摘要
机械臂的伺服系统面临着高精度和快速响应的冲突、动态扰动下稳定性不足等问题。以永磁同步电机为研究对象,通过RBF网络磁场矢量控制系统改进控制器结构,改善了PI控制器的积分迟滞作用,提高了系统响应速度;使用监督学习改善了神经网络在控制系统中的不稳定问题;使用在线学习的思路提高了控制系统在环境变化时的适应能力。实验结果表明,RBF网络磁场矢量控制系统具有良好的稳态性能,能够有效提高永磁同步电机响应速度和抗干扰能力。
In order to solve the problems of the conflict of high precision and fast response, and stability of robotic arm servo control in a dynamic environment, several measures were proposed. Firstly, taking the permanent magnet synchronous motor(PMSM) as the study object, a radial basis function(RBF) field-oriented control(FOC) system was developed to improve the controller structure, to overcome the integral hysteresis of the PI controller and to improve the response speed of the system. And then, a supervised learning method was used to solve the instability problem of neural network in the control system. An online learning method was applied to improve the adaptability of the control system in a dynamic environment. The experiment results show that the proposed methods can effectively improve the stability of the RBF-FOC system, the dynamic response speed and anti-interference ability of the PMSM.
作者
唐晓刚
杨广雨
郇浩
余昊元
李可盈
TANG Xiaogang;YANG Guangyu;HUAN Hao;YU Haoyuan;LI Keying(School of Space Information,Space Engineering University,Beijing 101400,China;School of Information and Electronics,Beijing Institute of Technology,Beijing 100081,China)
出处
《北京理工大学学报》
EI
CAS
CSCD
北大核心
2022年第10期1089-1096,共8页
Transactions of Beijing Institute of Technology
基金
国家自然科学基金资助项目(62027801)。
关键词
永磁同步电机
磁场矢量控制
RBF神经网络
模型预测控制
permanent magnet synchronous motor(PMSM)
field-oriented control
RBF network
model pre-dictive control
