氧乐果合成过程集成智能控制方法与应用
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摘要
我国作为一个人口大国,农业始终是国民经济的基础,关系国家的稳定和发展。农药是保证农作物稳定高产的重要因素之一,农药的生产水平和质量对我国农业乃至整个国民经济的发展有很大的影响。为了提高农药生产的技术水平和产品质量,对传统的农药生产过程进行自动化改造,是非常必要的。本文以氧乐果合成过程为研究对象,研究了氧乐果合成过程的智能控制方法,并开发了已获得实际应用的氧乐果合成过程集成智能控制系统。
本文阐述了氧乐果合成的工艺流程,对氧乐果合成的特性和对象模型进行了详细分析,说明氧乐果合成具有非线性、时变、大滞后、扰动复杂的特点,并从系统可测参数和不可测参数两个方面分析了氧乐果合成中的影响因素。研究了基于神经网络的氧乐果合成过程的系统辨识,并针对氧乐果合成过程中影响最大的温度因素利用BP网络进行辨识。针对静态BP网络对系统动态模型辨识效果不佳的缺陷,采用分阶段BP网络和由静态BP网络加TDL环节所构成的回归网络对氧乐果合成中的温度对象进行了动态辨识,并对三种方法的辨识性能进行了比较分析。
根据氧乐果合成过程不同阶段的复杂特性,分别研究了相应的智能控制方法,作为整体设计氧乐果集成智能控制方案的基础。第一,针对氧乐果合成具有非线性、大滞后的特点,设计了一种参数反馈模糊控制器,并对其性能进行了仿真验证。
第二,针对氧乐果合成具有非线性、时变的特点,提出了基于递归遗传算法的混合学习算法,来对用于辨识的神经网络的结构和连接权值进行自动优化设计。针对模糊神经网络应用中存在的问题,提出了基于模糊补偿算子的神经网络控制器结构和算法。通过采用基于递归遗传算法的神经网络辨识器对氧乐果合成的温度对象进行辨识,与递归补偿模糊神经网络控制器组成温度智能控制系统,并对该控制系统的性能进行了仿真研究。
第三,针对氧乐果合成具有典型间歇过程的特点,在分析了迭代学习控制基本原理的基础上,通过在迭代学习控制中引入预测的思想,提出了一种改进的迭代学习算法。并将模糊模型辨识技术、预测控制和迭代学习控制三者相结合,设计了基于模糊预测的迭代学习控制器。以氧乐果合成过程中的温度为被控对象,仿真研究了所设计的控制器的性能。
根据一甲胺累积投料量,将氧乐果合成过程划分为四个阶段:反应初始阶段,温度上升阶段,稳定反应阶段和反应结束阶段,并根据不同阶段的反应特点,分别采用上述所研究的多种智能控制算法,组成氧乐果合成过程的集成智能控制方案,并开发了成套的氧乐果合成过程集成智能控制系统,包括控制系统信号处理、控制系统故障诊断、系统软件设计等。研制的两套系统分别在沙隆达郑州农药有限公司的两个农药分厂投入使用,2003年投入使用以来的运行效果表明:该系统技术先进、性能优良、工作可靠,取得了显著的社会经济效益。
As a great population country, agriculture is the base of national economy of China and has great relation with our country's stability and development. Pesticide is one of the main influencing elements on agriculture stability and high production. Production level and quantity of pesticide have great influence on our agriculture, then on the total national economy. To improve the technique level and production quality of pesticide production, it is very necessary to rebuild the traditional pesticide production precess. Omethod composition process as the research object, this paper researches the intelligent control methods during the Omethod composition process, and develops an Omethod composition procedure integrated intelligent control system that has been used in practice.
