摘要
针对传统滑模变结构控制在三相电压型PWM整流器中应用时参数摄动所引起的抖动现象,提出一种改进PID神经网络的滑模变结构在线控制方法,将PID三个参数作为神经网络隐藏层的神经元,利用PID算法响应快、无静差的特点以及神经网络的在线自学习能力,实时对滑模趋近律参数进行修改,从而缩短系统状态进入滑模面的时间并减小抖动。对选取的价值函数进行改进,使算法不会陷入局部最优而逼近全局最优解,并对系统的全局稳定性进行分析。通过仿真和实验验证,结果表明该方法能使系统全局稳定,抖动有明显削弱且具有更好的动态响应。
For the problem that the system input parameters exists disturbances when the traditional sliding-mode varia-ble-structure control（SMVSC）is applied to the three-phase voltage source PWM rectifier,an online solution that sliding-mode variable-structure control base on improved PID neural network design was presented in this paper,taking three pa-rameters of PID as neurons of neural network in the hidden layer and considering that PID algorithm is of fast response,no static error and the online self-learning ability of neural network,the PID algorithm and neural network is combined to modi-fy the sliding approaching rate parameter in real-time,thus the time of system state into the sliding surface and jitter is re-duced.Through improving the selected value function,the algorithm cannot fall into local minimum and the global optimal solution is approached;also,the overall stability of the system is analyzed.Finally into simulation and experimental valida-tion research,show that the method possesses smaller shake and preferable dynamic response.
出处
《计算技术与自动化》
2015年第3期26-32,共7页
Computing Technology and Automation
基金
湖南省教育厅重点项目资助(12A136)
企业合作项目:特研协(2013-122-6)
关键词
PWM整流器
滑模变结构
PID神经网络
趋近律
全局最优解
PWM rectifier
sliding-mode variable-xstructure
PID neural network
reaching law
globally optimal
