| 摘要: |
| 设计了尾门自动控制系统，运用水位仪实时采集尾门水位数据，反馈调节电机转速驱动翻板门，实现水位控制。采用PID模型作为电机转速控制器，测试了固定PID参数条件下，系统在平原河流和山区河流河工模型中水位控制效果，山区河流水位最大控制偏差达到1.3 mm，无法满足规范要求。针对该问题运用产生式学习法总结了尾门控制中PID参数调节规则，构建了以水位跟踪偏差、水位涨落速度、涨落预期及跟踪相位为关键要素的知识库，确立了12条控制策略。尾门控制时根据实测数据与知识库规则自主选择对应控制策略，实现PID参数的自整定。工程实践表明，基于PID参数自整定的尾门控制系统水位控制最大偏差为0.5 mm。 |
| 关键词: 河工模型 尾门控制 PID控制 自整定 |
| DOI： |
| 分类号:TV83 |
| 基金项目:重大科学仪器设备开发专项（2011YQ070055）； 国家自然科学基金资助项目（51309083，51404331） |
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| Research on tailgate control of river model based on PID parameter by self tuning |
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CHEN Hong1, JI Yang2, TANG Limo1, WU Yanjun1
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1.College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing;2.College of Science, Hohai University, Nanjing
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| Abstract: |
| The tailgate automatic control system is designed, and the water level data of the tailgate is collected in real-time by water level meter,setting the flap door in motion by the motor speed of feedback regulation for realizing water level control. Using the PID model as the motor speed controller, the water level control effect in the physical models for alluvial rivers and mountain rivers under the conditions of the fixed PID parameter is tested. The maximum deviation of the water levels in the mountain rivers reaches 1.3 mm,which can not meet the requirements of the specification. For this problem, the rule of adjusting PID parameters in controlling the tailgate by using the production learning method summarized, and a knowledge base with the water level of tracking error,level fluctuation velocity, expected fluctuation and tracking phase as the key elements is established with 12 control strategies. According to the measured data and the knowledge base rules, the corresponding control strategy is chosen independently to realize the by self-tuning of PID parameters when the tailgate is controlled. The practical application results show that the maximum deviation in controlling the water level by the tailgate control system based on PID parameters self-tuning is 0.5 mm. |
| Key words: physical model tailgate control proportional integral derivative (PID) control self-tuning |
