A Distributed Model Predictive Control for Multiple Mobile Robots with the Model Uncertainty | Zendy

Wei Shang | Zendy; Hanzong Zhu | Zendy; Yurong Pan | Zendy; Xiuhong Li | Zendy; Daode Zhang | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

A Distributed Model Predictive Control for Multiple Mobile Robots with the Model Uncertainty

Author(s) -

Wei Shang,

Hanzong Zhu,

Yurong Pan,

Xiuhong Li,

Daode Zhang

Publication year - 2021

Publication title -

discrete dynamics in nature and society

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.264

H-Index - 39

eISSN - 1607-887X

pISSN - 1026-0226

DOI - 10.1155/2021/9923496

Subject(s) - model predictive control , computer science , mobile robot , mathematical optimization , minification , control theory (sociology) , stability (learning theory) , robot , lyapunov function , lyapunov stability , quadratic equation , control (management) , robust control , set (abstract data type) , artificial intelligence , control system , mathematics , machine learning , engineering , nonlinear system , physics , geometry , quantum mechanics , electrical engineering , programming language

In this paper, a distributed predictive control with the model uncertainty which uses the data-driven strategy and robust theory (data-driven RDMPC) is proposed for the formation control of multiple mobile robots. The robust performance objective minimization is applied to replace the quadratic performance objective minimization to establish the optimization problem, where the model uncertainty is considered in the distributed system. The control policy is derived by applying the data-driven strategy, and the future predictive value is obtained by employing the linear law in the historical data. Lyapunov theory is referred to analyze the stability of the mobile robot formation system. The effectiveness of the proposed method is proved by a set of simulation experiments.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research