The LQR controller design of two-wheeled self-balancing robot based on the particle swarm optimization algorithm
type: Item
instance of: scholarly article
title: The LQR controller design of two-wheeled self-balancing robot based on the particle swarm optimization algorithm
zbMATH Open document ID: 1407.93250
DOI: 10.1155/2014/729095
author: Xianqiang Yang
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: The dynamics model is established in view of the self-designed, two-wheeled, and self-balancing robot. This paper uses the particle swarm algorithm to optimize the parameter matrix of LQR controller based on the LQR control method to make the two-wheeled and self-balancing robot realize the stable control and reduce the overshoot amount and the oscillation frequency of the system at the same time. The simulation experiments prove that the LQR controller improves the system stability, obtains the good control effect, and has higher application value through using the particle swarm optimization algorithm.
Mathematics Subject Classification ID: 93C85
Mathematics Subject Classification ID: 90C59
zbMATH DE Number: 7017140
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/729095
OpenAlex ID: W2042407819
Wikidata QID: Q59071743
Recommended article: Q3180476
Recommended article: LQR and MPC controller design and comparison for a stationary self-balancing bicycle robot with a reaction wheel
Recommended article: A two-wheeled self-balancing robot with the fuzzy PD control method
Recommended article: Optimal control of a two‐wheeled self‐balancing robot by reinforcement learning
Recommended article: Design low-order robust controller for self-balancing two-wheel vehicle
Recommended article: Stochastic Fuzzy Controller Based on OCPFA and Applied on Two-Wheeled Self-balanced Robot
Recommended article: Adaptive observer-based output feedback control for two-wheeled self-balancing robot
description: scientific article; zbMATH DE number 7017140