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Based on a coupled electric circuit-magnetic field model on the basis of 2D-finite element method (FEM), the influences of modeling methods and winding types on AC calculation of a 12-slot/10-pole 54.7kW/10krpm flux-switching permanent magnet (FSPM) machine is investigated. Firstly, the influences of voltage/current-source, steady/rotating rotor between phase windings are evaluated by different modeling methods. Then, the differences of AC loss between two types of winding wires with varying frequencies are analyzed, where round and flat copper wire gauges are evaluated. Finally, an experimental modular device is built to measure AC copper loss and the measured results are consistent with the predictions.

To improve anti-disturbance performance of a five-phase flux-switching permanent magnet (FSPM) motor drive, a robust deadbeat model predictive current control (RDB-MPCC) method with disturbance observer is proposed in this paper. First, parameter sensitivity of the traditional deadbeat model predictive current control (DB-MPCC) method and the influence of parameter mismatches on system performance are theoretically analyzed. Secondly, the observer system is designed based on the simplified gain matrix. A specific and practical observer gain design scheme is proposed, which can realize accurate observation of disturbances. The proposed RDB-MPCC method can eliminate static current error caused by parameter deviations as well as improve the stability of the system. Finally, a five-phase FSPM prototype motor is used to verify the effectiveness of the proposed RDB-MPCC method.

In this paper, an asynchronous discrete-time opinion dynamics model on a social influence network is considered. At each time instant, a single individual activates and updates two state variables simultaneously. The individual’s new private opinion is a weighted average of her current private opinion, the expressed opinions of her neighbors, and a constant prejudice. Meanwhile, the individual’s new expressed opinion is equal to her current private opinion, altered due to a pressure to conform to the public opinion as perceived by the individual, being the average expressed opinion among her neighbors. We analyze the system for social networks which are rooted, and show that if no individual holds a prejudice, then a mild assumption on the activation sequence of the individuals guarantees convergence. In particular, the expressed and private opinions of all individuals converge to the same value exponentially fast, with two lower bounds on convergence speeds based on two different assumptions on the network topology. Simulations are provided to illustrate the result, and provide support to the conjecture that the system dynamics may converge even if individuals hold an existing prejudice.

This paper studies the evolution of an expressed and private opinion dynamics model with asynchronous updating. It is shown that under some mild connectivity and individual activation conditions, the expressed and private opinions of all individuals in the network converge to a common value exponentially fast when the susceptibility to influence of all individuals is equal to one. A numerical example verifies the theoretical result.

This paper studies the resilient consensus problem with time-varying adversary attacks and asynchronous events. In the synchronous or asynchronous update case, all the nodes or only a fraction of nodes update their states at each time step, respectively. For the synchronous update case, the nodes under attack are time-varying but do not change after some finite time; for the asynchronous update case, the nodes under attack are time-invariant. For both cases, a resilient consensus can be reached exponentially fast under a network topology condition inspired by the notion of the scrambling matrix and an explicit expression for the convergence rate is provided. Numerical examples are given to validate the theoretical results and to compare the performance with an existing algorithm.

We study the general formation problem for a group of mobile agents in a plane, in which the agents are required to maintain a distribution pattern, as well as to rotate around or remain static relative to a static/moving target. The prescribed distribution pattern is a class of general formations that the distances between neighboring agents or the distances from each agent to the target do not need to be equal. Each agent is modeled as a double integrator and can merely perceive the relative information of the target and its neighbors. A distributed control law is designed using the limit-cycle based idea to solve the problem. One merit of the controller is that it can be implemented by each agent in its Frenet-Serret frame so that only local information is utilized without knowing global information. Theoretical analysis is provided of the equilibrium of the N -agent system and of the convergence of its converging part. Numerical simulations are given to show the effectiveness and performance of the proposed controller.

