摘要
针对直线电机驱动的精密运动平台轨迹跟踪控制中存在的轨迹跟踪精度不高以及外部干扰问题,提出了基于粒子群优化自抗扰控制器与基于BP神经网络优化自抗扰控制器相结合的控制策略。自抗扰控制算法对外部扰动进行估计和补偿,提高了直线电机的抗干扰能力;通过粒子群算法对自抗扰控制器的部分参数迭代寻优,解决人工整定参数的问题;通过BP神经网络拟合扩张观测器的部分扰动,降低扩张观测器的计算复杂度,提高了观测精度。仿真与实验结果表明,该方法控制下的直线电机有较高的轨迹跟踪精度,相较于自抗扰控制与粒子群优化的自抗扰控制,该方法显著提高了轨迹跟踪精度,适用于精密平台系统。
To tackle the problems of low trajectory tracking accuracy and external disturbance in trajectory tracking control of precision motion platform driven by linear motor,a control algorithm of control strategy based on particle swarm optimization based ADRC and BP neural network based ADRC was proposed.The active disturbance rejection control algorithm can estimate and compensate for external disturbances,which improves the anti-disturbance capability of the linear motor.The particle swarm algorithm is used to iteratively optimize some parameters of the active disturbance rejection controller to solve the problem of manual parameter tuning;through the BP neural network the network fits the partial disturbance of the expanded observer,which reduces the computational complexity of the expanded observer and improves the observation accuracy.The simulation and experimental results show that the linear motor controlled by this method has higher trajectory tracking accuracy.Compared with the ADRC and the particle swarm optimization ADRC,this method significantly improves the trajectory tracking accuracy and is suitable for precision platform systems.
作者
施昕昕
费军
SHI Xinxin;FEI Jun(School of Automation,Nanjing Institute of Technology,Nanjing 211167,China;School of Mechanical Engineering,Nanjing Institute of Technology,Nanjing 211167,China)
出处
《组合机床与自动化加工技术》
北大核心
2023年第6期132-135,共4页
Modular Machine Tool & Automatic Manufacturing Technique
基金
江苏省高等学校自然科学研究重大项目(20KJA510007)
江苏省自然科学基金面上项目(BK20201469)。
关键词
直线电机
轨迹跟踪
自抗扰控制
PSO-BP
linear motor
trajectory tracking
active disturbance rejection control
PSO-BP
