基于混合算法改进的采煤机采高仿人智能控制模型设计
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1. 中北大学 动态测试省部共建国家重点实验室 太原 030051;2. 中北大学 电气与控制工程学院 太原 030051

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TP273+.3

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山西省应用基础研究计划青年科技研究基金资助项目“改进二值局部特征与深层聚合网络融合的煤岩智能识别模型”(编号:201901D211249);山西省高等学校科技创新项目“煤岩界面智能检测、跟踪与采煤机采高仿人智能控制模型研究”(编号:2020L0294)资助


Design of human-simulated intelligent control model for mining height of shearer based on improved hybrid algorithm
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1. Dynamic testing provincial department co-builds the national key laboratory, North University of China ,Taiyuan 030051,China; 2. School of Electrical and Control Engineering, North University of China, Taiyuan 030051,China.

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    摘要:

    为解决基于仿人智能控制的采煤机采高控制系统在模态转换时易发生边界突变、稳定性差、模态参数不易确定等问题,提出一种基于模糊逻辑改进模态切换,利用粒子群算法优化参数的仿人智能控制模型。该模型将仿人智能控制误差相平面的特征模态扩展为模糊集合,通过模糊逻辑推理跟随误差和误差变化实时地选择最优的控制模态,通过粒子群算法跟随误差和误差变化实时地调整控制模态参量。以阶跃响应模拟在煤岩界面遇见断层的控制性能仿真实验表明,本文所提出的仿人智能控制模型相较于原始仿人智能控制算法稳定时间提高了4.71s、上升时间提高了0.345s、峰值时间提高了0.671s、超调量降低了5.458%。本文提出的模型在系统模态转换时的稳定性、快速性、鲁棒性及参数寻优方面均好于其他控制模型,具有优越性。

    Abstract:

    In order to solve the problems of boundary mutation, poor stability and difficult determination of modal parameters in mode conversion of shearer mining height control system based on human-simulated intelligent control, a human-simulated intelligent control model based on fuzzy logic to improve mode switching and particle swarm optimization to optimize parameters is proposed. The model extends the characteristic mode of the human-simulated intelligent control error phase plane to a fuzzy set. The optimal control mode is selected in real time by fuzzy logic reasoning following the error and error change, and the control mode parameters are adjusted in real time by particle swarm optimization following the error and error change. The step response simulation is used to simulate the control performance of the fault at the coal-rock interface. The simulation results show that the human-simulated intelligent control model proposed in this paper improves the stability time by 4.71 s, the rise time by 0.345 s, the peak time by 0.671 s, and the overshoot by 5.458 % compared with the original human-simulated intelligent control algorithm. The stability, rapidity, robustness and parameter optimization of the model proposed in this paper are better than those of other control models in system modal transformation, and have superior performance.

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王冲,孙传猛,靳鸿,李欣宇,魏宇.基于混合算法改进的采煤机采高仿人智能控制模型设计[J].电子测量技术,2022,45(1):35-42

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  • 在线发布日期: 2024-06-19
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