Multi‐tasking optimal control of networked control systems: A delta operator approach

In this paper, the multi‐tasking optimal control problem is addressed in the delta‐domain for a class of networked control systems (NCSs) with external disturbances. A delta‐domain model is proposed to describe the NCSs with random packet dropouts and long‐time delay, and the so‐called ε‐Nash equilibrium is employed to quantify the impacts from the disturbances on the underlying NCSs. The multi‐tasking optimal control strategies are developed and the upper bound for the ε‐Nash equilibrium is provided explicitly. Some simulation results on the two‐area load frequency control system are given to show the validity and applicability of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.     

Passivity‐based state synchronization of homogeneous multiagent systems via static protocol in the presence of input saturation

This paper studies semiglobal and global state synchronization of homogeneous multiagent systems with partial‐state coupling (ie, agents are coupled through part of their states) via a static protocol. We consider 2 classes of agents, ie, G‐passive and G‐passifiable via input feedforward, which are subjected to input saturation. The proposed static protocol is purely decentralized, ie, without an additional channel for the exchange of controller states. For semiglobal synchronization, a static protocol is designed for an a priori given set of network graphs with a directed spanning tree. In other words, the static protocol only needs rough information on the network graph, ie, a lower bound for the real part and an upper bound for the modulus, of the nonzero eigenvalues of the corresponding Laplacian matrix. Whereas for global synchronization, only strongly connected and detailed balanced network graphs are considered. In this case, for G‐passive agents, the static protocol does not need any network information, whereas for G‐passifiable agents via input feedforward, the static protocol only needs an upper bound for the modulus of the eigenvalues of the corresponding Laplacian matrix.     

Decentralized tube‐based model predictive control of uncertain nonlinear multiagent systems

This paper addresses the problem of decentralized tube‐based nonlinear model predictive control (NMPC) for a general class of uncertain nonlinear continuous‐time multiagent systems with additive and bounded disturbance. In particular, the problem of robust navigation of a multiagent system to predefined states of the workspace while using only local information is addressed under certain distance and control input constraints. We propose a decentralized feedback control protocol that consists of two terms: a nominal control input, which is computed online and is the outcome of a decentralized finite horizon optimal control problem that each agent solves at every sampling time, for its nominal system dynamics; and an additive state‐feedback law which is computed offline and guarantees that the real trajectories of each agent will belong to a hypertube centered along the nominal trajectory, for all times. The volume of the hypertube depends on the upper bound of the disturbances as well as the bounds of the derivatives of the dynamics. In addition, by introducing certain distance constraints, the proposed scheme guarantees that the initially connected agents remain connected for all times. Under standard assumptions that arise in nominal NMPC schemes, controllability assumptions, communication capabilities between the agents, it is guaranteed that the multiagent system is input‐to‐state stable with respect to the disturbances, for all initial conditions satisfying the state constraints. Simulation results verify the correctness of the proposed framework.     

Cooperative output regulation of heterogeneous multiagent systems based on event‐triggered control with fixed and switching topologies

This paper addresses the cooperative output regulation problem of multiagent systems with fixed and switching topologies. Each agent is a heterogeneous linear system, and the output of the exosystem can be available to only a subset of agents. For the agents that can directly access the exosystem, a common observer based on an event‐triggered strategy is constructed to estimate the exogenous signal for feedback control design. For the rest of the agents, estimators based on an event‐triggered mechanism to acquire the estimation value of the exogenous signal are designed under some essential assumptions. A decentralized event‐triggered formulation is considered first by applying a Lyapunov function for a fixed topology. Furthermore, a topology‐dependent triggering condition and the average dwell‐time switching law are deduced simultaneously by using multiple Lyapunov functions for switching topologies. Under communication constraints, we propose observer‐based and estimator‐based feedback controllers to solve the cooperative output regulation problem using available local information among agents. Two examples are finally provided to verify the effectiveness of the proposed theoretical results.     

MetrIntPair—A Novel Accurate Metric for the Comparison of Two Cooperative Multiagent Systems Intelligence Based on Paired Intelligence Measurements

In this paper, we propose a novel metric called MetrIntPair (Metric for Pairwise Intelligence Comparison of Agent‐Based Systems) for comparison of two cooperative multiagent systems problem‐solving intelligence. MetrIntPair is able to make an accurate comparison by taking into consideration the variability in intelligence in problem‐solving. The metric could treat the outlier intelligence indicators, intelligence measures that are statistically different from those others. For evaluation of the proposed metric, we realized a case study for two cooperative multiagent systems applied for solving a class of NP‐hard problems. The results of the case study proved that the small difference in the measured intelligence of the multiagent systems is the consequence of the variability. There is no statistical difference between the intelligence quotients/level of the multiagent systems. Both multiagent systems should be classified in the same intelligence class.     

