Experimental Implementation of Distributed Average Tracking for Heterogeneous Physical Agents Using Neighbors’ Positions

The focus of this thesis is on average tracking algorithm implementation for a group of heterogeneous physical agents consisting of single-integrator, double-integrator and Euler-Lagrange dynamics. In the algorithms, each agent is able to track the average of the time-varying reference inputs, where each agent has access to only its own position, its own input signals and the relative positions between itself and its neighbors. The algorithms are experimentally implemented on a multi-robot platform under an undirected communication topology. Simulation results and the experimental results based on the multi-robot platform are shown to validate the proposed algorithms. Finally, the error in simulation and experiment is analyzed based on experimental environment and robot characteristics

A new effective metric for dynamical robustness of directed networks

In this article, dynamical robustness of a directed complex network with additive noise is inverstigated. The failure of a node in the network is modeled by injecting noise into the node. Under the framework of mean-square stochastic stability, a new robustness metric is formulated to characterize the robustness of the network in terms of synchronization to the additive noise. It is found that the node dynamics plays a pivotal role in dynamical robustness of the directed network. Numerical simulations are shown for illustration and verification

Distributed Tracking Control for Discrete-Time Multiagent Systems with Novel Markovian Switching Topologies

Distributed discrete-time coordinated tracking control problem is investigated for multiagent systems in the ideal case, where agents with a fixed graph combine with a leader-following group, aiming to expand the function of the traditional one in some scenes. The modified union switching topology is derived from a set of Markov chains to the edges by introducing a novel mapping. The issue on how to guarantee all the agents tracking the leader is solved through a PD-like consensus algorithm. The available sampling period and the feasible control gain are calculated in terms of the trigonometric function theory, and the mean-square bound of tracking errors is provided finally. Simulation example is presented to demonstrate the validity of the theoretical results