Safety Guaranteed Optimal Control Policy for Multi-agent Data Harvesting Using a CLF-CBF Approach.

2023 AMERICAN CONTROL CONFERENCE, ACC（2023）

Cited 1|Views9

Abstract

We consider the problem of harvesting data from a set of targets in a wireless sensor network using a collection of mobile agents. The targets lie in a one dimensional mission space and broadcast their data while the agents move overhead. The agents are required to collect all the data and move to terminal locations to offload that data. We use a Hamiltonian analysis to show that the optimal control can be described using a parameterized policy and then develop a gradient descent scheme using infinitesimal perturbation analysis (IPA) to calculate the gradients of the cost function with respect to the control parameters. To avoid collisions between agents, we then apply a Control Lyapunov Function-Control Barrier Function (CLF-CBF) technique to ensure the agents closely track the desired optimal trajectory to complete their mission while avoiding any collisions. Additionally, we analyze the problem of symmetric deadlock in the CLF-CBF controller and show that it can be avoided by adding a small perturbation to the initial and final heights of the agents over the mission space. The approach is demonstrated through simulation.

Translated text

Key words

Wireless Energy Harvesting,RF Energy Harvesting,Multi-Agent Systems,Cooperative Control,Distributed Optimization

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper