几类新的分布式自适应迭代学习协议的设计

This project mainly studies coordination problems for several kinds of nonlinear multi-agent systems by using adaptive iterative learning control, and design several new distributed adaptive iterative learning protocols such that the nonlinear multi-agent systems can achieve consensus in a finite time interval. In order to solve the perfect consensus problem for nonlinear multi agent systems with unknown control direction in a finite time interval, we explore the design method of distributed adaptive iterative learning control protocol based on Nussbaum gain. Design fuzzy observer-based distributed adaptive iterative learning protocol for continuous nonlinear multi-agent systems with unmeasured states, and then establish the theory and methods of perfect output consensus and the formation control problems for nonlinear multi-agent systems in a finite time interval. We study the precise consensus of nonlinear discrete multi-agent systems and propose the basic principles and methods for the design of distributed discrete adaptive iterative learning protocol, which realize the combination between adaptive discrete iterative learning control theory and distributed discrete nonlinear multi-agent systems. This research leads to significant theoretical value and wide application prospects in many fields, such as cooperative control of multi-robot systems.

本项目拟运用自适应迭代学习控制的方法研究几类非线性多智能体系统精确协调合作问题，设计出几类新的分布式自适应迭代学习协议，使得非线性多智能体系统能够在有限时间区间上达到完全一致。探索基于Nussbaum增益的分布式自适应迭代学习控制协议的设计方法，解决控制方向未知的非线性多智能体系统在有限时间区间上的精确一致性。针对状态不可观测的连续非线性多智能体系统，设计基于观测器的分布式模糊自适应迭代学习协议，建立非线性多智能体系统在有限时间区间上的精确输出一致性和编队控制问题的理论和方法。对离散非线性多智能体系统的精确一致性，提出分布式离散自适应迭代学习协议设计的基本原理和方法，实现离散自适应迭代学习控制理论与分布式离散非线性多智能体系统的有效结合。本研究成果在多机器人系统的协作控制等领域具有重要的理论意义和广阔的应用前景。

自适应控制与学习控制方法可以有效地解决具体未知非线性动态的多智能体系统的一致性。本项目运用自适应迭代学习控制的方法研究几类非线性多智能体系统协调合作问题，设计出了几类分布式自适应迭代学习协议。设计基于Nussbaum增益的分布式自适应迭代学习控制协议，解决了控制方向未知的非线性多智能体系统在的精确一致性。针对状态不可观测的连续线性多智能体系统，设计基于观测器的分布式模糊自适应迭代学习协议，建立非线性多智能体系统在有限时间区间上的精确输出一致性和编队控制问题的理论和方法。此外，研究了多智能体系统的量化一致性问题。设计了几类量化迭代学习控制协议，解决了多智能体系统量化一致性问题。并探讨了边Laplacian在设计具有量化状态信息的自适应一致性协议中的作用。此外，当多智能体系统具有未知的相同控制方向时，在协议中为每个节点设计基于Nussbaum增益项的控制协议，并且所有节点的Nussbaum增益项可以自适应地调节协作从而寻求真实的控制方向。本研究成果在多机器人系统的协作控制等领域具有重要的理论意义和广阔的应用前景。