Intelligent Formation Control of Nonlinear Multi-Agent Systems

非线性多智能体系统的智能编队控制

• 批准号: RGPIN-2018-05093
• 负责人: Selmic, Rastko
• 金额: $ 2.04万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=682474
• 关键词: Intelligent; Formation; Control; Nonlinear; Multi

The main goal of this proposal is to establish a long-lasting research program in intelligent and robust formation control of nonlinear multi-agent systems and their swarms. Recent results in formation control of multi-agent systems mostly assume simple agents' models (point mass, single and double integrators), ideal communication links between the agents, and ideal, unconstrained on-board sensors. While those results are important from a theoretical standpoint, their use is limited in practical multi-agent applications.******The proposed research program aims to bridge that gap and to study intelligent control of multi-agents in rigid graph formations. Moreover, multi-agents will include nonlinear models with various nonlinearities, and non-ideal sensor and communication models. The proposed research aims to develop a new framework for rigid graph formation control of mobile agents in 3D space that is based on intelligent control tools such as Recurrent Neural Networks (RNNs), which showed great promise in classical nonlinear control. The developed controllers will have real-time learning capabilities, will be able to accommodate for the systems' nonlinearities and sensors' constraints, and will have adjustable structure to account for rapidly changing formations of agents in the field. Since multi-agents are inherently distributed systems, the proposed control algorithms will be distributed in nature, while aiming to achieve a global objective of the swarm.******The novelty in this research program is threefold: (1) A Neural Network (NN) formation control of nonlinear agents whose movements are constrained with graph rigidity and where both the control algorithms and the NN structure are novel. The NN structure is adaptable in real-time based on number of adjacent agents in the field and the overall network resembles a deep learning NN with many layers from different agents. Adaptation of the intelligent controller will be based on local indices of performance that consider graph rigidity, energy constraints, and agent's nonlinear dynamics. (2) A fault-tolerant multi-agent formations that are robust on individual agent failures and where agents exploit the localized swarm topology in order to monitor and detect faults of individual agents. (3) A new heterogeneous multi-robot test bed that will enable experimentation with multi-agent formation control at various levels including static and mobile sensors, ground-based agents, and micro-aerial vehicles. ******The research program will combine two emerging areas of engineering and computer science, i.e. multi-agent systems and deep learning NNs. As a result, future multi-agent formations will improve robustness to failures of individual agents, will have built-in intelligence to adapt to changing dynamics (of individual agents and the swarm), and will be able to handle various nonlinearities and communication links imperfections.***********

本论文的主要目标是建立一个长期的非线性多智能体系统及其群体的智能和鲁棒编队控制研究计划。多智能体系统编队控制的最新研究成果大多假设智能体模型简单（质点、单积分器和双积分器），智能体之间的通信链路理想，传感器无约束。虽然这些结果从理论的角度来看很重要，但它们在实际的多智能体应用中的使用是有限的。拟议的研究计划旨在弥合这一差距，并研究智能控制的多智能体在刚性图形的形成。此外，多智能体将包括具有各种非线性的非线性模型，以及非理想的传感器和通信模型。建议的研究旨在开发一个新的框架，刚性图形形成控制的移动的代理在3D空间，是基于智能控制工具，如递归神经网络（RNNs），这在经典的非线性控制表现出很大的希望。开发的控制器将具有实时学习能力，能够适应系统的非线性和传感器的约束，并具有可调节的结构，以应对现场快速变化的代理形式。由于多智能体本质上是分布式系统，因此所提出的控制算法在本质上是分布式的，同时旨在实现群体的全局目标。在这项研究计划的新奇是三方面的：（1）神经网络（NN）形成控制的非线性代理人的运动受到限制与图形刚性和控制算法和NN结构都是新颖的。NN结构基于现场相邻代理的数量实时自适应，整个网络类似于具有来自不同代理的多层的深度学习NN。智能控制器的自适应将基于考虑图刚性、能量约束和代理非线性动态的局部性能指标。(2)一种容错多代理编队，是强大的个人代理故障和代理利用本地化的群拓扑结构，以监测和检测故障的个人代理。(3)一个新的异构多机器人测试床，将使实验与多智能体形成控制在不同的水平，包括静态和移动的传感器，地面代理，和微型航空器。** 该研究计划将联合收割机结合工程和计算机科学的两个新兴领域，即多智能体系统和深度学习神经网络。因此，未来的多智能体编队将提高对单个智能体故障的鲁棒性，将具有内置的智能以适应不断变化的动态（单个智能体和群），并能够处理各种非线性和通信链路缺陷。