The technique flow of Omethod composition is introduced, and the reaction features and the subject model of Omethod composition are analyzed in detail. It is shown that Omethod composition reaction has the following characteristics: multi-variable, non-linear, time-changing, and complex disturbance. The influencing elements during Omethod composition procedure are analyzed from system measureable and non-measureable aspects. Furthermore, Omethod composition procedure system identification based on neural network is introduced. To the greatest influence element, temperature, during Omethod composition, a BP network is adopted to identify the temperature in the composition reaction. In order to overcome the shortcoming of static BP network in identifying the dynamic system parameters, a segmental BP network and recursive network composed of static BP and TDL are used to dynamically identify the temperature parameter. The identification results of BP Network, segmental BP network and recursive network are compared and analyzed.
According to the complex features during the Omethod composition, the corresponding intelligent control approaches are studied as the total design base of the Omethod composition reaction integrated intelligent control system. Due to the features of nonlinear, big lag, for the temperature control in Omethod composition, a parameter feedback fuzzy controller is designed and the controller performance is simulated.
Due to the features of nonlinear, time changing, recursive genetic algorithm is advanced which can design the network structure and weights automatically. For the existing problems in fuzzy neural network application, the neural network controller structure and algorithm based on fuzzy compensation operator are proposed. Neural network based on recursive genetic algorithm is adopted to identify the temperature during Omethod composition procedure. Combining the neural network identifier with the recursive fuzzy neural network construct a temperature intelligent control system. The control quality of the intelligent system is tested through simulation.
Due to Omethod composition being an intermission process, based on the principle of iterative learning control, the forecasting thought is introduced in the iterative learning control, and an improved iterative learning algorithm is put forward. Combining the fuzzy model identification technique, forecasting control and iterative learning control, an iterative learning controller based on the fuzzy forecasting is designed. Taking the temperature during the Omethod composition as the controlled object, the control quality of this controller is simulated.
According to the amic sum input, the Omethod composition procedure can be divided into four phases: initial reaction phase, temperature increasing phase, stable reaction phase and end phase. According to the different features in different phases, the intelligent control algorithms discussed in former chapters are used to construct the integrated intelligent control system. A total integrated intelligent control system for Omethod composition is developed actually and it's main function includes control system signal processing, control system fault diagnosis, system software design, etc. Two integrated intelligent control systems of Omethod composition have been used in the two pesticide branch plants of Zhengzhou Sha-Long-Da pesticide limited firm. Working effect shows that this system has high technique, eccellent performance and stable working since 2003. It has obtained obvious social and economic benefits.
本文阐述了氧乐果合成的工艺流程,对氧乐果合成的特性和对象模型进行了详细分析,说明氧乐果合成具有非线性、时变、大滞后、扰动复杂的特点,并从系统可测参数和不可测参数两个方面分析了氧乐果合成中的影响因素。研究了基于神经网络的氧乐果合成过程的系统辨识,并针对氧乐果合成过程中影响最大的温度因素利用BP网络进行辨识。针对静态BP网络对系统动态模型辨识效果不佳的缺陷,采用分阶段BP网络和由静态BP网络加TDL环节所构成的回归网络对氧乐果合成中的温度对象进行了动态辨识,并对三种方法的辨识性能进行了比较分析。
根据氧乐果合成过程不同阶段的复杂特性,分别研究了相应的智能控制方法,作为整体设计氧乐果集成智能控制方案的基础。第一,针对氧乐果合成具有非线性、大滞后的特点,设计了一种参数反馈模糊控制器,并对其性能进行了仿真验证。
第二,针对氧乐果合成具有非线性、时变的特点,提出了基于递归遗传算法的混合学习算法,来对用于辨识的神经网络的结构和连接权值进行自动优化设计。针对模糊神经网络应用中存在的问题,提出了基于模糊补偿算子的神经网络控制器结构和算法。通过采用基于递归遗传算法的神经网络辨识器对氧乐果合成的温度对象进行辨识,与递归补偿模糊神经网络控制器组成温度智能控制系统,并对该控制系统的性能进行了仿真研究。
第三,针对氧乐果合成具有典型间歇过程的特点,在分析了迭代学习控制基本原理的基础上,通过在迭代学习控制中引入预测的思想,提出了一种改进的迭代学习算法。并将模糊模型辨识技术、预测控制和迭代学习控制三者相结合,设计了基于模糊预测的迭代学习控制器。以氧乐果合成过程中的温度为被控对象,仿真研究了所设计的控制器的性能。
根据一甲胺累积投料量,将氧乐果合成过程划分为四个阶段:反应初始阶段,温度上升阶段,稳定反应阶段和反应结束阶段,并根据不同阶段的反应特点,分别采用上述所研究的多种智能控制算法,组成氧乐果合成过程的集成智能控制方案,并开发了成套的氧乐果合成过程集成智能控制系统,包括控制系统信号处理、控制系统故障诊断、系统软件设计等。研制的两套系统分别在沙隆达郑州农药有限公司的两个农药分厂投入使用,2003年投入使用以来的运行效果表明:该系统技术先进、性能优良、工作可靠,取得了显著的社会经济效益。
