An Overview of Recent Progress in the Study of Distributed Multi-agent Coordination

This article reviews some main results and progress in distributed multi-agent coordination, focusing on papers published in major control systems and robotics journals since 2006. Distributed coordination of multiple vehicles, including unmanned aerial vehicles, unmanned ground vehicles and unmanned underwater vehicles, has been a very active research subject studied extensively by the systems and control community. The recent results in this area are categorized into several directions, such as consensus, formation control, optimization, task assignment, and estimation. After the review, a short discussion section is included to summarize the existing research and to propose several promising research directions along with some open problems that are deemed important for further investigations

Genetic algorithms

Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology

Adaptive Synchronization of Complex Dynamical Networks with State Predictor

This paper addresses the adaptive synchronization of complex dynamical networks with nonlinear dynamics. Based on the Lyapunov method, it is shown that the network can synchronize to the synchronous state by introducing local adaptive strategy to the coupling strengths. Moreover, it is also proved that the convergence speed of complex dynamical networks can be increased via designing a state predictor. Finally, some numerical simulations are worked out to illustrate the analytical results

Consensus Control of Linear Multiagent Systems Under Actuator Imperfection: When Saturation Meets Fault

National Natural Science Foundation of China (Grant Number: 61873148 and 61733009); National Key Research and Development Program of China (Grant Number: 2017YFA0700300); Natural Science Foundation of Guangdong Province of China (Grant Number: 2018B030311054); Beijing National Research Center for Information Science and Technology BNRist Program of China (Grant Number: BNR2019TD01009); Royal Society of the U.K.; Alexander von Humboldt Foundation of Germany

Linear Regression Models Applied to Imperfect Information Spacecraft Pursuit-evasion Differential Games

Within satellite rendezvous and proximity operations lies pursuit-evasion differential games between two spacecraft. The extent of possible outcomes can be mathematically bounded by differential games where each player employs optimal strategies. A linear regression model is developed from a large data set of optimal control solutions. The model is shown to map pursuer relative starting positions to final capture positions and estimate capture time. The model is 3.8 times faster than the indirect heuristic method for arbitrary pursuer starting positions on an initial relative orbit about the evader. The linear regression model is shown to be well suited for on-board implementation for autonomous mission planning

Distributed Control for Robotic Swarms Using Centroidal Voronoi Tessellations

This thesis introduces a design combining an emerging area in robotics with a well established mathematical research topic: swarm intelligence and Voronoi tessellations, respectively. The main objective for this research is to design an economical and robust swarm system to achieve distributed control. This research combines swarm intelligence with Voronoi tessellations to localize a source and create formations. Extensive software coding must be implemented for this design, such as the development of a discrete centroidal Voronoi tessellation (CVT) algorithm. The ultimate purpose of this research is to advance the existing Mobile Actuator and Sensor Network (MASnet) platform to eventually develop a cooperative robot team that can sense, predict, and nally neutralize a diusion process. Previous work on the MASnet platform has served as a foundation for this research. While growing closer to the MASnet goal, results also provide stimulating discoveries for mathematical and swarm research areas