Research on BP Neural Network PID Control Strategy for Improving Particle Swarm Optimization of Ball Screw Feed System

WU Qin; ZHOU Shunqian; WANG Xinglian

doi:10.7652/xjtuxb202406003

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Research on BP Neural Network PID Control Strategy for Improving Particle Swarm Optimization of Ball Screw Feed System

更新时间：2025-07-28

- Research on BP Neural Network PID Control Strategy for Improving Particle Swarm Optimization of Ball Screw Feed System
- Vol. 58, Issue 6, Pages: 24-33(2024)
- 作者机构：
  
  1. 兰州理工大学机电工程学院,兰州,730050
  2. 兰州石化公司,兰州,730060
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202406003
  CLC： TP273
- Online First：10 June 2024，
  
  Published：2024
- 稿件说明：
移动端阅览
WU Qin, ZHOU Shunqian, WANG Xinglian. Research on BP Neural Network PID Control Strategy for Improving Particle Swarm Optimization of Ball Screw Feed System[J]. 2024, 58(6): 24-33.
DOI：

WU Qin, ZHOU Shunqian, WANG Xinglian. Research on BP Neural Network PID Control Strategy for Improving Particle Swarm Optimization of Ball Screw Feed System[J]. 2024, 58(6): 24-33. DOI： 10.7652/xjtuxb202406003.

摘要

针对传统的BP神经网络PID(BP-PID)控制因其初始权值随机

导致系统的收敛速度较慢、控制前期会有较大误差和BP神经网络初始权值优化等问题

建立了滚珠丝杠进给系统伺服三环模型

设计了BP-PID控制器

提出了一种二阶振荡混沌映射粒子群算法(SCMPSO)优化滚珠丝杠进给系统BP-PID控制器。首先

混沌映射初始化粒子位置

使粒子均匀分布于空间

增加粒子解的多样性; 随后

提出一种非线性余弦自适应惯性权重

以平衡算法的全局搜索能力和局部搜索能力; 其次

在算法中引入二阶振荡环节

在面对突变多峰干扰时

能及时跳出局部最优解。研究结果表明: 当加入外界干扰时

控制策略SCMPSO-BP-PID在正向进给时段的位移平均误差为0.013 mm

相比SAWPSO-BP-PID、LDWPSO-BP-PID、PSO-BP-PID这3种控制策略分别提升约45.8%、55.2%、61.7%; 当加入阶跃响应时

SCMPSO-BP-PID的最大超调量仅为0.029

系统调节时间和峰值时间相比3种控制策略均有较大提升

具有较高的控制精度和稳定性。

Abstract

Traditional BP neural network PID(BP-PID)control has a slow convergence speed due to its random initial weights

resulting in significant errors in the early stages of control. This paper focuses on the initial weight optimization problem of BP neural network

establishes a servo three loop model of the ball screw feed system

designs a BP-PID controller

and proposes a second-order oscillation chaotic mapping particle swarm optimization algorithm(SCMPSO)to optimize the BP-PID controller of the ball screw feed system. Firstly

the chaotic mapping initializes the particle position

making the particles evenly distributed in space and increasing the diversity of particle solutions. Then

a nonlinear cosine adaptive inertia weight is proposed to balance the global search ability and local search ability of the algorithm. Finally

a second-order oscillation link is introduced in the algorithm

which can timely jump out of the local optimal solution in the face of sudden multi peak interference. The results show that when external interference is added

the average displacement error of SCMPSO-BP-PID during the forward feed period is 0.013 mm

which is about 45.8%

55.2%

and 61.7% higher than the three control strategies of SAWPSO-BP-PID

LDWPSO-BP-PID

and PSO-BP-PID

respectively. When step response is added

the maximum overshoot of SCMPSO-BP-PID is only 0.029

and the system adjustment time and peak time are significantly improved compared with the three control strategies

representing high control accuracy and stability.

关键词

Keywords

references

LIU Jiyuan, YU Houyu, WANG Xujie. Fuzzy PID control of parallel 6-DOF motion platform [C]//2023 5th International Conference on Intelligent Control, Measurement and Signal Processing. Piscataway, NJ, USA: IEEE, 2023: 274-277.

邓鲁克, 吕东坡. 基于遗传算法对控制水下机器人运动姿态进行PID参数整定 [J]. 制造业自动化, 2023, 45(1): 177-179.

DENG Luke, LÜ Dongpo. PID parameter tuning of remotely operated vehicle control attitude based on genetic algorithm [J]. Manufacturing Automation, 2023, 45(1): 177-179.

HE Zuming, DONG Ruili, TAN Yonghong. BP neural network PID control scheme for electromagnetic scanning micromirror [C]//2022 IEEE International Conference on Real-time Computing and Robotics. Piscataway, NJ, USA: IEEE, 2022: 322-326.

LIU Yugang. Research on BP neural network based on particle swarm optimization algorithm [C]//2023 2nd International Conference on Artificial Intelligence and Computer Information Technology. Piscataway, NJ, USA: IEEE, 2023: 1-3.

曾雄飞. 基于粒子群算法优化BP神经网络的PID控制算法 [J]. 电子设计工程, 2022, 30(11): 69-73.

ZENG Xiongfei. The PID control algorithm based on particle swarm optimization optimized BP neural network [J]. Electronic Design Engineering, 2022, 30(11): 69-73.

SUN Quan, SUN Yuan. Short-term prediction of BP neural network optimized by improved particle swarm optimization algorithm [C]//Proceedings International Conference on Advanced Algorithms and Neural Networks. Bellingham, WA, USA: SPIE, 2022: 1228511.

