This paper presents a solution to the control problem of inverted pendulum, in presence of model uncertainties due to its varying pendulum mass and length. Here, a fuzzy PD controller is proposed which is continuously tuned and updated by a non-fuzzy adaptive scheme. In addition to the proposed tuning scheme, a fuzzybased set-point weighing scheme is also incorporated for effective control in load variations. The application of the proposed fuzzy-based set-point weighting self-adaptive fuzzy PD controller (FSW-SAFPDC) stabilized the pendulum in an upright position in minimum time and the cart returned to its initial position.

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Complex, imprecise, ill-defined and uncertain systems can be identified by fuzzy modeling. The identification of such a linguistic model consists of two parts; structure identification and parameter estimation. In this paper, we proposed Self-Organizing Map (SOM) clustering technique for structure identification. Initial parameters of the input-output membership functions (MFs) will be estimated from the clustering results. In the process of proto-type generation, the gradient descent method is used to refine and tune the parameters effectively. The proposed scheme has been successfully tested to identify the rules required to realize the gain factor of self-tuning fuzzy PI controller, which is highly non-linear in nature. Here, the system identification method is integrated with rule extraction method in such a way that it can pick up required rules from any processes. Finally, examples of various systems are given to illustrate the effectiveness of the proposed approach.

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