| The traditional fuzzy logic control system, based on the knowledge or rules, is one of the important methods to deal with the uncertainty and solve the nonlinear control problems. However, with the higher requirement of the control performance and more uncertain problems to be solved, the traditional fuzzy logic control has been unable to meet the requirements. The type-2 fuzzy theory as the extension of the traditional fuzzy theory can deal with more uncertain information. So the type-2 fuzzy theory came into the vision of control scholars and has been one of the hot spots in current research.Type-2 fuzzy system is based on the knowledge or rules similar to the traditional fuzzy system. Type-2 fuzzy system is built on type-2 fuzzy sets. While the type-2 fuzzy, the shape of whose membership functions have three-dimensional character, can describe the uncertainties in reality more appropriately. Compared to the traditional type-1 fuzzy system, type-2 fuzzy system has a stronger ability to deal with the uncertainty of system. Meanwhile the present of interval type-2 fuzzy sets greatly simplifies the computation between the general fuzzy sets. Now most of the fuzzy logic system based on type-2 fuzzy sets theory focus on the interval type-2 fuzzy system. This thesis mainly studies the design and the analytical structure of the interval type-2 TSK fuzzy control system. The main works are summarized as follows:1. Design the interval type-2 fuzzy logic control system. The interval type-2 fuzzy sets of input variables are constructed based on the nonlinear function in this fuzzy controller. Further more, this controller belongs to TSK type fuzzy controller. Compare the control performance between the different fuzzy control systems through the numerical simulation of the coupled tank.2. Analyze the structure of the interval type-2 fuzzy controller and provide the optimization method of the design parameters.The analytical structure is one of the important methods to research the fuzzy controller. The expression of the controller output is converted to the form of classical PI/PD controller by partitioning the whole input space so that we can find out the equivalence relation between the IT2 TSK fuzzy PI/PD controller and the classical PI/PD controller.3. The control performance of IT2 TSK fuzzy PI/PD controller is tested through the numerical simulation instance of flexible mechanism in noise environment. Finally, the feasibility of IT2 TSK fuzzy PI/PD controller is demonstrated in Quanser HIL experiment. |
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