张淑芳, 宋香明, 朱彬华. 结合改进PSO-BP神经网络的无刷直流电机控制 [J]. 南开大学学报(自然科学版), 2021, 54(4): 62-67.

ZHANG Shufang, SONG Xiangming, ZHU Binhua. Brushless DC motor controller combined with improved PSO-BP neural network [J].Acta Scientiarum Naturalium Universitatis Nankaiensis, 2021, 54(4): 62-67.

CHEN Bingsheng, CHEN Huijie, LI Mengshan. Feature selection based on BP neural network and adaptive particle swarm algorithm [J]. Mobile Information Systems, 2021, 2021: 6715564.

YE Dan, WANG Xiaogang, HOU Jin. An edge computing offloading algorithm based on second-order oscillatory particle swarm optimization [C]//2022 3rd Information Communication Technologies Conference. Piscataway, NJ, USA: IEEE, 2022: 221-226.

WANG Chun, JIANG Tao, JI Bin, et al. Research on power network maintenance plan optimization based on improved second-order oscillation PSO [C]//2022 Power System and Green Energy Conference. Piscataway, NJ, USA: IEEE, 2022: 1043-1051.

韩硕. 基于滑模控制的滚珠丝杠进给系统运动控制研究 [D]. 南京: 东南大学, 2019.

张松伟. 基于数字孪生的数控机床工作台进给系统机械故障诊断研究 [D]. 西安: 西安理工大学, 2023.

梁生龙, 李军利, 赵新宽. 基于数字孪生的步进滑台远程虚实同步研究 [J]. 机电工程技术, 2022, 51(9): 122-126.

LIANG Shenglong, LI Junli, ZHAO Xinkuan. Research on remote virtual real synchronization of stepping slide based on digital twin [J]. Mechanical Electrical Engineering Technology, 2022, 51(9): 122-126.

王慧霞, 郭润兰, 赵强, 等. 基于交叉耦合与迭代学习的伺服系统运动控制研究 [J]. 机电工程, 2021, 38(4): 440-446.

WANG Huixia, GUO Runlan, ZHAO Qiang, et al. Motion control of servo system based on cross-coupling and iterative learning [J]. Journal of Mechanical Electrical Engineering, 2021, 38(4): 440-446.

邱国富, 贺云波, 陈观轩, 等. 永磁同步直线电机的降阶线性自抗扰控制器研究 [J]. 组合机床与自动化加工技术, 2023(12): 85-88.

QIU Guofu, HE Yunbo, CHEN Guanxuan, et al. Research on reduced-order linear active disturbance rejection controller for permanent magnet synchronous linear motor [J]. Modular Machine Tool Automatic Manufacturing Technique, 2023(12): 85-88.

LI Zheng, WANG Kangtao, WANG Jinsong, et al. Full-coefficient intelligent adaptive position control of permanent magnet synchronous linear motor [J]. Journal of Electrical Engineering Technology, 2023, 18(3): 1975-1984.

ZHANG Qiming, YU Haoyi, BARBIERO M, et al. Artificial neural networks enabled by nanophotonics [J]. Light: Science Applications, 2019, 8(1): 42.

张良, 何山, 艾纯玉. 基于Sine-SSA-BP神经网络模型的风机叶根载荷预测 [J]. 可再生能源, 2023, 41(10): 1322-1328.

ZHANG Liang, HE Shan, AI Chunyu. The wind turbine leaf root load prediction based on Sine-SSA-BP neural network model [J]. Renewable Energy Resources, 2023, 41(10): 1322-1328.

王文. 基于有限状态反馈遗传BP算法的高层结构振动控制研究 [D]. 合肥: 合肥工业大学, 2022.

王剑, 聂宜云. 基于PSO-BP算法优化的转台PID控制系统仿真研究 [J]. 航空精密制造技术, 2023, 59(6): 5-8.

WANG Jian, NIE Yiyun. Simulation research on neural network PID control system of turntable based on PSO-BP combination algorithm optimization [J]. Aviation Precision Manufacturing Technology, 2023, 59(6): 5-8.

石勇. 滚珠丝杠进给系统的轨迹跟踪控制方法研究 [D]. 南京: 东南大学, 2018.

WU Yanmin, SONG Qipeng. Improved particle swarm optimization algorithm in power system network reconfiguration [J]. Mathematical Problems in Engineering, 2021, 2021: 5574501.

邱辰霖, 程礼. 一种基于相邻数据依赖性的混沌分析方法 [J]. 物理学报, 2016, 65(3): 030503.

QIU Chenlin, CHENG Li.A chaotic analyzing method based on the dependence of neighbor sub-sequences in the data series [J]. Acta Physica Sinica, 2016, 65(3): 030503.

ZHANG Peng, CUI Zhiwei, WANG Yinjiang, et al. Application of BPNN optimized by chaotic adaptive gravity search and particle swarm optimization algorithms for fault diagnosis of electrical machine drive system [J]. Electrical Engineering, 2022, 104(2): 819-831.

吴易泽, 张旭, 江明阳, 等. 粒子群优化BP神经网络的曲线光顺算法 [J]. 化工自动化及仪表, 2018, 45(12): 939-942.

WU Yize, ZHANG Xu, JIANG Mingyang, et al. Curve smoothing algorithm for particle swarm optimization BP neural network [J]. Control and Instruments in Chemical Industry, 2018, 45(12): 939-942.

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