| 碱赤泥浆流量的非线性智能PID控制 |
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| 引用本文： | 白 锐,柴天佑.碱赤泥浆流量的非线性智能PID控制[J].控制理论与应用,2008,25(4):783-786. |
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| 作者姓名： | 白 锐 柴天佑 |
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| 作者单位： | 1. 辽宁工业大学电气工程学院,辽宁,锦州,121001东北大学流程工业综合自动化教育部重点实验室,辽宁,沈阳,110004
2. 东北大学流程工业综合自动化教育部重点实验室,辽宁,沈阳,110004东北大学自动化研究中心,辽宁,沈阳,110004 |
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| 基金项目： | 国家重点基础研究发展计划(973计划)，国家自然科学基金，国家创新研究群体科学基金，国家自然科学基金 |
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| 摘 要： | 在生料浆配料过程中,碱赤泥浆的流量直接影响着生料浆的各项工艺指标.针对碱赤泥浆流量的非线性和不确定性,本文提出了基于非线性补偿的智能PID控制方法.基于非线性补偿的智能PID控制器由常规PID控制器和非线性动态补偿两部分组成,利用极点配置方法设计常规PID控制器,利用神经网络对碱赤泥浆流量对象中的非线性部分进行逼近并在控制器中进行补偿.设计并开发了碱赤泥浆流量的非线性智能PID控制软件,应用结果表明,碱赤泥浆的流量可以准确、稳定的跟踪设定值,具有良好的控制效果.
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| 关 键 词： | PID控制 神经网络 非线性补偿 碱赤泥浆流量 |
| 收稿时间： | 2006/7/23 0:00:00 |
| 修稿时间： | 2007/8/11 0:00:00 |
Nonlinear intelligent PID control for the flowrate of red alkali slurry |
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| BAI Rui and CHAI Tian-you.Nonlinear intelligent PID control for the flowrate of red alkali slurry[J].Control Theory & Applications,2008,25(4):783-786. |
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| Authors: | BAI Rui and CHAI Tian-you |
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| Affiliation: | College of Electrical Engineering, Liaoning University of Technology, Jinzhou Liaoning 121001, China; Key Laboratory of Integrated Automation of Process Industry, Ministry of Education, Northeastern University, Shenyang Liaoning 110004, China;Key Laboratory of Integrated Automation of Process Industry, Ministry of Education, Northeastern University, Shenyang Liaoning 110004, China; Research Center of Automation, Northeastern University, Shenyang Liaoning 110004, China |
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| Abstract: | The flowrate of red alkali slurry is an important factor affecting the quality of raw slurry which is produced in the blending process.For the nonlinearities and uncertainties,a nonlinear intelligent proportional-integral-differential (PID)control strategy is proposed to control the flowrate of red alkali slurry,which is the integration of a conventional PID controller and a neural compensator for the unmodeled dynamics.The parameters of PID controller are determined by the pole placement approach,and neural networks are adopted to estimate and compensate the unmodeled nonlinear dynamics. The effectiveness and robustness of the proposed controller is validated in the application. |
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| Keywords: | PID control neural networks nonlinear compensation red alkali slurry |
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