This paper studies the stability of equilibria of a nonlinear opinion dynamics model proposed in [1] for biased assimilation, which generalizes the DeGroot model by introducing a bias parameter. When the bias parameter is zero, the model reduces to the original DeGroot model. A positive value of this parameter reflects the degree of how biased an agent is. The opinions of the agents lie between 0 and 1. When the bias parameter is positive, it is shown that the equilibria with all elements equal identically to the extreme value 0 or 1 is locally stable, while the equilibrium with all elements equal to the intermediate consensus value $\frac{1}{2}$ is unstable. For the equilibrium consisting of both extreme values 0 and 1, which corresponds to opinion polarization according to the model, it is shown that the equilibrium is locally stable if the bias parameter is greater than one for two-island networks, becomes unstable if the bias parameter is less than one, and its stability heavily depends on the network topology when the parameter equals one, in which case the limiting behavior of the model is established for certain initial conditions. It is also shown that for a small negative bias parameter, with which the agents can be regarded as anti-biased, the equilibrium with all elements equal to $\frac{1}{2}$ is locally stable.

We study the consensus problem of discrete-time systems under persistent flow and non-reciprocal interactions between agents. An arc describing the interaction strength between two agents is said to be persistent if its weight function has an infinite l 1 norm. We discuss two balance conditions on the interactions between agents which generalize the arc-balance and cut-balance conditions in the literature respectively. The proposed conditions require that such a balance should be satisfied over each time window of a fixed length instead of at each time instant. We prove that in both cases global consensus is reached if and only if the persistent graph, which consists of all the persistent arcs, contains a directed spanning tree. The convergence rates are also provided in terms of the number of node interactions that have taken place.

This paper investigates the asymptotic stability of a class of switched linear time-invariant discrete-time systems. Without the restriction of switching signals, a novel result is first proposed to guarantee uniform global asymptotic stability. Then, it is applied to studying some consensus problems of linear multi-agent systems under switching network topology. The proposed result modifies some insufficient condition assumed in a related paper and establishes the exponentially fast convergence rate of the multi-agent system. An interesting example illustrates the effectiveness of the derived results.

In a recent paper [1], a modified DeGroot-Friedkin model was proposed to study the evolution of the social-confidence levels of individuals in a reflected appraisal mechanism in which a network of n individuals consecutively discuss a sequence of issues. The individuals update their self-confidence levels on one issue in finite time steps, via communicating with their neighbors, instead of waiting until the discussion on the previous issue reaches a consensus, while the neighbor relationships are described by a static relative interaction matrix. This paper studies the same modified DeGroot-Friedkin model, but with time-varying interactions which are characterized by a sequence of doubly stochastic matrices. It is shown that, under appropriate assumptions, the n individuals' self-confidence levels will all converge to 1/n exponentially fast. An explicit expression of the convergence rate is provided.

In this paper we deal with robust synchronizationproblems for directed Lur’e networks subject to incrementallypassive nonlinearities and incrementally sector bounded nonlinearities,respectively. By making use of general algebraicconnectivities of strongly connected graphs and subgraphs,sufficient synchronization conditions are obtained for diffusivelyinterconnected identical Lur’e systems on both the stronglyconnected interconnection topology and the topology containinga directed spanning tree. The static feedback gain matricesare determined by the matrices defining the individual agentdynamics and the general algebraic connectivities. The synchronizationcriteria obtained in the present paper extend those forundirected Lur’e networks in our previous work.

This paper discloses the similarities between the condition for realizing cluster synchronization and that for uncontrollability in diffusively coupled multi-agent networks, both of which are built upon the characteristics of the networks' topologies. We first generalize the notions of equitable partitions and almost equitable partitions to make them applicable to directed, weighted graphs. Consequently, we are enabled to characterize the controllable subspace of a given diffusively coupled multi-agent system using graph theoretic ideas. After comparing the condition to realize cluster synchronization and the condition for the network to be controllable, we conclude that those diffusively coupled multi-agent networks that are not controllable usually realize cluster synchronization asymptotically. Simulation results are provided to illustrate the theoretical results.

This paper presents two approaches to achieving cluster synchronization in dynamical multi-agent systems. In contrast to the widely studied synchronization behavior, where all the coupled agents converge to the same value asymptotically, in the cluster synchronization problem studied in this paper, we require that all the interconnected agents to evolve into several clusters and each agent only to synchronize within its cluster. The first approach is to add a constant forcing to the dynamics of each agent that are determined by positive diffusive couplings; and the other is to introduce both positive and negative couplings between the agents. Some sufficient and/or necessary conditions are constructed to guarantee n-cluster synchronization behavior. Simulation results are presented to illustrate the effectiveness of the theoretical analysis.