| Traditional control is constituted based on the accurate mathematics model. But the real system has its properties such as complexity, non-linear, real time, indeterminate and so on. It is hard to obtain the accurate mathematics model. As a new technology of intelligent control, fuzzy control is widely used for its virtues such as easy realization, robust. The routine method of fuzzy controller design is to sum experts' experience up to fuzzy control rules. However, the gained fuzzy rules can not reflect the essence characters of the whole control system when the experts' experience is incomplete, and especially the individual experience is incorrect. In this circumstance, it is an important task of application in design of fuzzy controller to improve the capability of fuzzy controller by optimizing the fuzzy rules.Ant colony algorithm is a novel optimization algorithm welled up in recent years, and has been widely used to solve the complex combined optimization problem. the paper makes a full research to it, and offer an improved algorithm instead of the basic ants algorithm's disadvantage such as convergence slowly and stagnancy easily, etc. The simulation of the research shows that this improved algorithm has a faster convergence speed and larger search space, and it also has a better result of parameter improvement. To confirm fuzzy rules is a issue of combined optimization problem. In this paper, ant colony algorithm is applied into the optimization design of fuzzy controller. A lot of simulation results indicate the feasibility and validity of the algorithm, and the optimized fuzzy controller has more dynamic, steadier and more robust capability. |
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