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This paper studies the consensus of multi-agent systems with faults and mismatches under switched topologies using a delta operator method. Since faults and mismatches can result in failure of the consensus even for a fixed topology with a spanning tree, how to reach a consensus is a complicated and challenging problem under such circumstances especially when part topologies have no spanning tree. Although some works studied the influence of faults and mismatches on the consensus, there is little work on reaching a consensus for the multi-agent systems with faults and mismatches. In this paper, we introduce the delta operator to unify the consensus analysis for continuous, discrete, or sampled systems under one framework. We develop the theories on the delta operator systems first and then apply theories of the delta operator systems to the consensus problems. By converting the consensus problems into stability problems, we investigate and prove consensus and the associated conditions for systems 1) without any fault, 2) with a known fault, and 3) with unknown faults, under switching topologies with matching or mismatching coefficients. Numerical examples are provided and validate the effectiveness of the theoretical results.

[EN] This paper presents an approach of a social emotional model, which allows to extract the social emotion of a group of intelligent entities. The emotional model PAD allows to represent the emotion of an intelligent entity in 3-D space, allowing the representation of different emotional states. The social emotional model presented in this paper uses individual emotions of each one of the entities, which are represented in the emotional space PAD. Using a social emotional model within intelligent entities allows the creation of more real simulations, in which emotional states can influence decision-making. The result of this social emotional mode is represented by a series of examples, which are intended to represent a number of situations in which the emotions of each individual modify the emotion of the group. Moreover, the paper introduces an example which employs the proposed model in order to learn and predict future actions trying to influence in the social emotion of a group of people., This work is partially supported by the MINECO/FEDER TIN2015-65515-C4-1-R and the FPI grant AP2013-01276 awarded to Jaime-Andres Rincon.

[EN] The electric vehicle (EV) is gradually being introduced in cities. The impact of this introduction is less due, among other reasons, to the lack of charging infrastructure necessary to satisfy the demand. In today¿s cities there is no adequate infrastructure and it is necessary to have action plans that allow an easy deployment of a network of EV charging points in current cities. These action plans should try to place the EV charging stations in the most appropriate places for optimizing their use. According to this, this paper presents an agent-oriented approach that analyses the different configurations of possible locations of charging stations for the electric vehicles in a specific city. The proposed multi-agent system takes into account data from a variety of sources such as social networks activity and mobility information in order to estimate the best configurations. The proposed approach employs a genetic algorithm (GA) that tries to optimize the possible configurations of the charging infrastructure. Additionally, a new crossover method for the GA is proposed considering this context., This work was partially supported by MINECO/FEDER RTI2018-095390-B-C31 and MODINVECI project of the Spanish government. Vicent Botti and Jaume Jordan are funded by UPV PAID-06-18 project. Jaume Jordan is funded by grant APOSTD/2018/010 of GVA-FSE

In this paper, the consensus problem is investigated for a class of nonlinear multi-agent systems in lower triangular form through dynamic output feedback control. Each agent can only access to the uncertain relative output information from other agents under directed communication. The presence of unknown measurement sensitivities causes the relative output can not be used to construct the observer for each agent directly. For the sake of overcoming this difficulty, a dual-domination gain method is applied to dominate the nonlinear terms and the unknown measurement sensitivities. Then, a new dynamic output feedback consensus prtocol is presented to drive the multi-agent systems to consensus. The upper bound of the unknown measurements sensitivities is also given. At last, we provide two numerical simulation examples to verify the effectiveness of our proposed methods.

This paper addresses the problem of distributed leader-following consensus for a multi-agent system with an affine nonlinear term. The communication topology we adopt is an undirected connected graph and the leader sends its information to one or more followers. To make each follower asymptotically synchronize with the leader, a nonlinear distributed control protocol is proposed. Using a Lyapunov function and a matrix theory, we establish sufficient conditions which ensure the consensus of these nonlinear multi-agent systems. Finally, a numerical simulation is provided to verify the effectiveness and usefulness of the developed method.

