Optimal gain-scheduled flight control system design using a new fuzzy clustering algorithm

Abstract

This article presents an analytical framework for the design of autopilots using fuzzy systems. A new fuzzy clustering method is presented in this article, which is used to model a non-linear flight vehicle using a set of linear models. It is shown that the proposed fuzzy clustering technique produces lower estimation errors compared with other fuzzy clustering techniques; it means that the modelling error using the technique introduced is lower than other clustering methods. The membership functions and rule sets, which are obtained by fuzzy clustering, are then applied to a set of linear time-invariant optimal state-feedback controllers, obtained for each rule, towards extraction of the global non-linear controller matching closely with the dynamic properties and changes in the plant. The stability of the fuzzy model and the fuzzy system is established by the Lyapunov-based linear matrix inequality analysis. Simulation studies are reported to demonstrate the merits of the fuzzy set-based modelling and control approach in handling the demanding non-linear modelling and control task.

Keywords

flight control autopilot design nonlinear systems fuzzy modelling fuzzy control

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