基于神经网络的双闭环伺服系统自适应控制

Adaptive Control of Double Closed-Loop Servo System Based on Neural Network

针对双闭环伺服系统中传统自抗扰控制(ADRC)控制器待整定的参数较多且整定过程较复杂的问题,设计了一种基于径向基函数神经网络的ADRC控制器。考虑到组合控制律的独立性,设计线性状态误差反馈进一步降低参数整定复杂性。径向基函数神经网络将扩张状态观测器中的非线性误差增益作为其权值系数,在线辨识出被控对象的Jacobian信息,利用神经网络的自学习功能实现了ADRC的参数在线自整定。以永磁同步电机(PMSM)作为被控对象,通过MATLAB进行仿真。仿真结果证明,此控制策略有效地优化了伺服系统的静态性能和动态品质,实现了控制系统的高动态和高精度。

The traditional active disturbance rejection control(ADRC)controller in a double closed-loop servo system have many parameters and very complicated adjusting processes.In order to solve these problems,an ADRC controller based on a radial basis function neural network is designed.Since the law of the combined control contains the characteristic of independence,a linear state error feedback is designed to further reduce the complexity of parameter setting.The gains of nonlinear errors in an extended stage observer are applied to the radial basis function neural network as weight coefficients,and the online identification for the controlled object’s Jacobian information could be carried out.So,the parameter online self-tuning of the ADRC controller could be realized through the self-learning ability of the neural network.Taking a permanent magnet synchronous motor(PMSM)as the controlled object,the experiment is carried out in MATLAB simulation.The results show that this control strategy can effectively optimize the static performance and dynamic quality of the servo system,and the better dynamic and higher precision of the system is reached.