Research on large flow intelligent liquid supply system in fully mechanized working face
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摘要: 针对综采工作面供液系统供液能力不足、压力波动大、系统运行稳定性差等问题，提出了一种免疫粒子群优化模糊神经网络PID（IPSO−FNN−PID）算法，设计了IPSO−FNN−PID控制器，实现了供液系统稳压控制。IPSO−FNN−PID算法将粒子群（PSO）算法和免疫算法（IA）引入模糊神经网络（FNN）PID控制器，针对FNN算法易陷入局部寻优问题，采用免疫粒子群(IPSO)算法优化FNN算法，通过在PSO算法中加入IA来提高PSO算法的收敛性，实现最优PID参数输出。为验证IPSO−FNN−PID控制器的有效性，选取传统PID控制器、Fuzzy−PID控制器、FNN−PID控制器进行比较，仿真结果表明：① IPSO−FNN−PID控制器对乳化液泵的控制效果最佳，其他3种控制器的上升时间、峰值时间和调节时间均比IPSO−FNN−PID控制器长，最大超调量均大于IPSO−FNN−PID控制器。② 在加入扰动信号后，IPSO−FNN−PID控制器具有较好的自适应性和鲁棒性，恢复到平稳状态仅用了1.2 s。③ 当利用传统PID和Fuzzy−PID控制器对乳化液泵进行控制时，振荡明显，超调量大，分别为41.2%，22.3%；当利用FNN−PID控制器对乳化液泵进行控制时，振荡明显减弱，超调量降低为17.6%，调节时间减少至2.68 s；当利用IPSO−FNN−PID控制器对乳化液泵进行控制时，几乎无振荡，超调量仅为5.22%，调节时间缩短至2.61 s，遇到干扰信号时稳定性更强。④ 在受到扰动信号时，负载干扰对IPSO−FNN−PID控制器的影响较小，且收敛迅速，鲁棒性大大提升，表明IPSO−FNN−PID控制器具备良好的抗扰动及扰动补偿能力，可满足供液系统的稳压控制要求。Abstract: The liquid supply system in fully mechanized working face has the problems of insufficient liquid supply capacity, large pressure fluctuation and poor system operation stability. In order to solve the above problems, an immune particle swarm optimization fuzzy neural network PID (IPSO-FNN-PID) algorithm is proposed. The IPSO-FNN-PID controller is designed to stabilize the pressure of the liquid supply system. In the IPSO-FNN-PID algorithm, a particle swarm optimization (PSO) algorithm and an immune algorithm (IA) are introduced into a fuzzy neural network (FNN) PID controller. The immune particle swarm optimization (IPSO) algorithm is used to solve the problem that the FNN algorithm is easy to fall into local optimization. The IA is added to the PSO algorithm to improve the convergence of the PSO algorithm. Therefore, the output of the optimal PID parameters is realized. In order to verify the effectiveness of the IPSO-FNN-PID controller, traditional PID controller, Fuzzy-PID controller and FNN-PID controller are selected to compare. The simulation results show that the IPSO-FNN-PID controller has the best control effect on the emulsion pump. The rise time, peak time and regulation time of the other three controllers are longer than the IPSO-FNN-PID controller. The maximum overshoot is greater than the IPSO-FNN-PID controller. After adding the disturbance signal, the IPSO-FNN-PID controller has good adaptability and robustness, and it takes only 1.2 s to restore to a stable state. When traditional PID and Fuzzy-PID controllers are used to control the emulsion pump, the oscillation is obvious and the overshoot is large, which are 41.2% and 22.3% respectively. When the FNN-PID controller is used to control the emulsion pump, the oscillation is significantly weakened, the overshoot is reduced to 17.6%, and the adjustment time is reduced to 2.68 s. When the IPSO-FNN-PID controller is used to control the emulsion pump, there is almost no oscillation. The overshoot is only 5.22%, the adjustment time is shortened to 2.61 s. And the stability is stronger when encountering interference signals. When the disturbance signal is received, the load disturbance has little effect on the IPSO-FNN-PID controller, the convergence is rapid, and the robustness is greatly improved. The results show that the IPSO-FNN-PID controller has good anti-disturbance and disturbance compensation capability, and can meet the pressure stabilization control requirements of the liquid supply system.
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图 3 输入变量和输出变量的隶属度函数曲线
Figure 3. Membership function curves of input and output variable
表 1 模糊控制规则
Table 1. Fuzzy control rules
EC E NB NM NS ZO PS PM PB NB PB PB PM PM PS PS ZO NM PB PM PM PS PS ZO NS NS PM PM PS PS ZO NS NS ZO PM PS PS ZO NS NS NM PS PS PS ZO NS NS NM NM PM PS ZO NS NS NM NM NB PB ZO NS NS NM NM NB NB 
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表 2 各控制器PID控制参数及动态特性比较
Table 2. Comparison of PID control parameters and dynamic characteristics of each controller
控制器 ${K}_{{\rm{p}}}$ ${K}_{{\rm{i}}}$ ${K}_{{\rm{d}}}$ $ \mathrm{\sigma } $/% ${t}_{{\rm{r}}}$/s ${t}_{{\rm{p}}}$/s ${t}_{{\rm{s}}}$/s PID 3.215 4.583 4.112 41.2 1.01 1.71 3.72 Fuzzy−PID 0.691 2.892 3.672 22.3 1.34 1.63 3.56 FNN−PID 0.882 3.000 3.127 17.6 0.98 1.55 2.68 IPSO−FNN−PID 1.218 2.614 3.745 5.22 0.89 1.31 2.61
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