As a great population country, agriculture is the base of national economy of China and has great relation with our country's stability and development. Pesticide is one of the main influencing elements on agriculture stability and high production. Production level and quantity of pesticide have great influence on our agriculture, then on the total national economy. To improve the technique level and production quality of pesticide production, it is very necessary to rebuild the traditional pesticide production precess. Omethod composition process as the research object, this paper researches the intelligent control methods during the Omethod composition process, and develops an Omethod composition procedure integrated intelligent control system that has been used in practice.
The technique flow of Omethod composition is introduced, and the reaction features and the subject model of Omethod composition are analyzed in detail. It is shown that Omethod composition reaction has the following characteristics: multi-variable, non-linear, time-changing, and complex disturbance. The influencing elements during Omethod composition procedure are analyzed from system measureable and non-measureable aspects. Furthermore, Omethod composition procedure system identification based on neural network is introduced. To the greatest influence element, temperature, during Omethod composition, a BP network is adopted to identify the temperature in the composition reaction. In order to overcome the shortcoming of static BP network in identifying the dynamic system parameters, a segmental BP network and recursive network composed of static BP and TDL are used to dynamically identify the temperature parameter. The identification results of BP Network, segmental BP network and recursive network are compared and analyzed.
According to the complex features during the Omethod composition, the corresponding intelligent control approaches are studied as the total design base of the Omethod composition reaction integrated intelligent control system. Due to the features of nonlinear, big lag, for the temperature control in Omethod composition, a parameter feedback fuzzy controller is designed and the controller performance is simulated.
Due to the features of nonlinear, time changing, recursive genetic algorithm is advanced which can design the network structure and weights automatically. For the existing problems in fuzzy neural network application, the neural network controller structure and algorithm based on fuzzy compensation operator are proposed. Neural network based on recursive genetic algorithm is adopted to identify the temperature during Omethod composition procedure. Combining the neural network identifier with the recursive fuzzy neural network construct a temperature intelligent control system. The control quality of the intelligent system is tested through simulation.
Due to Omethod composition being an intermission process, based on the principle of iterative learning control, the forecasting thought is introduced in the iterative learning control, and an improved iterative learning algorithm is put forward. Combining the fuzzy model identification technique, forecasting control and iterative learning control, an iterative learning controller based on the fuzzy forecasting is designed. Taking the temperature during the Omethod composition as the controlled object, the control quality of this controller is simulated.
According to the amic sum input, the Omethod composition procedure can be divided into four phases: initial reaction phase, temperature increasing phase, stable reaction phase and end phase. According to the different features in different phases, the intelligent control algorithms discussed in former chapters are used to construct the integrated intelligent control system. A total integrated intelligent control system for Omethod composition is developed actually and it's main function includes control system signal processing, control system fault diagnosis, system software design, etc. Two integrated intelligent control systems of Omethod composition have been used in the two pesticide branch plants of Zhengzhou Sha-Long-Da pesticide limited firm. Working effect shows that this system has high technique, eccellent performance and stable working since 2003. It has obtained obvious social and economic benefits.
引文
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