New methodology for the development of adaptive and self-learning fuzzy controllers in real time

This work proposes a procedure to design adaptive and self-learning fuzzy controllers in real time, requiring only a limited prior knowledge of the plant to be controlled, both in terms of the quantity and precision of this information. The algorithm does not need a mathematical model of the plant, or its approximation by means of a Jacobian matrix. Neither is it necessary to know the response desired at each instant of time, nor need there be previously available data. Auxiliary fuzzy controllers accomplish simultaneously the adaptation of the output scale factor (which is essential in the first steps of the control process) and learning of the parameters within the principal fuzzy controller (fuzzy rules). To verify the validity of the algorithm, real control problems were used: the stabilization of the temperature of a thermostat and level control within a liquid-filled tank. An analysis of the stability and robustness of the proposed algorithm is performed for different initial configurations of the fuzzy systems required by the algorithm and for abrupt changes in the plant to be controlled. (C) 1999 Elsevier Science Inc. All rights reserved.