Weighted predictor‐feedback formation control in local frames under time‐varying delays and switching topology

This article presents a novel control strategy based on predictor‐feedback delay compensation for multiagent systems to reach a prescribed target formation under unknown but bounded communication delays and switching communication topology. Both communication delays and network topology can be subjected to arbitrarily‐fast time variations. The key idea is to implement predictor‐feedback strategies using only relative measurements between agents expressed in each local agent's frame, with the aim to counteract the negative effect of time delays. Nevertheless, due to the decentralized nature of the control, the presence of time‐varying delays and switching communication topology, only partial delay compensation is possible. Despite this, we show that better performance can be achieved with our proposal with respect to nonpredictor control schemes by introducing a weighting factor for predictor‐feedback terms in the control law. Sufficient conditions based on Linear Matrix Inequalities for robust stability are also provided, which allow to easily design the controller parameters in order to maximize the speed of convergence. Finally, simulation results are provided to show the effectiveness of the proposed approach.     

Distributed integral‐type event‐triggered synchronization of multiagent systems

This paper investigates the synchronization problem of generic linear multiagent systems via integral‐type event‐triggered control. Each agent can only utilize the intermittent information of its neighboring agents in the control scheme. Based on the integral‐type event conditions, an event‐triggered control protocol is designed to guarantee the synchronization of multiagent systems, and Zeno behavior is excluded by showing the existence of a positive lower bound on the inter‐event intervals. Then, we propose the integral‐type event‐triggered control algorithms to study the leader‐following synchronization. It is shown that under the control algorithms all the followers track the leader and no Zeno behavior occurs. The effectiveness of the proposed control schemes is demonstrated by simulation examples.     

Consensus analysis of hybrid multiagent systems: A game‐theoretic approach

This paper considers a consensus problem for hybrid multiagent systems, which comprise two groups of agents: a group of continuous‐time dynamic agents and a group of discrete‐time dynamic agents. Firstly, a game‐theoretic approach is adopted to model the interactions between the two groups of agents. To achieve consensus for the considered hybrid multiagent systems, the cost functions are designed. Moreover, it is shown that the designed game admits a unique Nash equilibrium. Secondly, sufficient/necessary conditions of solving consensus are established. Thirdly, we find that the convergence speed of the system depends on the game. By the mechanism design of the game, the convergence speed is increased. Finally, simulation examples are given to validate the effectiveness of the theoretical results.     

Output synchronization for heterogeneous networks of introspective right‐invertible agents

In this paper, we consider the output synchronization problem for heterogeneous networks of right‐invertible linear agents. We assume that all the agents are introspective, meaning that they have access to their own local measurements. Under this assumption, we then propose a decentralized control scheme for solving the output synchronization problem for a set of network topologies. The proposed scheme can also be applied to solve the output formation problem with arbitrary formation vectors. We also consider the regulation of output synchronization problem, where the output of each agent has to track an a priori specified reference trajectory, generated by an exosystem. In this case, we assume that the root agent has access to its own output relative to the reference trajectory.Copyright © 2013 John Wiley & Sons, Ltd.     

Solvability conditions and design for synchronization of discrete‐time multiagent systems

This paper provides solvability conditions for state synchronization with homogeneous discrete‐time multiagent systems with a directed and weighted communication network under partial‐ or full‐state coupling. Our solvability conditions reveal that the synchronization problem is solvable for all possible, a priori given, set of graphs associated with a communication network only under the condition that the agents are at most weakly unstable (ie, agents have all eigenvalues in the closed unit disc). However, if an upper bound on the eigenvalues inside the unit disc of the row stochastic matrices associated with any graph in a given set of graphs is known, then one can achieve synchronization for a class of unstable agents. We provide protocol design for at most weakly unstable agents based on a direct eigenstructure assignment method and a standard H₂ discrete‐time algebraic Riccati equation. We also provide protocol design for strictly unstable agents (ie, agents have at least one eigenvalue outside the unit disc) based on the standard H₂ discrete‐time algebraic Riccati equation.     

H2 and H∞ almost output synchronization of heterogeneous continuous‐time multi‐agent systems with passive agents and partial‐state coupling via static protocol

This paper studies H₂ and H_∞ almost output synchronization problems for heterogeneous continuous‐time multiagent systems with passive agents and strongly connected communication graph. For non‐introspective passive agents, a linear static protocol can be designed to achieve almost output synchronization with arbitrarily small H₂ norm. Moreover, we show that the H_∞ almost output synchronization problem via static protocol is not solvable for this class of systems.     

