基于改进BP−PID的带式输送机速度控制方法

桂改花; 苑占江

doi:10.13272/j.issn.1671-251x.2022080058

基于改进BP−PID的带式输送机速度控制方法

doi: 10.13272/j.issn.1671-251x.2022080058

广东科学技术职业学院计算机工程技术学院(人工智能学院), 广东珠海　519090

基金项目: 广东省普通高校特色创新项目（K01057037）。

详细信息

作者简介:
桂改花（1981—），女，山东聊城人，讲师，硕士，主要研究方向为数学建模、控制算法，E-mail：ggaihua2022@163.com

中图分类号: TD528.1
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- 被引次数: 0
出版历程
- 收稿日期: 2022-08-21
- 修回日期: 2023-05-15
- 网络出版日期: 2023-05-22

Speed control method for belt conveyor based on improved BP-PID

Computer Enineering Technical College (Artificial Intelligence College), Guangdong Polytechnic of Science and Technology, Zhuhai 519090, China

摘要

摘要: 针对传统BP−PID控制算法采用梯度下降法求解，存在收敛速度慢、易陷入局部极值且在低信噪比(LSNR)条件下性能下降等问题，提出了一种基于改进遗传模拟退火算法(ImGSAA)优化的BP−PID带式输送机速度控制方法(ImGSAA−BP−PID)。首先将交叉、变异概率取值与迭代时间关联，并引入反余弦函数增加遗传模拟退火算法（GSAA）动态调整和非线性变化适应能力。然后通过对传统Metropolis准则进行加权处理，提出加权Metropolis准则，对新种群个体进行修正，提升GSAA的噪声稳健性。最后利用ImGSAA对BP−PID初始参数进行优化，自动确定BP−PID的最优参数组合，从而提升参数整定的实时性和控制精度及对LSNR环境的适应能力。试验结果表明：① ImGSAA仅需11次迭代即可收敛，表明利用改进的交叉、变异策略和加权Metropolis准则对GSAA进行优化，能够有效提升算法的收敛速度和实时性。② ImGSAA−BP−PID的控制误差为−0.468 5~0.572 3 m/s，与遗传算法（GA）−BP−PID、粒子群算法（PSO）−BP−PID、GSAA−BP−PID的控制方法相比，分别提升了224.88%，104.07%，38.33%。③ ImGSAA性能受LSNR影响最小，迭代15次即收敛于全局最优解，具有较强的噪声稳健性。④ 在LSNR条件下，ImGSAA−BP−PID的控制误差均值下降了3.54%，控制性能明显优于GA−BP−PID，PSO−BP−PID，GSAA−BP−PID，更满足实际工程应用需求。
- 带式输送机 /
- 速度控制 /
- 遗传模拟退火算法 /
- 神经网络-PID /
- 低信噪比 /
- 参数优化
Abstract: The traditional BP-PID control algorithm uses the gradient descent method to solve, which has problems such as slow convergence speed, easy trapping in local extremum, and performance degradation under low signal-to-noise ratio (LSNR) conditions. In order to solve the above problems, a BP PID belt conveyor speed control method (ImGSAA-BP-PID) based on improved genetic simulated annealing algorithm (ImGSAA) optimization is proposed. Firstly, the values of crossover and mutation probabilities are correlated with the iteration time. The inverse cosine function is introduced to enhance the dynamic adjustment and nonlinear change adaptability of GSAA. Secondly, by weighting the traditional Metropolis criterion, a weighted Metropolis criterion is proposed to modify the new population individuals and improve the noise robustness of genetic simulated annealing algorithm (GSAA). Finally, ImGSAA is used to optimize the initial parameters of BP-PID, automatically determining the optimal parameter combination for BP-PID. It improves its real-time parameter tuning, control precision, and adaptability to the LSNR environment. The experimental results show the following points. ① ImGSAA only needs 11 iterations to converge, indicating that optimizing the GSAA using the proposed improved crossover and mutation strategies and weighted Metropolis criteria can effectively improve the convergence speed and real-time performance of the algorithm. ② The control error of ImGSAA-BP-PID is −0.468 5-0.572 3 m/s, which is 224.88%, 104.07%, and 38.33% higher than the control methods based on genetic algorithm (GA)-BP PID, particle swarm optimization (PSO)-BP PID, and GSAA-BP-PID, respectively. ③ The performance of ImGSAA is least affected by LSNR. It converges to the global optimal solution after 15 iterations, which has strong noise robustness. ④ Under LSNR conditions, the average control error of ImGSAA-BP-PID decreases by 3.54%. The control performance is significantly better than GA-BP-PID, PSO-BP-PID, and GSAA-BP-PID, which better meets the practical engineering application requirements.
- belt conveyor /
- speed control /
- genetic simulated annealing algorithm /
- neural network-PID /
- low signal-to-noise ratio /
- parameter optimization

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图 1 BP神经网络结构

Figure 1. Structure of BP neural network

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图 2 ImGSAA−BP−PID控制流程

Figure 2. Control process of the ImGSAA-BP-PID

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图 3 仿真架构

Figure 3. Simulation structure

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图 4 各算法迭代过程中参数变化曲线

Figure 4. Parameter change curve during iteration of different algorithms

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图 5 4种方法速度控制结果

Figure 5. Speed control results of four methods

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图 6 LSNR条件下速度变化曲线

Figure 6. Speed change curve under LSNR

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图 7 LSNR条件下参数变化曲线

Figure 7. Parameter change curve under LSNR

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图 8 LSNR条件下4种方法的速度控制误差

Figure 8. Speed control error of four methods under LSNR

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图 9 LSNR条件下4种方法的控制性能

Figure 9. Control performance of four methods under LSNR

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表 1 4种方法控制性能指标

Table 1. Control performance indexes of four methods

控制方法	调整时间/s	峰值/V	峰值时间/s	最大超调量	误差均值/ (m·s⁻¹)	误差均方根
GA−BP−PID	0.0056	1.1382	0.031	24.56	0.3356	1.53
PSO−BP−PID	0.0033	1.0845	0.027	15.31	0.2108	0.76
GSAA−BP−PID	0.0020	1.0056	0.007	10.09	0.1429	0.42
ImGSAA−BP−PID	0.0016	1.0073	0.006	7.11	0.1033	0.25

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表 2 LSNR条件下4种方法控制性能指标

Table 2. Control performance indexes of four methods under LSNR

调速方法	调整时间/s	峰值/V	峰值时间/s	最大超调量	误差均值/ (m·s⁻¹)	误差均方根
GA−BP−PID	0.0079	1.6578	0.062	34.77	0.5590	2.23
PSO−BP−PID	0.0039	1.1067	0.031	16.52	0.2910	0.92
GSAA−BP−PID	0.0055	1.3182	0.047	23.21	0.4755	1.39
ImGSAA−BP−PID	0.0019	1.0348	0.010	8.21	0.1387	0.45

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