基于PSO−BP神经网络的临时支架支撑力自适应控制

田劼; 李阳; 张磊; 刘振

doi:10.13272/j.issn.1671-251x.2022100017

基于PSO−BP神经网络的临时支架支撑力自适应控制

doi: 10.13272/j.issn.1671-251x.2022100017

田劼^{1, 2,},
李阳^{1, 2},
张磊^{1, 2},
刘振^{1, 2}

1.
中国矿业大学(北京) 机电与信息工程学院, 北京　100083
2.
中国矿业大学(北京) 煤矿智能化与机器人创新应用应急管理部重点实验室, 北京　100083

基金项目: 国家自然科学基金面上项目（51774293）；中央高校基本科研业务−重点领域交叉创新项目（2022JCCXJD02）。

详细信息

作者简介:
田劼 (1982—)，女，山西太原人，教授，博士，主要研究方向为机器人化矿山综掘智能控制，E-mail：tianj@cumtb.edu.cn

中图分类号: TD355.4
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- 被引次数: 0
出版历程
- 收稿日期: 2022-10-10
- 修回日期: 2023-07-16
- 网络出版日期: 2023-08-03

Adaptive control of temporary support force based on PSO-BP neural network

TIAN Jie^{1, 2
,},
LI Yang^{1, 2},
ZHANG Lei^{1, 2},
LIU Zhen^{1, 2}

1.
School of Mechanical Electronic and Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
2.
Key Laboratory of Coal Mine Intelligence and Robot Innovative Application, Ministry of Emergency Management, China University of Mining and Technology-Beijing, Beijing 100083, China

摘要

摘要: 为了使临时支架的支撑力更好地与矿压相适应，提高支架的支护能力，以双联自移式临时支架为研究对象，提出了基于粒子群优化（PSO）−BP神经网络的临时支架支撑力自适应控制方法。利用PSO算法的全局搜索能力及快速收敛特性对BP神经网络的初始权值进行优化，提高BP神经网络的收敛速度；再通过优化后的BP神经网络实现PID参数在线自调整，构建PSO−BP神经网络优化PID控制器，使临时支架的支撑力更快速、准确地达到预定值，实现临时支架支撑力自适应控制，避免因支护力和顶板压力不匹配而对顶板造成破坏。用单位阶跃信号模拟临时支护支架的期望初撑力进行实验验证，结果表明，与BP神经网络优化PID控制器及传统PID控制器相比，PSO−BP神经网络优化PID控制器可以更快、更准确地达到预期的初撑力，调整时间仅为0.5 s且基本不存在超调。根据实际地质条件仿真模拟开挖支护过程中支架受到的顶板压力，研究3种控制器的支撑力自适应控制效果，结果表明，在PSO−BP神经网络优化PID控制器的控制下，系统误差仅为0.02 MPa，误差最小，控制效果最好。
- 综掘工作面 /
- 临时支护 /
- 支撑力自适应控制 /
- PSO−BP神经网络 /
- PID控制
Abstract: In order to make the temporary support force better adapt to the mine pressure and improve the support capacity of the support, taking the dual self-moving temporary support as the research object, an adaptive control method of temporary support force based on particle swarm optimization (PSO) - BP neural network is proposed. The initial weights of the BP neural network are optimized by using the global search capability and fast convergence features of the PSO algorithm to improve the rate of convergence of the BP neural network. Then, the optimized BP neural network is used to achieve online self-adjustment of PID parameters. The PSO-BP neural network is constructed to optimize the PID controller. This enables the temporary support force to reach the predetermined value more quickly and accurately, achieving adaptive control of the temporary support force. It avoids damage to the roof due to the mismatch between support force and roof pressure. The expected initial support force of the temporary support is simulated using unit step signals for experimental verification. The results show that compared with the BP neural network optimized PID controller and traditional PID controller, the PSO-BP neural network optimized PID controller can achieve the expected initial support force faster and more accurately. The adjustment time is only 0.5 s and there is almost no overshoot. Based on actual geological conditions, the roof pressure on the support during excavation support is simulated. The adaptive control effect of three controllers for support force is studied. The results show that under the control of the PSO-BP neural network optimized PID controller, the system error is only 0.02 MPa, with the smallest error and the best control effect.
- fully mechanized excavation working face /
- temporary support /
- adaptive control of support force /
- PSO-BP neural network /
- PID control

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图 1 双联自移式临时支护支架结构

1—底座；2—支架护帮板；3—护帮板千斤顶；4—顶板千斤顶；5—上顶板；6—下顶板；7—液压支撑油缸；8—推移油缸。

Figure 1. Structure of dual self-moving temporary support

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图 2 临时支撑力控制系统结构

1—油箱；2—滤油器；3—电动机；4—液压泵；5—电液伺服阀；6—液压锁；7—油液压力传感器；8—支撑液压缸；9—溢流阀。

Figure 2. Structure of support force control system of temporary support

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图 3 临时支架支撑力控制系统原理

Figure 3. Principle of support force control system of temporary support

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图 4 临时支架支撑力控制系统性能曲线

Figure 4. Performance curves of support force control system of temporary support

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图 5 调整后系统Bode图

Figure 5. Bode diagram of the adjusted system

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图 6 BP神经网络结构

Figure 6. BP neural network structure

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图 7 初撑力响应曲线

Figure 7. Response curves of initial support force

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图 8 初撑力误差曲线

Figure 8. Error curves of initial support force

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图 9 巷道地质仿真模型

Figure 9. Roadway geological simulation models

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图 10 临时支架支撑力自适应控制曲线

Figure 10. Support force adaptive control curves of temporary support

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图 11 临时支架支撑力自适应控制误差曲线

Figure 11. Error curves of support force adaptive control of temporary support

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表 1 临时支架支撑力控制系统参数

Table 1. Parameters of support force control system of temporary support

参数	数值
液压缸内腔直径/mm	130
液压缸活塞杆外径/mm	80
k_q/(L∙min⁻¹∙m⁻¹)	27 000
k_ce/(L∙min⁻¹∙MPa⁻¹)	0.06
A_q/cm²	84.425
ω_n/Hz	502.4
ω_h/Hz	0.13
ω₀/Hz	842.5
ζ₀	0.15
k_v/(m∙A⁻¹)	0.056
ω_v/Hz	110
ζ_v	0.7
k_a/(A∙V⁻¹)	0.007
k_f/(V∙N⁻¹)	100

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表 2 煤矿地质参数

Table 2. Coal mine geological parameters

顶底板名称	岩层名称	厚度/m	平均厚度/m
基本顶	砂岩	2.28~11.97	7.29
直接顶	泥岩	0.5~13.8	5.4
煤层	煤	0.66~3.24	3
直接底	泥岩	2.8	2.8
基本底	泥岩	4	4

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表 3 各岩层力学参数

Table 3. Mechanical parameters of each rock layer

岩层名称	密度/ (kg·m⁻³)	体积模量/GPa	剪切模量/GPa	黏聚力/MPa	内摩擦角/(°)
砂岩	2650	6	3.6	3.0	35
泥岩	2550	5	2.3	1.2	28
煤	1650	4	2.5	1.0	24
砂质泥岩	2000	5	3.0	2.0	33

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