Data-driven Dual-rate Control for Mixed Separation Thickening Process in a Wireless Network Environment
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摘要: 无线网络环境下赤铁矿混合选别浓密过程控制问题是以底流矿浆泵频率为内环输入，以底流矿浆流量为内环输出外环输入，以底流矿浆浓度为外环输出的非线性串级工业过程控制问题.其外环反馈回路存在丢包，且模型参数难以辨识，故本文利用工业运行过程的在线数据，设计不依赖模型参数的跟踪控制器.首先，利用浓密过程运行在工作点附近的特点进行线性化，对流量过程设计Q-学习控制器，保证流量过程能够跟踪给定的流量设定值；然后采用提升技术，得到统一时间尺度的以底流矿浆流量设定值为输入，以底流矿浆浓度为输出的被控对象；最后，考虑到在无线网络环境下浓度过程存在反馈丢包，当前的状态可能无法获得，故采用史密斯预估器的思想，利用历史的数据估计系统当前的状态，设计丢包Q-学习设定值控制器为流量过程提供最优设定值.通过仿真实验验证所提算法的有效性.Abstract: The mixed separation thickening process (MSTP) of hematite beneficiation in a wireless network environment is a nonlinear cascade process with the frequency of underflow slurry pump as the inner loop input, the slurry flow-rate as the inner loop output and the concentration as the outer loop output. The dropout occurs in the outer feedback loop, making it difficult to identify the parameters of the model, so the tracking controller only using the data generated by operational processes and independent of the knowledge of model parameters is designed in this paper. First, linearize the thickening system near the steady states, then design a controller based on Q-learning algorithm to make the inner process trace the set-point of the slurry flow-rate. Second, use the lifting technology to obtain the uniform time scale controlled object with the set-point of the slurry flow-rate as the input and the concentration as the output. Finally, considering that the networked-induced feedback dropout exists in the feedback process, meaning the current state information may be lost, a novel Smith predictor is developed to predict the current state from historical measured data, and a dropout Q-learning method is designed to provide the optimal set-point of lower loop. A simulation experiment on MSTP is given to show the effectiveness of the proposed method.
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Key words:
- Mixed separation thickening process /
- Q-learning /
- dropout /
- Smith predictor
1) 本文责任编委 侯忠生 -
图 2 数据驱动的无线网络下浓密过程的控制结构图
Fig. 2 Structure diagram of data-driven for MSTP under wireless network environment
图 3 浓密过程中浓度、流量的跟踪曲线以及底流泵转速的输入的曲线
Fig. 3 The tracing result of the slurry concentration and the slurry flow-rate, and the input of the frequency of slurry pump
图 5 流量过程Q-学习的结果
Fig. 5 The result of the slurry flow-rate process during the Q-learning process
图 6 浓度过程控制增益$\tilde L$的收敛过程
Fig. 6 Convergence of $\tilde L$to its optimal value $\tilde L$*
图 7 浓度过程丢包Q-学习的结果
Fig. 7 The result of the slurry concentration process during the dropout Q-learning process
图 8 浓度过程Q-学习的结果
Fig. 8 The result of the slurry concentration process during the Q-learning process
表 1 浓密过程符号表
Table 1 Mixed separation thickening process symbol table
符号 物理含义 符号 物理含义 $S$ 浓密机横截面积 $\frac{{\Delta \rho (t)}}{{g\rho (\cdot)}}$ 泵两端管路单位重量
矿浆的势能差$\mu$ 介质的粘度 $D$ 阻力损失 $p$ 平均浓度系数 $k_i$, $\bar{K}$ 与浓密机结构有关的常数 $p _s$ 矿浆内固体密度 $g$ 重力加速度 $p _l$ 矿浆内液体密度 $\theta(t)$ 干扰 $k_{0}$ 静态放大系数 $h(\cdot)$ 泥层界面高度 $\tau$ 时间常数 ${v_p}(\cdot)$ 矿浆颗粒沉降速度 ${\varphi _1}$ 浮选中矿矿浆浓度 ${q_1}$ 浮选中矿流量 ${\varphi _2}$ 污水浓度 ${q_2}$ 污水流量 ${\varphi _3}$ 磁选精矿矿浆浓度 ${q_3}$ 磁选精矿矿浆流量 
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表 2 对比实验2和3评价指标
Table 2 Performance index of comparison experiment
IAE MSE 本文$Q_2$ 8.4224 0.0191 未丢包 8.4093 0.0190 增大$Q_2$ 0.0418 6.63$\times 10^{-7}$
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