Finite-Time and Fixed-Time Synchronization of Inertial Neural Networks with Mixed Delays

This paper considers the drive-response synchronization in finite-time and fixed-time of inertial neural networks with time-varying and distributed delays(mixed delays). First, by constructing a proper variable substitution, the original inertial neural networks can be rewritten as a first-order differential system. Second, by constructing Lyapunov functions and using differential inequalities,some new and effective criteria are obtained for ensuring the finite-time synchronization. Finally, three numerical examples are also given at the end of this paper to show the effectiveness of the results.     

OPTIMAL TARGET TRAJECTORY ESTIMATION AND FILTERING USING NETWORKED SENSORS

Target tracking using distributed sensor network is in general a challenging problem because it always needs to deal with real-time processing of noisy information. In this paper the problem of using nonlinear sensors such as distance and direction sensors for estimating a moving target is studied. The problem is formulated as a prudent design of nonlinear filters for a linear system subject to noisy nonlinear measurements and partially unknown input, which is generated by an exogenous system. In the worst case where the input is completely unknown, the exogenous dynamics is reduced to the random walk model. It can be shown that the nonlinear filter will have optimal convergence if the number of the sensors are large enough and the convergence rate will be highly improved if the sensors are deployed appropriately. This actually raises an interesting issue on active sensing： how to optimally move the sensors if they are considered as mobile multi-agent systems？ Finally, a simulation example is given to illustrate and validate the construction of our filter.     

Fixed-Time Anti-Disturbance Average-Tracking for Multi-Agent Systems Without Velocity Measurements

In this paper, the authors tackle a fixed-time average-tracking problem for multi-agent systems which do not require velocity measurements and are affected by external mismatched and matched disturbances. The objective requires the considered agents with second-order dynamics to follow the average value of several time-varying reference signals, each of which is acquired by only one agent. First of all, the average value is estimated by using a novel distributed average estimator in a fixed time...     

Leader-following consensus protocols for formation control of multi-agent network

Two protocols are presented,which can make agents reach consensus while achieving and preserving the desired formation in fixed topology with and without communication timedelay for multi-agent network.First,the protocol without considering the communication time-delay is presented,and by using Lyapunov stability theory,the sufficient condition of stability for this multi-agent system is presented.Further,considering the communication time-delay,the effectiveness of the protocol based on Lyapunov-Krasovskii function is demonstrated.The main contribution of the proposed protocols is that,as well as the velocity consensus is considered,the formation control is concerned for multi-agent systems described as the second-order equations.Finally,numerical examples are presented to illustrate the effectiveness of the proposed protocols.     

Event-triggered average-consensus of multi-agent systems with weighted and direct topology

This paper investigates the average-consensus problem of multi-agent systems with direct and weighted topologies.Event-triggered control laws are adopted so as to reduce the frequency of individual control updating since the agents may be resource-limited in many real systems.The discrete time instants where the events are triggered are determined by a trigger function with respect to a certain measurement error.A centralized average-consensus protocol is proposed first for networks with fixed interaction topology,the stability and influencing factors of which are also analyzed.The design of trigger functions for networks with variable topology is also discussed.Then the results are extended to the decentralized counterpart,in which agents require only the information of their neighbors.Numerical examples are also provided that demonstrate the effectiveness of the theoretical results.     

Finite-Time Observability of Probabilistic Logical Control Systems

This study investigates finite-time observability of probabilistic logical control systems(PLCSs) under three definitions(i.e., finite-time observability with probability one, finite-time singleinput sequence observability with probability one, and finite-time arbitrary-input observability with probability one). The authors adopt a parallel extension technique to recast the finite-time observability problem of a PLCS as a finite-time set reachability problem. Then, the finite-time set reachabili...     

High-order sliding mode attitude controller design for reentry flight

A novel high-order sliding mode control strategy is proposed for the attitude control problem of reentry vehicles in the presence of parametric uncertainties and external disturbances, which results in the robust and accurate tracking of the aerodynamic angle commands with the finite time convergence. The proposed control strategy is developed on the basis of integral sliding mode philosophy, which combines conventional sliding mode control and a linear quadratic regulator over a finite time interval with a free-final-state and allows the finite-time establishment of a high-order sliding mode. Firstly, a second-order sliding mode attitude controller is designed in the proposed high-order siding mode control framework. Then, to address the control chattering problem, a virtual control is introduced in the control design and hence a third-order sliding mode attitude controller is developed, leading to the chattering reduction as well as the control accuracy improvement. Finally, simulation examples are given to illustrate the effectiveness of the theoretical results.     

