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。
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.①ImGSAA 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.②The control error of ImGSAA-BP-PID is−0.4685-0.5723 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.③The performance of ImGSAA is least affected by LSNR.It converges to the global optimal solution after 15 iterations,which has strong noise robustness.④Under 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 GSAABP-PID,which better meets the practical engineering application requirements.
作者
桂改花
苑占江
GUI Gaihua;YUAN Zhanjiang(Computer Enineering Technical College(Artificial Intelligence College),Guangdong Polytechnic of Science and Technology,Zhuhai 519090,China)
出处
《工矿自动化》
CSCD
北大核心
2023年第5期104-111,共8页
Journal Of Mine Automation
基金
广东省普通高校特色创新项目(K01057037)。
关键词
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
belt conveyor
speed control
genetic simulated annealing algorithm
neural network-PID
low signal-to-noise ratio
parameter optimization
