Robust self-learning fuzzy controller design for a class of nonlinear MIMO systems

A new learning paradigm that can be applied for the design of a fuzzy logic-based learning controller that is robust to external signals such as disturbances and set-point changes is proposed in the paper. It is well known that the self-organizing fuzzy controller proposed by Procyk and Mamdani is sensitive to external signals. Such a phenomenon may be observed in other types of direct fuzzy logic-based learning controllers that utilize an adaptation scheme in which the locational information of the current error state vector determines the degree of adaptation that should be made in the learning controller. To resolve the problem of sensitivity to external signals, it is proposed that learning and modification of the controller be made in consideration of the motional trend of the error state vector as well as its locational information. This paradigm is adopted in the design of a new robust self-learning fuzzy controller for a dass of nonlinear MIMO systems. The well-known techniques of sliding mode control and the fuzzy decision making method are utilized to implement the proposed learning scheme in the fuzzy learning controller. Via simulation study and experimental results, the proposed learning controller is verified to be robust in the presence of external disturbances. (C) 2000 Elsevier Science B.V. All rights reserved.