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Research on T-S Fuzzy Modeling of Complex Thermal System

Author: WangZhiQin
Tutor: WangShuangXin
School: Beijing Jiaotong University
Course: Detection Technology and Automation
Keywords: Fuzzy Identification T-S Model Chaos Genetic Algorithm Chaos immigrants Online Identification
CLC: TK323
Type: Master's thesis
Year: 2009
Downloads: 198
Quote: 4
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Abstract


Thermal process control, the controlled object dynamic characteristics tend to exhibit the characteristics of nonlinear, time-varying, large delay and large inertia, which makes it difficult its more accurate models, making it difficult to accurately represent the thermal process and the implementation of the overall optimal control. Takagi and Sugeno in 1985 the famous TS fuzzy model with universal approximation to arbitrary precision approximation of nonlinear dynamic system, has become a widely used fuzzy model. In order to achieve the purpose of accurate thermal model, the paper studies the fuzzy modeling method based on TS model offline and online. 1.TS fuzzy model off-line identification method This article is based on the traditional fuzzy clustering algorithm in optimization clustering center iterative process large amount of calculation, and prone to dead center, local minima and center redundancy, put forward a kinds of parameter optimization method based on chaos genetic algorithm. First, based on the order of the loss function identification model based on the input selection criteria to determine the input variables, and the introduction of a generalized TS model, its membership function with self-adaptive generalized Gaussian function, using the chaos genetic algorithm to optimize its shape in based on the use of parameter identification and consequent parameters. The chaos genetic algorithm introduced in the basic genetic algorithm chaotic immigration operator, replacing the original population of poor individuals to participate in the group's mating multiply, in order to ensure the diversity of the population, to prevent genetic diseases due to inbreeding caused by the recession, prematurity and slow convergence to overcome the basic genetic algorithm, the parameter identification of the front piece achieved very good results. 2.TS fuzzy model line identification method of taking into account the actual thermal processes in the system conditions and changes in the external environment and other reasons uncertainty parameters and structure are prone to migration, off-line identification model is difficult to reflect the adaptive system non-linear changes. Online fuzzy identification technology, has a strong theoretical and practical value, and has a wide range of applications in adaptive control, predictive control. Based on the amendment to the definition of the TS model and the fuzzy rules influential new standards for online updates of fuzzy rules and optimization problems. The closeness degree between the sample and the cluster center vector to correct the cluster center, and the distance to the center vector based on a sample of input data space divided. On this basis, the use of recursive least squares algorithm to identify the conclusions of the model parameters. The identification algorithm has the required number of fuzzy rules, high recognition accuracy, the algorithm is simple and easy to implement, etc.. The identification method applied to the Box-Jenkins gas furnace data and the actual boiler superheated steam temperature system identification to verify the effectiveness of the method, showed a good approximation ability, and achieved good results.

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CLC: > Industrial Technology > Energy and Power Engineering > Thermal measurement and thermal automatic control > Thermal Automatic Control > Automatic control system
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