Fuzzy modelling and control using parameterised linear filters

The paper presents a nonlinear identification scheme, which consists of a linear dynamical section (filter) and a nonlinear zero-memory section (implemented by a fuzzy mapping). Only the filter section is on the primary signal path. The nonlinear mapping (depending on the system input and state) delivers the filter parameters. The identification assumes structural knowledge about the process with proper parameterisation. An adaption procedure is introduced, which tunes the nonlinear mapping (e.g. membership function parameters) to minimize identification error. The adaption procedure is driven by the approximate dynamical sensitivity model of the system thus the method is very effective with respect to the number of training steps necessary to reach the accuracy required. The scheme proposed can incorporate a priori knowledge on two levels (structure and fuzzy rule set). One of the most distinctive features of the scheme is that it directly supports controller design and/or (on-line) tuning