Search NASA⌕ Search

DOE OSTI · 1601132

Distributed formation control with open-loop Nash strategy

Abstract

In this paper, we investigate game theoretical multiagent formation control problem. The main challenge of this problem is how to enable local execution of game strategies, e.g., Nash equilibrium, which generally requires global information in the presence of communication topology among agents. Toward this, we first derive open-loop Nash equilibrium for agents minimizing formation error based performance indices and proceed to introduce a distributed estimation scheme to facilitate local implementation of the derived Nash strategies. In addition, a shrinking horizon control technique is integrated into the proposed scheme to handle unexpected state changes. Finally, the proposed scheme is fully distributed and ensures an ϵ-Nash equilibrium. An illustrative examples is presented to verify the effectiveness.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Lin, Wei, Li, Chaoyong, Qu, Zhihua, Simaan, Marwan A.. 2019-05-21. Distributed formation control with open-loop Nash strategy. https://doi.org/10.1016/j.automatica.2019.04.034

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