Research On Identification Algorithms For Nonlinear System Using Intelligence Computation

Almost all production systems are nonlinear systems. The so-called linear system is obtained approximately under some assumptions or by ignoring to some nonlinear characteristics of the system, while this kind of approximation will inevitably produce errors, which will influence the control effect of production systems. As the structure of production system becomes more and more complex, nonlinear characteristics contained in those systems are also more diverse, as a result, simple linear approximation can no longer meet the requirement of improving system productivity, and therefore, nonlinear system identification has become the general trend. So far, there is no universal method to identify nonlinear systems with different structures, usually, different methods to differernt systems with different nonlinear characteristics.The modular nonlinear model has not simple structure only, but also clear internal connections, so it is popular and widely used by researchers in recent years. Modular models mainly include Hammerstein model (H model), Wiener model (W model), and the succeeding Hammerstein-Wiener model (H-W model) and Wiener-Hammerstein model (W-H model).Thermal system is large-scale production system with complex structure and high demand for control performance, and there are different degrees of nonlinearity in the production process. In this thesis, analyzes nonlinear characteristics of actuators and detection transmitters in thermal control systems, starting from the composition of the automatic control system, and then it is learned that modular nonlinear models are suitable for the identification of typical processes of thermal systems.Three modular nonlinear models are mainly studied in this thesis:Hammerstein model, Wiener model, and Hammerstein-Wiener model. Model parameters are optimized using particle swarm optimization algorithm and its improved algorithm. With neural network theory, new modular models are constructed and the learning rules of the models itself are deduced. Identification methods of modular models are applied to the identification of thermal systems, with the input and output data of thermal processes stored in distributed control systems. The main contents include:1. Spline function polynomial Hammerstein model based on cluster analysis is introduced and particle swarm optimization algorithm is used to optimize model parameters. This identification algorithm is applied to identify a typical link of thermal system, and the simulation results show the effectiveness of this spline function Hammerstein model, which provided an effective way for identification of production systems.2. Two networked Wiener models are introduced, the nonlinear parts of which are represented by BP network and RBF network respectively, and then the models can be converted into series structures. Both of these two kinds of models adopt double-optimization strategy, which optimizes networked models in inner and outer layers with BP algorithm and particle swarm optimization algorithm respectively. These two methods are applied to identification of two objects of thermal systems. The identification results of CO2concentration system show that networked W model outperforms spline function H model, and the identification results of main-steam pressure system show that networked W model has better applicability.3. Identify general index polynomial Hammerstein model with quantum particle swarm optimization (QPSO) algorithm, by adopting quantum computing theory, and apply this algorithm to the identification of thermal systems. Generally index polynomial H model has simple structure and faster calculation speed. Precocity is avoided to some extent, for the diversity of population is increased in QPSO algorithm, compared with general particle swarm optimization (PSO) algorithm. From the identification results of three objects of circulating fluidized bed unit, it can be seen that simple polynomial H model can be used for part of the actual system identification and quantum particle swarm algorithm can improve the identification accuracy of model to some extent.4. One kind of networked Hammerstein-Wiener model is introduced, and two identification methods of general polynomial H-W model and the networked model proposed in this paper are studied, adopting QPSO algorithm to identify the parameters of the models. The two methods are applied to the identification of two typical production links in thermal systems, and simulation results show that H-W models are more capable to express characteristics of production systems.This thesis studies mainly the identification algorithms of nonlinear models and its application in thermal systems based on intelligent computing. It's wished that the research work in this thesis has some theoretical and practical value for the identification of thermal systems and some enlightening action for the identification of other production systems.