Decentralized nonlinear estimation and control of multi-agent systems

多智能体系统的分散非线性估计和控制智能化优点：该项目涉及由多个相互作用的智能体组成的非线性动态系统的控制和估计算法的设计和分析。 每个代理都有自己的动态，这些动态受到代理的局部控制动作的影响，也可能受到干扰或与其他代理的不受控制的相互作用的影响。 所考虑的系统的显著特征是：1）控制器是分散的，即每个代理仅基于通过其传感器或通过与相邻代理的直接通信接收的信息来决定控制动作，以及2）两个代理是否是邻居是它们的状态的函数，使得互连代理的网络的拓扑随着代理的状态演变而随时间改变。 建议的工作的重点是同时控制和估计这些网络系统：本地控制行动的决定是基于当前估计的参数的整个集合的代理，使控制器和估计器的动态相互影响，通过反馈的相互作用。 这些复杂的非线性相互作用防止任何简单的分离原则的应用，因此，估计器的控制器的设计中考虑（和/或反之亦然）。 该项目将为这些多智能体系统的组合分散控制和估计提供特定的算法，通过数学分析和仿真来证明性能特性。 所提出的算法的应用可以包括单队目标，如编队控制或移动的传感器的最优分布，或多队目标，如多玩家的追求/evasion.Before被修改为估计器工作，控制器必须首先被设计为在完全信息下工作。 一类重要的局部控制器是梯度控制器，其中每个代理在相对于代理的个体成本（其可能与其他代理的成本相同或不同）的最陡下降方向上应用控制动作。所提出的工作的一个目标是确定类的成本函数，这些梯度计划具有适当的全局收敛性能（例如，任何平衡对应于一个不希望的配置应该是不稳定的）。 另一个目标是将辅助控制目标纳入基本的下降策略中，例如碰撞/障碍物避免（在移动的代理的情况下）或网络连接的维护。对于分散式实现，本地控制算法将依赖于估计器来获得关于收集所需的信息，但不能从本地传感器获得。 这样的估计器必须是动态的，在这个意义上，他们估计的信号随着时间的推移，随着代理的状态演变。 估计器的输入来自本地传感器或来自与邻近代理的直接通信。 目标的拟议工作包括设计和分析适当的估计，强调其收敛性能，估计增益优化，适应schemes.Broader影响：拟议的工作将提供工具的设计多智能体系统，潜在的各种应用领域的移动的机器人，传感器网络，制造（例如，智能部件的自组装），以及多玩家游戏（例如，协调的自动化战场或搜索和救援场景）。通过这项提案资助的研究生将受益于通过参与西北复杂系统研究所获得的跨学科视角，在那里他们与来自不同领域的研究人员分享想法（例如，经济学、医学、物理学、化学和工程学）。 此外，一名大学本科生将通过西北大学住宿学院项目赞助的助理研究员奖参与该项目。 这名本科生将受益于为研究项目做出重大贡献的机会，这是学生为科学生涯做好准备的重要一步。

Decentralized Nonlinear Estimation and Control of Multi-Agent SystemsIntellectual merit: This project involves the design and analysis of control and estimation algorithms for nonlinear dynamic systems consisting of multiple interacting agents. Each agent has its own dynamics which are affected by the agent's local control action and possibly also by disturbances or by uncontrolled interactions with other agents. The salient features of the systems considered are that 1) the controllers are decentralized, namely, each agent decides on a control action based solely on information it receives through its sensors or through direct communication with neighboring agents, and 2) whether or not two agents are neighbors is a function of their states, so that the topology of the network of interconnected agents changes with time as the states of the agents evolve. The focus of the proposed work is on the simultaneous control and estimation of these networked systems: decisions about local control actions are based on current estimates of the parameters of the entire collection of agents, so that the controller and estimator dynamics affect each other through feedback interactions. These complex nonlinear interactions prevent the application of any simple separation principle; as a result, the estimators are taken into account in the design of the controllers (and/or vice versa). This project will provide specific algorithms for the combined decentralized control and estimation of these multi-agent systems, with performance properties demonstrated both through mathematical analysis and through simulation. Applications of the proposed algorithms may include single-team objectives, such as formation control or the optimal distribution of mobile sensors, or multi-team objectives such as multi-player pursuit/evasion.Before being modified to work with estimators, the controllers must be designed first to work under full information. An important class of local controllers are the gradient controllers in which each agent applies a control action in the steepest descent direction relative to the agent's individual cost (which may or may not be the same as the costs of the other agents). One goal of the proposed work is to identify classes of cost functions for which these gradient schemes have appropriate global convergence properties (for example, any equilibrium corresponding to an undesirable configuration should be unstable). Another goal is to incorporate auxiliary control objectives into the basic descent strategies, such as collision/obstacle avoidance (in the case of mobile agents) or the maintenance of network connectivity. For decentralized implementation, local control algorithms will rely on estimators for information needed about the collection but not available from local sensors. Such estimators must be dynamic in the sense that the signals they estimate are changing with time as the states of the agents evolve. Inputs to the estimators come from local sensors or from direct communication with neighboring agents. Goals of the proposed work include the design and analysis of appropriate estimators, with an emphasis on their convergence properties, estimator gain optimizations, and adaptation schemes.Broader impacts: The proposed work will provide tools for the design of multi-agent systems, potentially contributing to a variety of applications in the fields of mobile robots, sensor networks, manufacturing (e.g., self-assembly of smart parts), and multi-player games (e.g., coordinated automated battlefield or search-and-rescue scenarios). Graduate students funded through this proposal will benefit from the interdisciplinary perspective gained through their involvement with the Northwestern Institute on Complex Systems, where they share ideas with researchers from diverse areas (e.g., economics, medicine, physics, chemistry, and engineering). In addition, a sophomore-level undergraduate student will participate in the project through a Fellow Assistant Researcher Award sponsored by Northwestern's Residential College program. This undergraduate student will benefit from the opportunity to make a significant contribution to a research project, an important step in preparing the student for a scientific career.