Finite-time tracking control for motor servo systems with unknown dead-zones

A finite-time tracking control scheme is proposed in this paper based on the terminal slid- ing mode principle for motor servo systems with unknown nonlinear dead-zone inputs. By using the differential mean value theorem, the dead-zone is represented as a time-varying system and thus the inverse compensation approach is avoided. Then, an indirect terminal sliding mode control （ITSMC） is developed to guarantee the finite-time convergence of the tracking error and to overcome the singu- larity problem in the traditional terminal sliding mode control. In the proposed controller design, the unknown nonlinearity of the system is approximated by a simple sigmoid neural network, and the ap- proximation error is diminished by employing a robust term. Comparative experiments on a turntable servo system are conducted to show the superior performance of the proposed method.     

CONVERGENCE OF A CLASS OF MULTI-AGENT SYSTEMS IN PROBABILISTIC FRAMEWORK

Multi-agent systems arise from diverse fields in natural and artificial systems, and a basic problem is to understand how locally interacting agents lead to collective behaviors （e.g., synchronization） of the overall system. In this paper, we will consider a basic class of multi-agent systems that are described by a simplification of the well-known Vicsek model. This model looks simple, but the rigorous theoretical analysis is quite complicated, because there are strong nonlinear interactions among the agents in the model. In fact, most of the existing results on synchronization need to impose a certain connectivity condition on the global behaviors of the agents＇ trajectories （or on the closed-loop dynamic neighborhood graphs）, which are quite hard to verify in general. In this paper, by introducing a probabilistic framework to this problem, we will provide a complete and rigorous proof for the fact that the overall multi-agent system will synchronize with large probability as long as the number of agents is large enough. The proof is based on a detailed analysis of both the dynamical properties of the nonlinear system evolution and the asymptotic properties of the spectrum of random geometric graphs.     

Expert discovery and knowledge mining in complex multi-agent systems

Complex problem solving requires diverse expertise and multiple techniques. In order to solve such problems, complex multi-agent systems that include both of human experts and autonomous agents are required in many application domains. Most complex multi-agent systems work in open domains and include various heterogeneous agents. Due to the heterogeneity of agents and dynamic features of working environments, expertise and capabilities of agents might not be well estimated and presented in these systems. Therefore, how to discover useful knowledge from human and autonomous experts, make more accurate estimation for experts＇ capabilities and find out suitable expert（s） to solve incoming problems （＂Expert Mining＂） are important research issues in the area of multi-agent system. In this paper, we introduce an ontology-based approach for knowledge and expert mining in hybrid multi-agent systems. In this research, ontologies are hired to describe knowledge of the system. Knowledge and expert mining processes are executed as the system handles incoming problems. In this approach, we embed more self-learning and self-adjusting abilities in multi-agent systems, so as to help in discovering knowledge of heterogeneous experts of multi-agent systems.     

Open-loop and closed-loop Dα-type iterative learning control for fractional-order linear multi-agent systems with state-delays

This study focuses on implementing consensus tracking using both open-loop and closed-loop Dα-type iterative learning control(ILC)schemes,for fractional-order multi-agent systems(FOMASs)with state-delays.The desired trajectory is constructed by introducing a virtual leader,and the fixed communication topology is considered and only a subset of followers can access the desired trajectory.For each control scheme,one controller is designed for one agent individually.According to the tracking error between the agent and the virtual leader,and the tracking errors between the agent and neighboring agents during the last iteration(for open-loop scheme)or the current running(for closed-loop scheme),each controller continuously corrects the last control law by a combination of communication weights in the topology to obtain the ideal control law.Through the rigorous analysis,sufficient conditions for both control schemes are established to ensure that all agents can achieve the asymptotically consistent output along the iteration axis within a finite-time interval.Sufficient numerical simulation results demonstrate the effectiveness of the control schemes,and provide some meaningful comparison results.     

On control of strong consensus for networked agents with noisy observations

The discrete-time first-order multi-agent networks with communication noises are under consideration.Based on the noisy observations,the consensus control is given for networks with both fixed and time-varying topologies.The states of agents in the resulting closed-loop network are updated by a stochastic approximation(SA) algorithm,and the consensus analysis for networks turns to be the convergence analysis for SA.For networks with fixed topologies,the proposed consensus control leads to consensus of agents with probability one if the graph associated with the network is connected.In the case of time-varying topologies,the similar results are derived if the graph is jointly connected in a fixed time period.Compared with existing results,the networks considered here are in a more general setting under weaker assumptions and the strong consensus is established by a simpler proof.     

Cooperative shift estimation of target trajectory using clustered sensors

In this paper, a mathematical model for target tracking using nonlinear scalar range sensors is formulated first. A time-shift sensor scheduling strategy is addressed on the basis of a k-barrier coverage protocol and all the sensors are divided into two classes of clusters, active cluster, and submissive cluster, for energy-saving. Then two types of time-shift nonlinear filters are proposed for both active and submissive clusters to estimate the trajectory of the moving target with disturbed dynamics. The stochastic stability of the two filters is analyzed. Finally, some numerical simulations are given to demonstrate the effectiveness of the new filters with a comparison of EKF.     

