Search

A new algorithm to enhance the pinning controllability of complex networks of chaotic maps is presented. Leveraging the numerical observation that the existence of directed cycles in the graph hinders controllability, we develop an iterative procedure to obtain a forest structure in the graph. The effectiveness of the algorithm is illustrated with reference to the test-bed example of a network of coupled logistic maps.

In this work we attempt to create a human-like virtual player able to play the mirror game with a human player in real time. A control architecture is developed in order to drive such virtual player to lead or track a human player during the game while maintaining a certain degree of similarity to its individual signature. The virtual player is able to exhibit diverse kinematic characteristics by integrating the relevant intrinsic dynamics. Two alternative control strategies are presented here to implement its cognitive architecture, namely PD control and a receding horizon optimal control strategy. To validate and compare the performance of these control strategies, we establish a benchmark based on experimental data collected from two human players so as to evaluate the human-like performance of the virtual player when playing together with a human. Experimental validation is provided showing the advantages and disadvantages of using different control strategies and models to drive the virtual player.

The increase of non-synchronous generation sources on a power network are changing the system dynamics and this can lead to problems with stability. One of the contributions to the change in system dynamics is the variation in system inertia. By approximating a traditional stability metric, the critical clearing time (CCT) in an energetic framework we conduct a parametric investigation of the effect of inertia on power system stability. A set of solid three phase to ground faults are considered on a small but not trivial power system, the two-machine infinite bus network. The performance of the approximated CCT is compared to the true CCT.

—The so called mirror game, which in its simplestformulation involves two people mirroring each other’s hand’smovement, provides a paradigm to study social interaction.However, a customized virtual player can replace either of the twohuman participants and hopefully help with the rehabilitation ofpatients suffering from social disorders by regulating its kine-matics. In this paper we investigate the coordination movementbetween an avatar (virtual player) and a human player in thementioned game. A novel cognitive architecture is proposed todrive the motion of the virtual player so that it generates ahuman-like trajectory in two different experimental models. Inorder to achieve this objective, the Haken-Kelso-Bunz (HKB)equation is adopted to describe the social motor coordinationbetween the virtual and the human player. In addition, bothan adaptive algorithm for the coupling parameters in the HKBequation and a feedback controller are developed in order toguarantee human features for the virtual player in its kinematics.Finally, extensive experiments are conducted to validate theapproach described above.

This paper investigates the problem of event-triggered pinning control for the synchronization of networks of nonlinear dynamical agents onto a desired reference trajectory. The pinned agents are those that have access to the reference trajectory. We consider both static and switching topologies. We prove that the system is well posed and identify conditions under which the network achieves exponential convergence. A lower bound for the rate of convergence is also derived. Numerical examples demonstrating the effectiveness of the results are provided.

The coordination of interpersonal rhythmic movements is of great significance due to its potential relevance to human motor rehabilitation. In this paper we consider the problem of designing a controller able to drive a virtual player (VP) capable of imitating and following a human player in the mirror game [1]. The classic nonlinear Haken-Kelso-Bunz (HKB) model is adopted to describe the social motor coordination between two players. An adaptive control algorithm is developed and implemented on the HKB model to drive the VP. It is proven that the position error between the VP driven by our control algorithm and the human player is upper bounded during the game. Finally, experiments are conducted on a prototype set-up in order to evaluate the performance of the proposed control algorithm and compare it with other existing algorithms.