Similarity Decomposition Approach to Oscillatory Synchronization for Multiple Mechanical Systems With a Virtual Leader

This paper addresses the oscillatory synchronization problem for multiple uncertain mechanical systems with a virtual leader, and the interaction topology among them is assumed to contain a directed spanning tree. We propose an adaptive control scheme to achieve the goal of oscillatory synchronization. Using the similarity decomposition approach, we show that the position and velocity synchronization errors between each mechanical system (or follower) and the virtual leader converge to zero. The performance of the proposed adaptive scheme is shown by numerical simulation results.Comment: 15 pages, 3 figures, published in 2014 Chinese Control Conferenc

Collective frequency variation in network synchronization and reverse PageRank

A wide range of natural and engineered phenomena rely on large networks of interacting units to reach a dynamical consensus state where the system collectively operates. Here we study the dynamics of self-organizing systems and show that for generic directed networks the collective frequency of the ensemble is {\it not} the same as the mean of the individuals' natural frequencies. Specifically, we show that the collective frequency equals a weighted average of the natural frequencies, where the weights are given by an out-flow centrality measure that is equivalent to a reverse PageRank centrality. Our findings uncover an intricate dependence of the collective frequency on both the structural directedness and dynamical heterogeneity of the network, and also reveal an unexplored connection between synchronization and PageRank, which opens the possibility of applying PageRank optimization to synchronization. Finally, we demonstrate the presence of collective frequency variation in real-world networks by considering the UK and Scandinavian power grids

A Nonlinear Model Predictive Control Scheme for Cooperative Manipulation with Singularity and Collision Avoidance

This paper addresses the problem of cooperative transportation of an object rigidly grasped by $N$ robotic agents. In particular, we propose a Nonlinear Model Predictive Control (NMPC) scheme that guarantees the navigation of the object to a desired pose in a bounded workspace with obstacles, while complying with certain input saturations of the agents. Moreover, the proposed methodology ensures that the agents do not collide with each other or with the workspace obstacles as well as that they do not pass through singular configurations. The feasibility and convergence analysis of the NMPC are explicitly provided. Finally, simulation results illustrate the validity and efficiency of the proposed method.Comment: Simulation results with 3 agents adde

Cooperative Adaptive Control for Cloud-Based Robotics

This paper studies collaboration through the cloud in the context of cooperative adaptive control for robot manipulators. We first consider the case of multiple robots manipulating a common object through synchronous centralized update laws to identify unknown inertial parameters. Through this development, we introduce a notion of Collective Sufficient Richness, wherein parameter convergence can be enabled through teamwork in the group. The introduction of this property and the analysis of stable adaptive controllers that benefit from it constitute the main new contributions of this work. Building on this original example, we then consider decentralized update laws, time-varying network topologies, and the influence of communication delays on this process. Perhaps surprisingly, these nonidealized networked conditions inherit the same benefits of convergence being determined through collective effects for the group. Simple simulations of a planar manipulator identifying an unknown load are provided to illustrate the central idea and benefits of Collective Sufficient Richness.Comment: ICRA 201

The role of ultrasound-driven microbubble dynamics in drug delivery : from microbubble fundamentals to clinical translation

In the last couple of decades, ultrasound-driven microbubbles have proven excellent candidates for local drug delivery applications. Besides being useful drug carriers, microbubbles have demonstrated the ability to enhance cell and tissue permeability and, as a consequence, drug uptake herein. Notwithstanding the large amount of evidence for their therapeutic efficacy, open issues remain. Because of the vast number of ultrasound- and microbubble-related parameters that can be altered and the variability in different models, the translation from basic research to (pre)clinical studies has been hindered. This review aims at connecting the knowledge gained from fundamental microbubble studies to the therapeutic efficacy seen in in vitro and in vivo studies, with an emphasis on a better understanding of the response of a microbubble upon exposure to ultrasound and its interaction with cells and tissues. More specifically, we address the acoustic settings and microbubble-related parameters (i.e., bubble size and physicochemistry of the bubble shell) that play a key role in microbubble cell interactions and in the associated therapeutic outcome. Additionally, new techniques that may provide additional control over the treatment, such as monodisperse microbubble formulations, tunable ultrasound scanners, and cavitation detection techniques, are discussed. An in-depth understanding of the aspects presented in this work could eventually lead the way to more efficient and tailored microbubble-assisted ultrasound therapy in the future