered control law and a novel distributed event-triggered mechanism to deal with our problem. By adjusting a design parameter in the proposed event-triggered mechanism, we show that the Zeno behavior does not happen and the ultimate bound of the tracking error can be made arbitrarily small. Our design will be illustrated by two examples.

Event-Based Communication and Finite-Time Consensus Control of Mobile Sensor Networks for Environmental Monitoring

This paper deals with the problem of environmental monitoring by designing a cooperative control scheme for mobile sensor networks. The proposed cooperative control scheme includes three main modules: a wireless communication module, a direction decision module, and a motion control module. In the wireless communication module, an event-based communication rule is proposed, which means that mobile sensor nodes do not send their positions, velocities, and the data of environmental attributes to the other sensor nodes in real-time for the coordination and control of mobile sensor networks. Due to using the event-based communication rule, the communication bandwidth can be saved. In the direction decision module, a radial basis function network is used to model the monitored environment and is updated in terms of the sampled environmental data and the environmental data from the other sensor nodes by the wireless communication module. The updated environment model is used to guide the mobile sensor network to move towards the region of interest in order to efficiently model the distribution map of environmental attributes, such as temperature, salinity, and pH values for the monitored environment. In the motion control module, a finite-time consensus control approach is proposed to enable the sensor nodes to quickly change their movement directions in light of the gradient information from the environment model. As a result of using the event-based communication rule in the wireless communication module, the proposed control approach can also lower the updating times of the control signal. In particular, the proposed cooperative control scheme is still efficient under the directed wireless communication situation. Finally, the effectiveness of the proposed cooperative control scheme is illustrated for the problem of environmental monitoring.