海南大学学报（自然科学版）2025，Vol.43Issue(3)：297-304,8.DOI:10.15886/j.cnki.hndk.2024121301
约束条件下电机的一种神经网络自适应控制算法
Neural network adaptive control for a DC motor with bounded constraints
摘要
Abstract
Aimed at the problems that the output and state constraints of the motor were limited within a bounded range,and there are unknown dynamics(nonlinear time-varying factors including friction,parameter uncertainty,external interference and others)that affect the control performance of the system,in the report,a neural network adaptive control algorithm based on integral barrier Lyapunov function(iBLF)was proposed.Firstly,based on the Lyapunov stability theory,the backstepping control method was adopted and iBLF was constructed to guarantee the constraints on output and state as well as the stability of the system;secondly,the RBF neural network was used to approximate the unknown nonlinear terms in the dynamic system for the unknown dynamic compensation of the DC motor system,by which the angular velocity can track the expected value quickly,the output and state are kept within the predetermined range,and the tracking error is constrained by decreasing exponentially over time;finally,the simulation experiment results further proved the effectiveness of the proposed control method.关键词
电机控制/有界约束/积分障碍/李雅普诺夫函数/自适应控制Key words
motor control/bounded constraints/integral barrier/Lyapunov function/adaptive control分类
信息技术与安全科学引用本文复制引用
李晓梅,郭晓君,陈学军..约束条件下电机的一种神经网络自适应控制算法[J].海南大学学报（自然科学版）,2025,43(3):297-304,8.基金项目
福建省自然科学基金项目(2022J011169) （2022J011169）
福建省科技计划引导性项目(2021H0058) （2021H0058）
