Static Balance Control Method of Slope Pole Self-balancing Robot

王玉瑶; 贺利乐; 陈佳旋; 贺宁

doi:10.13433/j.cnki.1003-8728.20230304

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Static Balance Control Method of Slope Pole Self-balancing Robot

更新时间：2026-04-23

- Static Balance Control Method of Slope Pole Self-balancing Robot
- Mechanical Science and Technology for Aerospace Engineering Vol. 44, Issue 9, Pages: 1549-1556(2025)
- 作者机构：
  
  西安建筑科技大学机构机电工程学院,西安,710055
- 作者简介：
- 基金信息：
- DOI：10.13433/j.cnki.1003-8728.20230304
  CLC：
- Published：2025
- 稿件说明：
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王玉瑶, 贺利乐, 陈佳旋, et al. Static Balance Control Method of Slope Pole Self-balancing Robot[J]. Mechanical Science and Technology for Aerospace Engineering, 2025, 44(9): 1549-1556.
DOI：

王玉瑶, 贺利乐, 陈佳旋, et al. Static Balance Control Method of Slope Pole Self-balancing Robot[J]. Mechanical Science and Technology for Aerospace Engineering, 2025, 44(9): 1549-1556. DOI： 10.13433/j.cnki.1003-8728.20230304.

摘要

针对一种适用于斜坡极点平衡的自平衡机器人，提出了一种递推最小二乘法参数辨识和模型预测控制（Recursive least squares and model predictive control

RLS-MPC）相结合的方法，以实现自平衡机器人在不同斜坡极点的静态平衡控制。该方法使用递推最小二乘（Recursive least squares

RLS）法辨识系统状态参数，并与模型预测控制（Model predictive control

MPC）方法相结合，解决了自平衡机器人在斜坡极点平衡时状态参数未知的控制问题。同时，对闭环系统进行了基于Lyapunov的稳定性分析。最后通过与线性二次调节器（Linear quadratic regulator

LQR）和MPC方法进行数值仿真对比和性能指标对比，结果表明所提出的RLS-MPC控制方法相较于传统的LQR和MPC方法，不仅趋于稳定时间短，而且精度更高，能有效地改善自平衡机器人的控制性能。

Abstract

A new control method combining recursive least square parameter identification and model predictive control (RLS-MPC) is proposed to realize slope pole static balance control of self-balancing robots. This method uses the recursive least squares (RLS) method to identify the state parameters of the system

and combines with the model predictive control (MPC) method to solve the control problem that the state parameters of the self-balancing robot are unknown when the slope pole balance is performed. At the same time

the stability analysis of the closed loop system based on Lyapunov is carried out. Finally

the numerical simulation and performance comparison with linear quadratic regulator (LQR) and MPC methods show that the proposed RLS-MPC control method not only has a short settling time

but also has a higher accuracy compared with the traditional LQR and MPC methods

and can effectively improve the control performance of the self-balancing robot.

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