非重复系统的鲁棒迭代学习控制及其在多智能体系统中的应用

项目名称： 非重复系统的鲁棒迭代学习控制及其在多智能体系统中的应用

项目编号： No.61473010

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 自动化技术、计算机技术

项目作者： 孟德元

作者单位： 北京航空航天大学

项目金额： 82万元

中文摘要： 本项目研究非重复系统的鲁棒迭代学习控制及其在非重复环境下的多智能体系统协调控制问题处理中的应用。首先，开发非重复系统的鲁棒迭代学习控制，设计算法并建立新的学习收敛分析方法及综合理论来弥补基于压缩映像及不动点原理的核心学习收敛理论的不足。进而，通过分析非重复环境和各智能体这两种实际对象的联系与区别，设计分布式协调控制及迭代学习控制交叉算法，提出鲁棒收敛性条件，使多智能体系统具有给定协调性能和学习性能。然后，利用数值仿真并以移动机器人为平台进行实验，验证所提学习控制和交叉协调学习控制算法的有效性。本项目能建立迭代学习控制和多智能体系统协调控制各自理论发展的新领域和新方法，也能开发两类控制问题交叉研究的全新思路，为航天领域的卫星编队、工业领域的机器人协作、交通领域的车辆协调运行及能源领域的电网谐波电流补偿等实际应用提供有效的交叉协调学习控制技术，因此其研究具有重要的理论探索价值和广阔的应用前景。

中文关键词： 迭代学习控制；多智能体系统；非重复系统；鲁棒性；协调控制

英文摘要： This project investigates robust iterative learning control for non-repetitive systems and its application in dealing with coordination control problems of multi-agent systems that operate in non-repetitive environments. First, we develop robust iterative learning control for non-repetitive systems, design algorithms, and establish new analysis methods and synthesis theories for learning convergence in order to overcome shortcomings of the key learning convergence theory that is established based on the contraction mapping principle and fixed point theorem. Then through analyzing the relations and differences between two kinds of actual plants in multi-agent systems, i.e., the non-repetitive environments and the agents, we design crossover algorithms of distributed coordination control and iterative learning control, and propose robust convergence conditions such that multi-agent systems can achieve the prescribed coordination and learning performances. Moreover, we employ numerical simulations and also perform experiments on mobile robots to validate the effectiveness of the proposed algorithms for both learning control and crossover coordination learning control. This project can establish new fields and methods for the theorectical developments of both iterative learning control and coordination control of multi-agent systems, and can also develop new ideas for crossover studies on the two control problems, which provides effective crossover coordination learning control techniques for many practical applications, such as formation of satellites in the spacecraft field, cooperative operation of robots in the industry field, coordination running of vehicles in the transpotation field, and harmonic currents compensation of power grids in the energy field. Therefore, the studies of this project have both significant theoretical exploration value and broad application perspectives.

英文关键词： Iterative Learning Control;Multi-Agent Systems;Non-Repetitive Systems;Robustness;Coordination Control