In this paper, the leader-following consensus problem is investigated for upper-triangular nonlinear MASs (multi-agent systems) via sampled and delayed output feedback. The sampled outputs transmitted through the communication channels are used to constructed the output feedback controllers directly. Then, the output feedback control signals transmitted through the other communication channels are used to update the MASs. Two kinds of transmission delays, i.e., transmission delays from the MASs to the output feedback controllers and transmission delays from the output feedback controllers to the MASs, make the MASs and the output feedback controllers to be updated at different time instants. A method of interval decomposition is proposed such that the MASs and the output feedback controllers are updated at the same interval. Then, sufficient conditions are presented to ensure the MASs reach leader-following consensus. At last, an example is provided to verify efficiency of the proposed methods.

In this paper, the leaderless consensus problem of multi-agent systems with jointly connected topologies and nonlinear dynamics is considered, in which the nonlinear dynamics are assumed to be non-identical and unknown. The unknown nonlinear dynamics existing in the systems are assumed to be linearly parameterized, and an adaptive design method for leaderless multi-agent systems is presented. By just using the relative position information between each agent and its neighbors, a distributed adaptive consensus control algorithm for the considered systems is proposed, in which the network graphs are jointly connected. Both the global uniform asymptotical stability and the global uniform asymptotical parameter convergence analysis of the adaptive control algorithm are carried out by using adaptive control theory, Lyapunov theory and algebraic graph theory. Finally, an example is given to illustrate the validity of our theoretical results.

In this paper, finite-time adaptive consensus problem is investigated for first-order multiagent systems with unknown nonlinear dynamics. Linearly parameterized method is introduced to model unknown nonlinear dynamics of the systems. By only utilizing the local relative position state information between each agent and its neighbors, decentralized finite-time adaptive consensus algorithms are presented with directed fixed and switching network topologies which satisfy detailed balance condition. Based on classical Lyapunov analysis techniques, both finite-time stability and finite-time parameter convergence are guaranteed by making use of the proposed control algorithms. Finally, the results in Simulations part are presented to validate our main results.

Time-varying group formation control problems for second-order multi-agent systems with directed topologies are investigated. Firstly, a time-varying group formation control protocol is constructed using local relative positions and velocities of each agent and its neighbors. Then based on graph theory, nonsingular transformations are applied to the closed-loop multi-agent systems. Sufficient conditions for second-order multi-agent systems to achieve time-varying group formation are further presented together with the time-varying group formation feasibility constraints. Explicit expressions of the subgroup formation reference functions are derived to describe the macroscopic movement of the time-varying subgroup formations. Moreover, by solving an algebraic Riccati equation, an approach to design the time-varying group formation protocol is proposed. Finally, a numerical example with three subgroups is provided to demonstrate the effectiveness of the obtained results. In contrast to the traditional complete formation, where only one formation is realized by the multi-agent system, in the group formation discussed in the current paper, agents are classified into subgroups and each subgroup is required to form a specified time-varying sub-formation via inter-subgroup and intra-subgroup interactions.

This paper investigates the consensus of second-order multi-agent systems without measuring the velocity states of the agents, where each agent can be either a double integrator or a harmonic oscillator. By utilizing the position information of the agents, a distributed observer-based protocol is proposed to solve the second-order consensus problem of multi-agent systems with or without time delay. The observer-based consensus problem is converted to the stability analysis for a set of second-order quasi-polynomials through coordinate transform and system decomposition. Then, some necessary and sufficient conditions are derived for reaching second-order consensus in multi-agent systems with or without time delay, respectively, and it is shown that the consensus can be achieved if and only if the communication delay is less than a critical value. Simulation examples are given to verify the theoretical analysis.

A novel hybrid artificial intelligent system for intrusion detection, called MObile-VIsualization Hybrid IDS (MOVIH-IDS), is presented in this study. A hybrid model built by means of a multiagent system that incorporates an unsupervised connectionist intrusion detection system (IDS) has been defined to guaranty an efficient computer network security architecture. This hybrid IDS facilitates the intrusion detection in dynamic networks, in a more flexible and adaptable manner. The proposed improvement of the system in this paper includes deliberative agents characterized by the use of an unsupervised connectionist model to identify intrusions in computer networks. This hybrid IDS has been probed through several real anomalous situations related to the simple network management protocol as it is potentially dangerous. Experimental results probed the successful detection of such attacks through MOVIH-IDS.