Role of different system delays on ecological food chain dynamics: Mathematical modelling and analysis

<正> This paper studies a three tier ecological food chain model consisting of nutrient,autotroph,and herbivore populations.Regeneration of nutrient from dead autotroph and herbivore biomass bydecomposers present in the soil is included.The time required for maturation of the herbivore populationis incorporated as a distributed time delay.Next,the authors introduce the time lag requiredfor regeneration of nutrient from the dead herbivore as a discrete time delay.Stability and bifurcationbehavior of the one-and two-delay models are carried out and a comparative study of the significanceof these delays in controlling the system dynamics is performed.Numerical simulations are done tojustify analytical results.     

SOFT CONTROL ON COLLECTIVE BEHAVIOR OF A GROUP OF AUTONOMOUS AGENTS BY A SHILL AGENT

This paper asks a new question： how can we control the collective behavior of self-organized multi-agent systems？ We try to answer the question by proposing a new notion called ＇Soft Control＇ which keeps the local rule of the existing agents in the system. We show the feasibility of soft control by a case study. Consider the simple but typical distributed multi-agent model proposed by Vicsek et al. for flocking of birds： each agent moves with the same speed but with different headings which are updated using a local rule based on the average of its own heading and the headings of its neighbors. Most studies of this model are about the self-organized collective behavior, such as synchronization of headings. We want to intervene in the collective behavior （headings） of the group by soft control. A specified method is to add a special agent, called a ＇Shill＇, which can be controlled by us but is treated as an ordinary agent by other agents. We construct a control law for the shill so that it can synchronize the whole group to an objective heading. This control law is proved to be effective analytically and numerieally. Note that soft control is different from the approach of distributed control. It is a natural way to intervene in the distributed systems. It may bring out many interesting issues and challenges on the control of complex systems.     

IDENTIFICATION ERROR BOUNDS AND ASYMPTOTIC DISTRIBUTIONS FOR SYSTEMS WITH STRUCTURAL UNCERTAINTIES

This work is concerned with identification of systems that are subject to not only measure-ment noises.but also structural uncertainties such as unmodeled dynamics,sensor nonlinear mismatch,and observation bias.Identification errors are analyzed for their dependence on these structural uncer-tainties.Asymptotic distributions of scaled sequences of estimation errors are derived.     

Fault Detection for Complex Systems with Channel Fadings,Randomly Occurring Multiple Delays and Infinitely Distributed Delays

This paper is concerned with the fault detection(FD) problem for a class of discretetime stochastic systems with channel fadings, randomly occurring multiple communication delays,and infinitely distributed delays. All of the three phenomena have the characteristics of randomly occurring and three sequences of stochastic variables which are mutually independent but obey the Bernoulli distribution are employed to describe them. The aim of this paper is to design an FD filter such that the FD dynamics is exponentially stable in the mean square and, at the same time, the error between the residual signal and the fault signal is made as small as possible. Intensive analysis is utilized to derive the sufficient conditions for the designed FD filter, which guarantees the exponential stability and the prescribed H∞ performance. FD filter parameters are obtained by solving a convex optimization problem. An illustrative example is provided to demonstrate the effectiveness of the FD design scheme.     

Integrating complex system dynamics of pandemic influenza with a multi-criteria decision making model for evaluating public health strategies

Recent developments in computational sciences and computer modeling have allowed emergency preparedness exercises to include simulation models as supporting tools.These simulation models are generally built for predicting temporal and geographic patterns of disease spread.However sole use of simulation models in exercise design falls short in terms of incorporating policy decision makers’ preferences into decision-making processes.In this paper,a general framework for exercising public health preparedness plans with a decision support system is presented to integrate estimation of key epidemiological parameters with a system dynamics model of an outbreak.A multi-criteria decision making framework,an Analytical Hierarchy Process model,is then developed and integrated with the simulation model to help public health policy makers prioritize their response goals and evaluate mitigation strategies in a table-top exercise environment.     

A message-driving formalism for modeling and simulation of multi-agent supply chain systems

To acquire a competitive advantage in the expanding market, manufacturing enterprises should be able to manage their supply chains as effectively as possible. It is now becoming popular to model supply chains as multi-agent systems and use discrete event simulation to learn more about their behaviors or investigate the implications of alternative configurations. In order to enhance the computational efficiency and keep the simulation credibility, this paper proposes a message-driving formalism for the simulation of multi-agent supply chain systems. Through the message-driving formalism, the problem of shared variables is addressed and the parallel operation of agents is implemented. Simulation experiments with a prototype implementation show that the message-driving formalism is able to provide credible results in significantly less simulation time.