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

A robust self-learning fuzzy controller for a class of nonlinear MIMO systems is proposed. It is well known that the self-organizing fuzzy controller proposed by Procyk is sensitive to external signals such as set-point changes and/or disturbances. Such a phenomenon is observed in the fuzzy learning controllers that use a linear combination of error states for its adaptation law. To overcome such a difficulty a new learning scheme is introduced. The proposed learning scheme is implemented by constructing the performance decision table based on the principle of sliding mode control. Experimental results show that the proposed controller is robust to external signals.