复杂系统的智能建模与控制方法

【作者】 康爱红；

【导师】 李战明；

【作者基本信息】 兰州理工大学 ， 控制理论与控制工程， 2004， 硕士

【摘要】 本文以白银铝厂阳极焙烧炉的温度控制系统为研究对象，针对复杂系统难建模、难控制的特点，采用智能控制方法对系统进行了深入的研究。模糊神经网络(FNN)是人工神经网络与模糊逻辑系统的有机结合，是一种能够处理抽象信息的网络结构，具有强大的自学习和自整定功能，可以不依赖模型而映射出对象的输入输出关系。FNN对于任意非线性对象的逼近和建模以及对不确定模型的控制均有很好的效果，有很强的容错性和鲁棒性。因此文中对T-S型RBF神经网络的控制算法进行了详细的理论分析和仿真研究，并与先进的控制方法--预测控制相结合，对复杂系统进行预测建模控制。在对T-S模糊模型与RBF神经网络进行深入研究的基础上，提出一种基于T-S模糊模型的RBF神经网络的动态学习算法，改进了RBF网络的学习方法。此方法可以动态地调节网络的隐节点个数、高斯径向基函数的数据中心及扩展常数，不仅能有效地对T-S模型的参数进行训练，而且改进了FNN学习性能，加快了网络的收敛速度，提高了算法的泛化能力和全局收敛能力。鉴于T-S型模糊RBF神经网络对非线性系统具有很好逼近能力，本文将T-SRBF模糊神经网络控制与预测控制相结合，提出一种能揉和模糊控制的逻辑推理能力与神经网络的强学习能力的模糊神经网络预测控制模型。仿真结果表明，该控制算法有较好的预测能力、自适应能力和较高的控制精度。在理论研究和仿真的基础上，对现场采集到的阳极焙烧炉的温度控制系统的大量的输入输出数据进行处理，建立了焙烧炉燃烧系统的预测模型，寻找出燃烧器的控制输出(控制喷油的脉冲数)与火道温度分布之间的关系，从而保证炉内温度的分布及升温速率满足工艺要求，从理论上为生产高质量炭阳极提供依据。更多还原

【Abstract】 Intelligent control tactics are adopted to lucubrate with temperature control system of anode baking furnace of Baiyin aluminum plant for the traits of difficult modeling and control of complex system in this paper. Fuzzy neural network (FNN) is composed of neural network and fuzzy logic system, and it is the organic integration of the two parts. FNN is the network structure, which can deal with the abstract information and has strong self-learning function and self-tuning function. It can map the relationship of input and output of the object independing on the model. In addition, it has also good fault freedom and robustness. Making use of FNN, we may get very good effect of the approximation and modeling of the random nonlinear plants and good effect of the control of the uncertain models. So, in this paper we combine the T-S model RBF neural network with the advanced control method-predictive control to predict, model and control the complex systems through theoretically analyzing the control algorithm of T-S model RBF neural network in detail and doing a mass of simulation research.A dynamic learning method of T-S fuzzy based RBF neural network is proposed on the basis of studying T-S fuzzy model and RBF neural network, by which the learning method of RBF NN is improved. The number of hidden layer nodes of T-S fuzzy RBF net is not only modified dynamically, but also the data centers and extended constants of gaussian radial basis function are changed adaptively during learning progress, moreover the algorithm can train effectively the parameters of T-S model. In addition, the algorithm also improve FNN learning performance, quicken convergence speed of the net, and generalization ability and convergence ability of the algorithm are enhanced.Due to better approximation ability of T-S fuzzy RBF neural network for nonlinear systems, T-SFRBFNN control is combined with predictive control to develop a FNN predictive control model syncretized logical inference ability of fuzzy control with strong learning ability of NN. Simulation results show that the control method has better predictive ability, adaptive ability and higher control precision.On the basis of the theory research and simulation, through processing a mass of collected input data and output data of temperature control system of anode baking furnace, we build the predictive model of baking furnace burning system and find out the relationship of control output of the firebox and temperature distributing in the fire way, which makes temperature distributing inside the furnace and calefactive velocity satisfy the technical need and offer theory foundation for producing anode of good quality.更多还原