【作者】 程菲；

【导师】 罗键；

【作者基本信息】 厦门大学 ， 控制理论与控制工程， 2007， 博士

【摘要】 针对带钢冷连轧过程质量控制特点,论文提出了一种基于模糊逻辑推理和神经网络的动态智能质量控制器DIQC。论文的最初动机是希望寻找一种有效的控制方法,来解决复杂的、非线性的、系统间存在强交互作用的、时变的带钢冷连轧系统控制问题。这类过程往往受干扰与噪声的影响,具有很强的不确定性。对于带钢冷连轧系统,需要增加控制器的智能性,以提高控制器从过程中抽象出函数关系的能力,并且通过调整这些关系以提高系统的控制精度,也即提高控制器的学习与推理能力。论文的目的就是针对上述问题,综合运用模糊逻辑推理和神经网络方法,构建一种具有自组织自学习功能的控制器。论文利用过程输入----输出的测量数据和可调结构与参数的参考模型来实现上述在线智能控制器——DIQC。论文讨论了智能控制器DIQC构建过程中涉及到的多个具体问题,如采用在最大输出误差点添加新隶属函数的构造性动态结构的控制器以减轻偏差/方差两难问题、控制器的全局逼近性质、参数的局部性与线性化要求等。同时,论文也研究了其他一些重要问题,如为达到全局闭环稳定而需要的全局控制方案、激励持续条件、学习率的界定等。同时,对于泛化能力的可靠性、数据分布的优化策略、在线学习条件、控制器反馈结构等方面,论文也进行了讨论。论文也对模糊逻辑推理的一些有关问题,如去模糊化方法的选定、T-norm算子与隶属函数的选择、ε-完备性要求以及模糊相似程度判定方面进行了研究。论文将所提的动态智能质量控制方法应用于带钢冷连轧工业控制过程中。通过仿真实验证明该方法的实用性与优越性。主要包括带钢冷连轧过程的扭振控制、偏心与来料硬度干扰控制、厚度控制。通过与传统控制方法PID及另一常用动态构造神经网络控制器CCNC对照,证明了论文所提方法即使在存在干扰与噪声的动态过程中,也能获得较好的逼近效果。更多还原

【Abstract】 In this thesis, a unified and comprehensive treatment of the neural network and fuzzy logical inference as the dynamic intelligent quality controller (DIQC) is provided to cope with the process of cold rolling mill. This work has been motivated by a need to develop the solid control methodologies capable of coping with the complexity, the nonlinearity, the interactions, and the time variance of the processes under control. In addition, the dynamic behavior of such processes is strongly influenced by the disturbances and the noise, and such processes are characterized by a large degree of uncertainty.Therefore, it is important to integrate an intelligent component to increase the control system ability to extract the functional relationships from the process and to change such relationships to improve the control precision, that is, to display the learning and the reasoning abilities. The objective of this thesis was to develop a self-organizing learning controller for above processes by using a combination of the fuzzy logic and the neural networks. An on-line, dynamic intelligent quality controller using the process input-output measurement data and the reference model with both structural and parameter tuning has been developed to fulfill the above objective.A number of practical issues were considered. This includes the dynamic construction of the controller in order to alleviate the bias/variance dilemma, the universal approximation property, and the requirements of the locality and the linearity in the parameters. Several important issues in the intelligent control were also considered such as the overall control scheme, the requirement of the persistency of excitation and the bounded learning rates of the controller for the overall closed loop stability. Other important issues considered in this thesis include the dependence of the generalization ability and the optimization methods on the data distribution, and the requirements for the on-line learning and the feedback structure of the controller. Fuzzy inference specific issues such as the influence of the choice of the defuzzification method, T-norm operator and the membership function on the overall performance of the controller were also discussed. In addition, theε-completeness requirement and the use of the fuzzy similarity measure were also investigated.Main emphasis of the thesis has been on the applications to the real-world problems such as the cold rolling mill industrial process control. The applicability of the proposed method has been demonstrated through the empirical studies on several real-world control problems of industrial complexity. This includes the torsional vibration, the eccentricity, the hardness and the thickness control in the cold rolling mills. Compared to the traditional linear controllers and the dynamically constructed neural network, the proposed DIQC shows the highest promise as an effective approach to such nonlinear multi-variable control problems with the strong influence of the disturbances and the noise on the dynamic process behavior.更多还原