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基于模型预测控制的非完整移动机器人在输入约束和未知干扰下的协同包围

MPC-Based Cooperative Enclosing for Nonholonomic Mobile Agents Under Input Constraint and Unknown Disturbance.

作者信息

Ju Shuang, Wang Jing, Dou Liya

出版信息

IEEE Trans Cybern. 2023 Feb;53(2):845-858. doi: 10.1109/TCYB.2022.3164713. Epub 2023 Jan 13.

Abstract

In this article, a model predictive control (MPC)-based cooperative target enclosing control approach is investigated for multiple nonholonomic mobile agents with input constraints and unknown disturbances. The agents are required to move along a desired circular orbit centered at a stationary target and maintain an even distribution on the orbit. Based on a dual-mode MPC strategy, a cooperative target enclosing control law is designed by only using the local sensing information. When the agents are inside a terminal region, a locally cooperative stabilizing control law is designed with a signal function defined componentwise part compensating for the unknown disturbances. A robust MPC algorithm is designed for the agents to enter the terminal region in finite time. Global asymptotic stability is guaranteed for multiple nonholonomic mobile agents with input constraints and unknown disturbances. Simulation results illustrate the effectiveness of the proposed approach.

摘要

本文针对具有输入约束和未知干扰的多个非完整移动机器人,研究了一种基于模型预测控制(MPC)的协同目标包围控制方法。要求机器人沿着以固定目标为中心的期望圆形轨道移动,并在轨道上保持均匀分布。基于双模MPC策略,仅利用局部传感信息设计了一种协同目标包围控制律。当机器人处于终端区域内时,通过按分量定义的信号函数部分补偿未知干扰,设计了一种局部协同稳定控制律。设计了一种鲁棒MPC算法,使机器人能在有限时间内进入终端区域。对于具有输入约束和未知干扰的多个非完整移动机器人,保证了全局渐近稳定性。仿真结果说明了所提方法的有效性。

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