Robust and nonfragile consensus of positive multiagent systems via observer‐based output‐feedback protocols

This article investigates the consensus problem for positive multiagent systems via an observer‐based dynamic output‐feedback protocol. The dynamics of the agents are modeled by linear positive systems and the communication topology of the agents is expressed by an undirected connected graph. For the consensus problem, the nominal case is studied under the semidefinite programming framework while the robust and nonfragile cases are investigated under the linear programming framework. It is required that the distributed state‐feedback controller and observer gains should be structured to preserve the positivity of multiagent systems. Necessary and/or sufficient conditions for the analysis of consensus are obtained by using positive systems theory and graph theory. For the nominal case, necessary and sufficient conditions for the codesign of state‐feedback controller and observer of consensus are derived in terms of matrix inequalities. Sufficient conditions for the robust and nonfragile consensus designs are derived and the codesign of state‐feedback controller and observer can be obtained in terms of solving a set of linear programs. Numerical simulations are provided to show the effectiveness and applicability of the theoretical results and algorithms.     

Decentralized Bayesian Search Using Approximate Dynamic Programming Methods

We consider decentralized Bayesian search problems that involve a team of multiple autonomous agents searching for targets on a network of search points operating under the following constraints: 1) interagent communication is limited; 2) the agents do not have the opportunity to agree in advance on how to resolve equivalent but incompatible strategies; and 3) each agent lacks the ability to control or predict with certainty the actions of the other agents. We formulate the multiagent search-path-planning problem as a decentralized optimal control problem and introduce approximate dynamic heuristics that can be implemented in a decentralized fashion. After establishing some analytical properties of the heuristics, we present computational results for a search problem involving two agents on a 5 times 5 grid.     

Finite-time coordination in multiagent systems using sliding mode control approach

Finite-time stability in dynamical systems theory involves systems whose trajectories converge to an equilibrium state in finite time. In this paper, we use the notion of finite-time stability to apply it to the problem of coordinated motion in multiagent systems. Specifically, we consider a group of agents described by fully actuated Euler–Lagrange dynamics along with a leader agent with an objective to reach and maintain a desired formation characterized by steady-state distances between the neighboring agents in finite time. We use graph theoretic notions to characterize communication topology in the network determined by the information flow directions and captured by the graph Laplacian matrix. Furthermore, using sliding mode control approach, we design decentralized control inputs for individual agents that use only data from the neighboring agents which directly communicate their state information to the current agent in order to drive the current agent to the desired steady state. Sliding mode control is known to drive the system states to the sliding surface in finite time. The key feature of our approach is in the design of non-smooth sliding surfaces such that, while on the sliding surface, the error states converge to the origin in finite time, thus ensuring finite-time coordination among the agents in the network. In addition, we discuss the case of switching communication topologies in multiagent systems. Finally, we show the efficacy of our theoretical results using an example of a multiagent system involving planar double integrator agents.     

EFFICIENT PERSONALIZATION OF E‐LEARNING ACTIVITIES USING A MULTI‐DEVICE DECENTRALIZED RECOMMENDER SYSTEM

Personalization is becoming a key issue in designing effective e‐learning systems and, in this context, a promising solution is represented by software agents. Usually, these systems provide the student with a student agent that interacts with a site agent associated with each e‐learning site. However, in presence of a large number of students and of e‐learning sites, the tasks of the agents are often onerous, even more if the student agents run on devices with limited resources. To face this problem, we propose a new multiagent learning system, called ISABEL. Our system provides each student, that are using a specific device, with a device agent able to autonomously monitor the student's behavior when accessing e‐learning Web sites. Each site is associated, in its turn, with a teacher agent. When a student visits an e‐learning site, the teacher agent collaborates with some tutor agents associated with the student, to provide him with useful recommendations. We present both theoretical and experimental results to show that this distributed approach introduces significant advantages in quality and efficiency of the recommendation activity with respect to the performances of other past recommenders.     

Decentralized adaptive consensus control for discrete‐time heterogeneous semiparametric multiagent systems

Different from the consensus control of traditional multiagent systems, this paper studies the decentralized adaptive consensus control for discrete‐time heterogeneous hidden leader‐following semiparametric multiagent system, in which the dynamic equation of each agent has both parametric uncertainties and nonparametric uncertainties. In the considered system, there is a hidden leader agent who can receive the reference signal, but it can only affect the states of those agents who are in its neighborhood. For other following agents, they do not know the leader's existence or the reference signal, and they can only receive information from their neighbors. Our goal is to design decentralized adaptive controllers to make sure that all agents can track the reference signal, and the closed‐loop system achieves consensus in the presence of mutual coupling relations. Due to the existence of both parametric and nonparametric uncertainties in the system, we need to estimate them separately. For the parametric part, we propose a novel dead zone with threshold converging to zero to modify the traditional gradient update law, while for the nonparametric part, we introduce an auxiliary variable including both two uncertainties to facilitate the nonparametric uncertainties compensation. Based on the certainty equivalence principle in adaptive control theory, the decentralized adaptive controller is designed for each agent to make sure that all of them can track the reference signal. Finally, under the proposed control protocol, strict mathematical proofs are given by using Lyapunov theory; then, simulation results are provided to demonstrate the effectiveness of proposed decentralized adaptive controllers.     

ε-Equilibrium in LQ differential games with bounded uncertain disturbances: robustness of standard strategies and new strategies with adaptation

A finite time multi-persons linear-quadratic differential game (LQDG) with bounded disturbances and uncertainties is considered. When players cannot measure these disturbances and uncertainties, the standard feedback Nash strategies are shown to yield to an ε-(or quasi) Nash equilibrium depending on an uncertainty upper bound that confirms the robustness property of such standard strategies. In the case of periodic disturbances, another concept, namely adaptive concept, is suggested. It is defined an “adaptation period” where all participants apply the standard feedback Nash strategies with the, so-called, “shifting signal” generated only by a known external exciting signal. Then, during the adaptation, the readjustment (or correction) of the control strategies is realized to estimate the effect of unknown periodic disturbances by the corresponding correction of the shifting vector. After that adaptation period, the complete standard strategies including the recalculated shifting signal are activated allowing the achievement of pure (ε?=?0) Nash equilibrium for the rest of the game. A numerical example dealing with a two participants game shows that the cost functional for each player achieves better values when the adaptive approach is applied.     

Adaptive bipartite consensus control of high‐order multiagent systems on coopetition networks

In this paper, a bipartite consensus problem is considered for a high‐order multiagent system with cooperative‐competitive interactions and unknown time‐varying disturbances. A signed graph is used to describe the interaction network associated with the multiagent system. The unknown disturbances are expressed by linearly parameterized models, and distributed adaptive laws are designed to estimate the unknown parameters in the models. For the case that there is no exogenous reference system, a fully distributed adaptive control law is proposed to ensure that all the agents reach a bipartite consensus. For the other case that there exists an exogenous reference system, another fully distributed adaptive control law is also developed to ensure that all the agents achieve bipartite consensus on the state of the exogenous system. The stability of the closed‐loop multiagent systems with the 2 proposed adaptive control laws are analyzed under an assumption that the interaction network is structurally balanced. Moreover, the convergence of the parameter estimation errors is guaranteed with a persistent excitation condition. Finally, simulation examples are provided to demonstrate the effectiveness of the proposed adaptive bipartite consensus control laws for the concerned multiagent system.     

Observer‐based event‐triggered control for singularly perturbed systems with saturating actuator

This paper investigates an observer‐based event‐triggered control problem of singularly perturbed systems with saturating actuator. A strategy that consists of an observer‐based controller (OBC) and an event‐triggered mechanism (ETM) is considered. Firstly, sufficient conditions, which guarantee that the saturated SPSs are asymptotically stable excluding Zeno phenomenon, are derived via constructing an ε‐dependent Lyapunov‐Krasovskii functional. Then, the OBC and ETM are designed simultaneously based on the aforementioned criteria. Furthermore, an estimate of the basin of attraction and an ε‐bound are given by solving an optimization problem in the form of LMIs. Finally, an electric circuit system and a numerical example are presented to demonstrate the merits of the obtained method.     

A LATTICE‐BASED APPROACH TO THE PROBLEM OF RECRUITMENT IN MULTIAGENT SYSTEMS

Multiagent systems constitute an independent topic at the intersection between distributed computing and artificial intelligence. As the algorithmic techniques and the applications for multiagent systems have been continuously developing over the last two decades reaching significantly mature stages, many methodological problems have been addressed. In this paper, we aim to contribute to this methodological assessment of multiagent systems by considering the problem of choosing, or recruiting, a subset of agents from a set of available agents to satisfy a given request. This problem, which we call problem of recruitment, is encountered, for example, in matchmaking and in task allocation. We present and study a novel formal approach to the problem of recruitment, based on the algebraic formalism of lattices. The resulting formal framework can support the development of algorithms for automatic recruitment.