In traditional adaptive control, the certainty equivalence principle suggests a two-step design scheme. A controller is first designed for the ideal situation assuming the uncertain parameter was known, and it renders a Lyapunov function. Then, the uncertain parameter in the controller is replaced by its estimation that is updated by an adaptive law along the gradient of Lyapunov function. This principle does not generally work for a multiagent system as an adaptive law based on the gradient of (centrally constructed) Lyapunov function cannot be implemented in a distributed fashion, except for limited situations. In this paper, we propose a novel distributed adaptive scheme, not relying on gradient of Lyapunov function, for general multiagent systems. In this scheme, asymptotic consensus of a second-order uncertain multiagent system is achieved in a network of directed graph.

中文翻译：

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多智能体系统的分布式自适应方案

在传统的自适应控制中，确定性等价原则建议采用两步设计方案。控制器首先设计用于假设不确定参数已知的理想情况，并呈现 Lyapunov 函数。然后，控制器中的不确定参数被其估计值替换，该估计值由沿着 Lyapunov 函数梯度的自适应定律更新。该原理通常不适用于多智能体系统，因为基于（中央构造的）Lyapunov 函数梯度的自适应定律不能以分布式方式实现，除非在有限的情况下。在本文中，我们提出了一种新的分布式自适应方案，不依赖于 Lyapunov 函数的梯度，用于一般多智能体系统。在这个方案中，