机械科学与技术2025，Vol.44Issue(9)：1549-1556,8.DOI:10.13433/j.cnki.1003-8728.20230304
斜坡极点自平衡机器人静态平衡控制方法
Static Balance Control Method of Slope Pole Self-balancing Robot
摘要
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.关键词
自平衡机器人/静态平衡/模型预测控制/参数辨识Key words
self-balancing robot/static balancing/model predictive control/parameter identification分类
信息技术与安全科学引用本文复制引用
王玉瑶,贺利乐,陈佳旋,贺宁..斜坡极点自平衡机器人静态平衡控制方法[J].机械科学与技术,2025,44(9):1549-1556,8.基金项目
国家自然科学基金项目(61903291) （61903291）
