LQR优化的BP神经网络PID控制器设计  

Design of BP Neural Network PID Controller Optimized by LQR

作　　者：唐伎玲[1] 赵宏伟[2] 王婷婷 胡黄水[3] TANG Jiling;ZHAO Hongwei;WANG Tingting;HU Huangshui(School of Computer Science and Technology,Changchun University,Changchun 130022,China;College of Computer Science and Technology,Jilin University,Changchun 130012,China;School of Computer Science and Engineering,Changchun University of Technology,Changchun 130012,China)

机构地区：[1]长春大学计算机科学技术学院,长春130022 [2]吉林大学计算机科学与技术学院,长春130012 [3]长春工业大学计算机科学与工程学院,长春130012

出　　处：《吉林大学学报（理学版）》2020年第3期651-658,共8页Journal of Jilin University:Science Edition

基　　金：吉林省科技创新专项基金(批准号:20190302026GX);吉林省发改委项目(批准号:2019C054-4).

摘　　要：针对传统神经网络PID(比例-积分-微分)控制器和传统线性二次调节器(LQR)优化型PID控制器对无刷直流电机转速控制恢复时间长及抗干扰性较差等问题,提出一种LQR优化的BP神经网络PID控制器,用于无刷直流电机的转速控制.首先,利用BP神经网络对PID增益进行调节,提高控制器的动态适应性和鲁棒性;然后,采用LQR优化BP神经网络最优输出,使其更接近目标PID增益.仿真结果表明,该控制器有效提高了响应速度,减小了稳态误差并增强了抗干扰能力.Aiming at the problem that the traditional neural network proportional-integral-differential(PID) controller and linear quadratic regulator(LQR) optimized PID controller had long recovery time and poor anti-interference for speed control of brushless direct current motor, we proposed a BP neural network PID controller optimized by LQR for speed control of brushless direct current motor. Firstly, BP neural network was used to adjust the PID gain to improve the dynamic adaptability and robustness of the controller. Secondly, LQR was used to optimize the optimal output of BP neural network, which was closer to the target PID gain. The simulation results show that the controller can effectively improve the response speed, reduce the steady-state error and enhance the anti-interference ability.

关 键 词：无刷直流电机 PID控制器 BP神经网络 线性二次调节器 转速控制 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]