Abstract
To solve the problems of conventional fuzzy inference, SIRMs (single input rule modules) dynamically connected fuzzy inference model is proposed in this chapter. For each input item, an SIRM is constructed and a dynamic importance degree is defined. The dynamic importance degree consists of a base value insuring the role of the input item throughout a control process, and a dynamic value changing with control situations to adjust the dynamic importance degree. Each dynamic value can easily be tuned based on the local information of current state. The model output is obtained by summarizing the products of the dynamic importance degree and the fuzzy inference result of each SIRM. The SIRMs process the input items dispersedly and the dynamic importance degrees express the control priority orders definitely. The present model is applied to several underactuated systems such as truck-trailer system, ball-beam system, and parallel-type double inverted pendulum system. The controller design approaches are given in detail. The simulation results indicate that the proposed model is effective even for very complex systems.
| Original language | English |
|---|---|
| Title of host publication | Advances In Computational Intelligence |
| Subtitle of host publication | Theory And Applications |
| Publisher | World Scientific Publishing Co. |
| Pages | 413-450 |
| Number of pages | 38 |
| ISBN (Electronic) | 9789812773920 |
| DOIs | |
| Publication status | Published - 1 Jan 2006 |
| Externally published | Yes |